AI Safety for Multi-Agent Network Incident Diagnosis, Coordinated Repair, and Outcome Verification

4.1. Complete Group B Common Workflow B-CW-01 (Original lines 22945-25481)

PSEUDOCODE, IMPLEMENTATION VARIATIONS, AND SPECIFIC ALTERNATIVE TECHNICAL PATHWAYS¶

10.1. Group Identification¶

Invention Group: B - Multiagent Network Incident Diagnosis and Repair.¶

Invention Group Number: 2 of 10.¶

Technical Title: Systems and Methods for Control-Specific Consistency of Network-¶

Incident Evidence, Multiagent Diagnostic Hypotheses, Repair Preconditions, and Post- Intervention Operational State.¶

Common Workflow: B-CW-01.¶

Common Algorithm: B-ALG-01.¶

Primary Technical Field: Computer network management, network fault diagnosis,¶

distributed systems, autonomous network operations, machine-assisted root-cause analysis, and consistency of state-changing remediation procedures.¶

Principal Technical Subject: Establishing and maintaining an incident-specific relationship¶

between observed network symptoms, the evidence relied upon by cooperating diagnostic agents, proposed explanations, selected remediation steps, the operational state to which those steps apply, and the resulting network behavior.¶

Planned Embodiments:¶

| x | 1.B - Incident Identity and Repeated-Alarm Correlation Under Delayed or Incomplete Evidence. | |---|---| | x | 2.B - Contradictory Multiagent Diagnoses and Evidence-Based Hypothesis Reconciliation. | | x | 3.B - Cascading Network Faults and Cross-Domain Causal Dependency Reconstruction. | | x | 4.B - Asynchronous Repair Execution, Pending Operations, and Competing Agent Interventions. | | x | 5.B - Partial Repair, Compensating Operations, and Recovery-State Reconstruction. | | x | 6.B - Post-Repair Verification, Recurrence Detection, and Diagnostic Continuity Across Agent Handoffs. |¶

The embodiments share the present common workflow but elaborate different technical mechanisms. Each embodiment will include its own implementation, specific alternative pathways, failure cases, and tests, without repeating this common procedure.¶

10.2. Technical Problem Space¶

10.2.1. Network Incidents Are Observed Through Incomplete and Distributed Information¶

A network incident may involve routing failure, packet loss, excessive delay, resource exhaustion, incorrect configuration, service degradation, link instability, authenticationservice failure, or another operational condition.¶

Information about the incident may originate from different network devices, management systems, telemetry streams, log repositories, alarm services, probes, configuration databases, and control applications.¶

These sources may report different aspects of the same underlying incident.¶

For example, a physical-link problem may produce a link alarm, routing adjacency loss, traffic rerouting, increased congestion, service-level degradation, and subsequent application errors.¶

The corresponding messages may reach diagnostic agents at different times.¶

A diagnostic system that treats every message as an independent fault may propose unnecessary or contradictory repairs.¶

A system that indiscriminately merges every temporally correlated alarm may instead combine unrelated incidents.¶

The technical problem is to establish the relationship between observations and the incident or incidents they may represent.¶

10.2.2. Multiple Agents May Produce Different Diagnoses¶

A network operator may employ multiple software agents or analytical components.¶

One agent may analyze routing topology.¶

Another may examine device health.¶

Another may inspect application telemetry.¶

Another may evaluate configuration changes.¶

Another may propose a remediation procedure.¶

These agents may operate concurrently, use different data sources, or possess different versions of the network state.¶

Agent A may conclude that packet loss results from a congested link.¶

Agent B may conclude that it results from a routing-loop condition.¶

Agent C may identify an earlier configuration change that could explain both symptoms.¶

The existence of several diagnostic conclusions does not establish that the conclusions can be combined.¶

The system must determine whether the hypotheses are compatible with the evidence, applicable topology, and current incident state.¶

10.2.3. Diagnostic Agreement Does Not Necessarily Establish Correctness¶

Several agents may agree because they relied upon the same underlying log, shared an incorrect assumption, or copied a previous agent's conclusion.¶

The number of agreeing agents is therefore not necessarily the number of independent evidentiary sources.¶

A system that counts repeated reports of the same underlying measurement as independent corroboration may exaggerate confidence.¶

The present workflow distinguishes independent observations from derived reports and agent-generated interpretations.¶

It does not presume statistical independence without supporting information.¶

10.2.4. Correlation Is Not Causation¶

Two alarm events may occur close together because they share an underlying cause.¶

They may also be independent, causally related in one direction, or consequences of a third event.¶

For example, an increase in packet loss may coincide with high interface utilization.¶

High utilization may contribute to loss, but the observations alone may not establish that relationship.¶

An underlying routing change may have increased utilization and altered traffic distribution.¶

The diagnostic process must distinguish an observed association from an established causal relationship under the applicable fault model.¶

Where necessary, the system may identify diagnostic probes or additional observations that discriminate among competing hypotheses.¶

10.2.5. The Network State Can Change During Diagnosis¶

Network topology and operational configuration may change while agents investigate an incident.¶

A link may recover.¶

A route may converge.¶

A controller may apply an unrelated configuration change.¶

A separate operator may change a capacity allocation.¶

An automated agent may perform a repair before another agent finishes its diagnosis.¶

A proposed remediation plan may therefore refer to a network state that no longer exists.¶

The problem extends beyond the freshness of individual measurements.¶

The relevant question is whether the diagnosis and proposed repair remain applicable to the current fault condition and affected resources.¶

10.2.6. Repair Preconditions Can Be More Specific Than Incident Identity¶

A remediation action may be appropriate only when certain technical conditions are satisfied.¶

For example, a routing configuration change may be based upon a particular route advertisement, adjacency state, next-hop relationship, and topology revision.¶

A service restart may be based upon a particular process instance or software deployment generation.¶

A link recovery procedure may depend upon an observed port state and neighboring topology.¶

A repair proposed for the correct incident may nevertheless become inapplicable if these conditions change.¶

The present workflow therefore associates each proposed repair with the technical conditions that justify its use.¶

10.2.7. Repair Submission Is Not Repair Completion¶

A remediation request may be generated, submitted, accepted, rejected, partially applied, applied with delay, or left in an unknown state following a communication failure.¶

A network-management interface may acknowledge a configuration request before the intended operational effect becomes observable.¶

Some operations may be transactional.¶

Others may be asynchronous.¶

Still others may involve several devices without a common atomic transaction.¶

The diagnostic system must distinguish these stages.¶

It must not conclude that an incident was repaired solely because an interface acknowledged a request.¶

10.2.8. Repair Success May Require Multiple Observations¶

A link-state alarm may clear even though application traffic remains impaired.¶

An interface counter may improve while end-to-end latency remains excessive.¶

A routing adjacency may recover while the affected service continues using an unsuitable path.¶

The verification requirements therefore depend upon the original incident and intended repair outcome.¶

A single favorable metric may be insufficient.¶

The system shall evaluate the post-repair observations required by the incident-specific verification procedure.¶

10.2.9. Repair Can Introduce New Faults¶

A remediation step may improve one metric while degrading another network service.¶

For example, moving traffic away from one congested route may overload another.¶

A service restart may temporarily restore connectivity while interrupting dependent sessions.¶

A configuration rollback may remove an unrelated later change if the implementation does not distinguish configuration dependencies.¶

The workflow must identify the relevant expected and unintended effects of the proposed remediation.¶

10.2.10. Failure to Verify Is Not Proof of Repair Failure¶

A post-repair verification attempt may fail because the verification source is unavailable, a probe times out, or the required observation window is incomplete.¶

Those conditions do not automatically prove that the repair failed.¶

The system must distinguish:¶

| x | repair outcome established; | |---|---| | x | repair outcome demonstrably unsuccessful; | | x | repair effect partially established; | | x | repair outcome unresolved. |¶

The classification shall depend upon the available technical evidence.¶

10.3. Distinct Technical Contribution Sought¶

The proposed contribution of Group B concerns incident-specific consistency across the¶

complete diagnostic and remediation information chain, not merely the use of multiple AI¶

agents or a generic network automation engine.¶

In a representative implementation, the system maintains relationships between:¶

1. A particular network incident or incident candidate. 2. The observed symptoms associated with that incident. 3. The source information underlying those observations. 4. The network topology and configuration context applicable to the observations. 5. The diagnostic hypotheses proposed by one or more agents. 6. The evidence supporting or contradicting each hypothesis. 7. The dependencies shared among different agents' evidence. 8. The tests or additional observations needed to distinguish competing explanations. 9. The network-state conditions required for a proposed repair. 10. The expected operational effects of that repair.¶

11. The current resource state against which the repair remains applicable. 12. Any other ongoing or completed interventions affecting the same resources. 13. The observed result of the repair. 14. The verification evidence required to establish incident resolution. 15. The handling of new or recurring symptoms after the repair.¶

The system may classify individual relationships as established, violated, or unresolved.¶

The system may preserve competing hypotheses rather than select one arbitrarily.¶

It may also reconstruct the relevant incident state when new observations, topology changes, repair results, or external interventions become available.¶

The technical relationship is independent of whether the diagnostic agents are conventional programs, rule engines, statistical models, language-model agents, specialized network analyzers, or combinations thereof.¶

A particular implementation may use human assistance or human approval without requiring that every diagnostic step involve a machine-learning model.¶

10.4. COMMON WORKFLOW B-CW-01¶

10.4.1. Workflow Overview¶

The common workflow comprises 36 stages organized into six processing phases:¶

Phase I: Incident identification and evidence acquisition.¶

Phase II: Evidence normalization, dependency interpretation, and topology reconstruction.¶

Phase III: Multiagent hypothesis generation and reconciliation.¶

Phase IV: Repair-plan correspondence and precondition evaluation.¶

Phase V: Intervention-state reconciliation.¶

Phase VI: Post-repair verification, recurrence evaluation, and incident-state preservation.¶

The workflow may be implemented sequentially, incrementally, asynchronously, or through distributed components.¶

Not every processing stage requires a physically distinct service.¶

Where a stage is omitted because the corresponding information or technical function is unnecessary for a particular calculation, the implementation must still satisfy the mandatory relationships of its selected operating procedure.¶

PHASE I - INCIDENT IDENTIFICATION AND EVIDENCE ACQUISITION¶

B-CW-01.01 - Receive an Incident Trigger¶

The processing system receives information indicating a possible network incident.¶

The trigger may comprise an alarm, performance threshold crossing, service-level violation, monitoring event, external trouble report, diagnostic probe result, or another technically supported signal.¶

The processor assigns or retrieves an incident-candidate reference.¶

A trigger is not automatically treated as conclusive evidence of one particular root cause.¶

Output: Incident candidate and initiating observation.¶

B-CW-01.02 - Establish the Relevant Operational Scope¶

The system identifies the network resources, services, interfaces, topology regions, or administrative domains potentially involved.¶

The scope may initially be incomplete.¶

It may be expanded or narrowed as additional observations become available.¶

The system does not presume that every downstream symptom belongs to the same physical failure.¶

Output: Initial incident scope and uncertainty.¶

B-CW-01.03 - Determine Required Evidence Classes¶

The diagnostic procedure identifies the evidence needed to evaluate the relevant fault classes.¶

These may include configuration state, interface counters, routing state, topology information, alarms, service measurements, recent changes, or operational probes.¶

The required evidence depends upon the incident symptoms and the hypotheses being evaluated.¶

The system may identify optional evidence separately from mandatory evidence.¶

Output: Evidence requirements.¶

B-CW-01.04 - Identify Available Evidence Sources¶

The system identifies sources capable of supplying the required information.¶

Each source is associated with its measurement definition, resource scope, reporting characteristics, and available acquisition history.¶

Where necessary, the system identifies source trust or authentication properties separately.¶

A successfully contacted source is not assumed to possess complete incident evidence.¶

Output: Source inventory.¶

B-CW-01.05 - Acquire Incident Observations¶

The processor obtains available observations from the relevant sources.¶

The observations may be delivered through event subscriptions, queries, log retrieval, active probes, or another supported method.¶

The processor records or references the original source context required for later interpretation.¶

Where acquisition is incomplete, the missing information is identified.¶

Output: Incident observation collection.¶

B-CW-01.06 - Distinguish Event Occurrence from Report Arrival¶

For observations whose temporal relationship is material, the processor distinguishes the time or event order of occurrence from the time a diagnostic agent received the report.¶

The system applies the source's actual timestamp, ordering, or replay semantics.¶

A delayed alarm shall not automatically be interpreted as a new fault occurring at the time of arrival.¶

Output: Applicable event-time or ordering information.¶

PHASE II - EVIDENCE INTERPRETATION AND NETWORK CONTEXT¶

B-CW-01.07 - Normalize Source Representation¶

The processor interprets each observation according to its source schema and reporting meaning.¶

It identifies the measured quantity, unit, resource context, aggregation behavior, and other mandatory properties.¶

A normalized representation shall not be interpreted as proving semantic equivalence where the source information is insufficient.¶

Output: Interpreted evidence records.¶

B-CW-01.08 - Resolve Network Resource Identity¶

The system identifies the resource instances associated with the observations.¶

A source-specific interface name, device identifier, service name, or topology reference may be mapped to the relevant operational resource.¶

The mapping is evaluated under the applicable network-state context.¶

Identifier reuse or resource reassignment may require additional information.¶

Output: Resource-associated evidence.¶

B-CW-01.09 - Reconstruct the Applicable Topology Context¶

The processor obtains the topology relationships necessary for the incident diagnosis.¶

These may include physical links, logical adjacencies, service dependencies, routing relationships, forwarding paths, controller relationships, or other relevant structures.¶

Where the topology changed during the incident, the processor identifies the applicable versions or transition conditions.¶

A current topology snapshot does not automatically establish the topology that existed when an earlier symptom occurred.¶

Output: Incident-relevant topology context.¶

B-CW-01.10 - Establish Configuration and Operational-State Context¶

The processor obtains configuration and observed operational-state information relevant to the incident.¶

It distinguishes intended, requested, accepted, applied, and observed conditions where the underlying interfaces support those distinctions.¶

The system associates the information with the applicable time or state version.¶

Output: Applicable state context.¶

B-CW-01.11 - Identify Evidence Dependencies and Duplicates¶

The processor determines whether several reports originate from the same underlying event or observation.¶

An agent summary copied from another agent's report may be recorded as a derived interpretation rather than an independent source.¶

The system may retain an evidence dependency graph or another equivalent representation.¶

This prevents unsupported multiplication of corroborating evidence.¶

Output: Evidence dependency relationships.¶

B-CW-01.12 - Classify Evidence Sufficiency¶

The processor evaluates whether the available observations satisfy the evidence requirements of the incident analysis.¶

It distinguishes:¶

| x | evidence established; | |---|---| | x | evidence demonstrably inconsistent with a required condition; and | | x | evidence insufficient to resolve the condition. |¶

The system does not replace unavailable evidence with an arbitrary favorable value.¶

Output: Evidence sufficiency classification.¶

PHASE III - MULTIAGENT DIAGNOSIS AND HYPOTHESIS RECONCILIATION¶

B-CW-01.13 - Construct Candidate Diagnostic Hypotheses¶

One or more diagnostic agents propose explanations for the incident.¶

Each hypothesis identifies the fault condition it proposes, the relevant resources, its assumed operating context, and the observations expected under that explanation.¶

A hypothesis may refer to one root cause or several interacting causes.¶

The system does not require every hypothesis to have identical granularity.¶

Output: Candidate hypothesis set.¶

B-CW-01.14 - Bind Each Hypothesis to Its Supporting Evidence¶

Each hypothesis is associated with the observations and interpreted information supporting it.¶

The record identifies evidence references rather than relying solely upon a generated naturallanguage explanation.¶

Where a conclusion depends upon inferred relationships, those assumptions are identified.¶

Output: Evidence-associated hypotheses.¶

B-CW-01.15 - Identify Contradictory and Missing Evidence¶

The processor examines observations that conflict with the predictions or conditions of a hypothesis.¶

It distinguishes a demonstrated contradiction from missing evidence.¶

Where the evidence is uncertain, the system preserves that uncertainty.¶

The absence of an expected alarm is not automatically contradictory unless the source's reporting and detection guarantees support that inference.¶

Output: Hypothesis contradiction and uncertainty records.¶

B-CW-01.16 - Determine Evidence Independence and Shared Assumptions¶

The system identifies dependencies among evidence sources and among agents' reasoning.¶

For example, three agents may all rely upon one topology database.¶

Such agreement need not count as three independent confirmations.¶

Where numerical confidence is used, the applicable dependency assumptions must be explicitly defined.¶

Output: Evidentiary dependency classification.¶

B-CW-01.17 - Evaluate Hypothesis Compatibility¶

The processor determines whether competing hypotheses may coexist or are mutually incompatible under the available evidence and network model.¶

Two faults may occur simultaneously.¶

Therefore, disagreement between agents does not necessarily imply that exactly one hypothesis is true.¶

The system may preserve multiple plausible explanations where they are not adequately distinguished.¶

Output: Hypothesis compatibility relationships.¶

B-CW-01.18 - Identify Discriminating Evidence Requirements¶

Where competing hypotheses remain unresolved, the system identifies additional observations or tests capable of distinguishing them.¶

The proposed test may include a read-only state query, log inspection, topology comparison, diagnostic packet probe, or another supported procedure.¶

The processor considers whether performing the test could materially change the network state.¶

A state-changing diagnostic probe is not automatically treated as passive observation.¶

Output: Diagnostic evidence acquisition plan.¶

B-CW-01.19 - Acquire and Reconcile Additional Diagnostic Evidence¶

The system obtains the additional observations where available and permitted.¶

It updates the affected hypotheses.¶

Evidence obtained after an earlier network-state change is interpreted under the corresponding context.¶

The processor does not automatically apply a later observation to an earlier fault state without establishing the required relationship.¶

Output: Updated hypothesis evaluations.¶

B-CW-01.20 - Determine the Diagnostic Outcome¶

The processor identifies the hypotheses sufficiently supported for the intended next processing stage.¶

The result may comprise:¶

| x | one established diagnosis under the specified diagnostic model; | |---|---| | x | several compatible diagnoses; | | x | a demonstrated rejection of certain hypotheses; | | x | a ranked set of hypotheses with stated uncertainty; or | | x | an unresolved diagnostic outcome. |¶

A ranking or confidence value is not equivalent to proof of causation or authorization to perform a repair.¶

Output: Incident-specific diagnostic result.¶

PHASE IV - REPAIR-PLAN CORRESPONDENCE¶

B-CW-01.21 - Construct Candidate Repair Plans¶

One or more repair-planning agents propose actions intended to address the identified fault condition.¶

Each candidate identifies:¶

| x | the fault or symptoms it is intended to address; | |---|---| | x | the target resources; | | x | the proposed technical operation; | | x | the network-state conditions required for applicability; | | x | the expected state transition; | | x | the effects that must be verified; and | | x | relevant dependencies or conflicts with other interventions. |¶

The plan may include one step or several related steps.¶

Output: Candidate repair plans.¶

B-CW-01.22 - Associate Plans with Diagnostic Justification¶

The processor identifies the diagnosis, evidence, and assumptions supporting each repair plan.¶

A repair proposed for one fault hypothesis is not automatically appropriate for another.¶

A plan may be conditional upon additional evidence.¶

The system retains this relationship so that a later change in diagnosis can trigger reevaluation of the plan.¶

Output: Diagnosis-to-repair correspondence.¶

B-CW-01.23 - Determine Repair Resource and State Preconditions¶

The processor evaluates the resource identities, configuration attributes, topology relationships, service dependencies, and other technical conditions required by the repair.¶

The preconditions are specific to the proposed operation.¶

A plan that remains associated with the correct incident may nevertheless be inapplicable to a changed resource state.¶

Output: Repair precondition evaluation.¶

B-CW-01.24 - Evaluate Plan Interactions and Competing Interventions¶

The system identifies other active, pending, or recently completed interventions affecting the same resources or dependent network services.¶

It evaluates whether the proposed repair conflicts with those interventions.¶

A plan may require ordering, replanning, cancellation, or an explicitly supported concurrent execution procedure.¶

The processor does not presume that independently valid repairs are jointly compatible.¶

Output: Intervention compatibility classification.¶

B-CW-01.25 - Evaluate Expected Effects and Verification Requirements¶

The processor identifies the operational changes expected if the repair succeeds.¶

It determines which measurements, resource states, or service observations are needed for verification.¶

It may also identify adverse outcomes relevant to the particular intervention.¶

The verification procedure is associated with the plan before the repair result is evaluated.¶

Output: Repair-effect and verification specification.¶

B-CW-01.26 - Produce the Repair-Applicability Decision¶

The processor determines whether the selected plan has established technical correspondence with the applicable diagnosis and network state.¶

The result may be:¶

| x | technically applicable; | |---|---| | x | technically inapplicable; | | x | unresolved pending information; | | x | conditionally applicable under a specified operating mode; or | | x | superseded by another plan. |¶

Technical applicability is not an authorization decision.¶

Where the deployment requires human approval, policy authorization, or protected execution finality, those controls remain separate.¶

Output: Repair-applicability result.¶

PHASE V - INTERVENTION AND STATE RECONCILIATION¶

B-CW-01.27 - Associate the Plan with an Intervention Reference¶

Where a plan is submitted for execution through a separately authorized networkmanagement procedure, the system associates the submission with an intervention reference.¶

The reference identifies the intended operation and the corresponding repair plan.¶

The receiving interface's actual idempotency and transaction guarantees are recorded where relevant.¶

Output: Intervention reference and submission context.¶

B-CW-01.28 - Observe the Intervention Lifecycle¶

The system obtains available information concerning the operation's processing status.¶

It distinguishes submission, acceptance, rejection, application, partial application, timeout, and unknown outcome where those states are relevant.¶

A submitted configuration change is not automatically considered applied.¶

Output: Observed intervention status.¶

B-CW-01.29 - Reconcile the Actual Network-State Transition¶

The processor compares the available post-intervention operational state with the expected state transition specified by the plan.¶

It obtains resource-side or other suitable evidence where required.¶

The comparison identifies whether the expected configuration or operational change occurred.¶

Where the evidence is insufficient, the corresponding transition status remains unresolved.¶

Output: State-transition correspondence.¶

B-CW-01.30 - Handle Partial, Competing, or Unknown Outcomes¶

Where only part of the repair took effect, the system identifies the completed and uncompleted portions.¶

It determines the relevant resource dependencies and whether any planned next step remains applicable.¶

Where another intervention changed the network state, the remaining plan is reevaluated.¶

Where the outcome of a prior operation is unknown, the processor uses an appropriate statusreconciliation or native idempotency procedure where available.¶

It does not assume that blind repetition is safe.¶

Output: Reconciled intervention context.¶

PHASE VI - POST-REPAIR VERIFICATION AND CONTINUITY¶

B-CW-01.31 - Acquire Post-Intervention Verification Observations¶

The system obtains the measurements and state information required by the repair's verification specification.¶

The observations are associated with their acquisition time, resource identity, measurement meaning, and applicable network state.¶

Observations from before the intervention are not automatically substituted for postintervention evidence.¶

Output: Verification observation set.¶

B-CW-01.32 - Evaluate Expected Recovery Conditions¶

The processor determines whether the observations establish the expected recovery properties.¶

For example, a repaired routing incident may require restoration of the intended forwarding behavior and relevant service measurements.¶

An alarm clearing may be one condition but need not be the only condition.¶

Output: Recovery-condition results.¶

B-CW-01.33 - Evaluate Material Adverse Effects¶

The processor evaluates specified adverse outcomes or dependent services that may have been affected by the intervention.¶

The verification is bounded by the technical requirements defined for the repair.¶

It does not claim to prove that no possible future adverse effect exists.¶

Output: Relevant adverse-effect evaluation.¶

B-CW-01.34 - Classify the Repair Outcome¶

The processor combines the required verification results.¶

It may classify the incident as:¶

| x | verified resolved under the specified checks; | |---|---| | x | unresolved because required verification evidence is missing; | | x | partially remediated; | | x | demonstrably not resolved; or | | x | requiring further diagnosis. |¶

A verification timeout is not automatically classified as proof of failure.¶

Output: Qualified repair outcome.¶

B-CW-01.35 - Detect Recurrence or Subsequent Related Incidents¶

The system processes later observations and determines whether they satisfy the defined conditions for recurrence of the same incident, continuation of the earlier fault, or a new incident.¶

The classification considers the applicable resource state, fault signature, observation history, and intervening changes.¶

An identical alarm name does not necessarily establish the same incident.¶

Output: Recurrence or new-incident classification.¶

B-CW-01.36 - Preserve the Incident-Evidence and Repair History¶

The system retains or references the information needed to interpret the diagnostic and repair process.¶

The record may include incident identity, evidence dependencies, hypotheses, repair assumptions, applicable network states, intervention status, verification observations, and later revisions.¶

The history may support subsequent agent handoff, audit, incident comparison, or technical investigation.¶

The repository need not constitute a protected execution-finality ledger.¶

Output: Reconstructible incident and remediation history.¶

10.5. Technical State Model¶

The workflow may employ the following logical states.¶

State¶

INCIDENT\_DETECTED¶

SCOPE\_UNRESOLVED¶

EVIDENCE\_COLLECTION¶

EVIDENCE\_INCOMPLETE¶

Meaning¶

A potential fault or service degradation has been observed The incident's affected resources or services are not adequately established Required observations are being acquired Mandatory evidence is unavailable or insufficient¶

HYPOTHESES\_AVAILABLE Candidate explanations have been produced¶

HYPOTHESIS\_CONFLICT¶

DIAGNOSIS\_QUALIFIED¶

DIAGNOSIS\_UNRESOLVED¶

REPAIR\_PROPOSED A plan has been constructed¶

REPAIR\_PRECONDITIONS\_READY¶

REPAIR\_PRECONDITIONS\_FAILED REPAIR\_PRECONDITIONS\_UNKNOWN One or more mandatory conditions remain¶

INTERVENTION\_PENDING¶

INTERVENTION\_PARTIAL INTERVENTION\_STATUS\_UNKNOWN Submission or application outcome cannot be¶

VERIFICATION\_PENDING¶

VERIFIED\_RESOLVED¶

VERIFIED\_UNRESOLVED¶

RECOVERY\_INCOMPLETE RECURRENCE\_UNDER\_EVALUATION Later symptoms may relate to the earlier¶

INCIDENT\_HISTORY\_RETAINED¶

Material diagnostic disagreement requires resolution A diagnosis or ranked hypothesis set has stated evidentiary support The available evidence does not establish the required explanation¶

Required technical applicability conditions have been established A mandatory technical condition has been violated unresolved A separately authorized operation is pending or awaiting an outcome Only part of the proposed intervention is established as applied established Required post-intervention evidence has not yet been obtained Required recovery criteria have been established Evidence establishes that required recovery conditions remain violated Verification evidence is insufficient for a conclusive outcome incident Relevant diagnosis and repair context is available for reconstruction¶

The states are logical descriptions.¶

A particular software implementation may combine stages or use multiple independent state dimensions.¶

A single Boolean repaired value is generally insufficient where partial application, unknown status, or incomplete verification matters.¶

10.6. Data Structures for B-ALG-01¶

10.6.1. Incident Record¶

IncidentRecord:¶

incident\_id candidate\_incident\_group incident\_generation affected\_resource\_references affected\_service\_references initial\_trigger\_references observation\_window topology\_context\_reference configuration\_context\_reference evidence\_requirements incident\_status prior\_incident\_relationships¶

The incident identifier is not necessarily a permanent representation of one physical root cause.¶

The system may later merge or split incident candidates where evidence supports doing so.¶

10.6.2. Evidence Record¶

IncidentEvidence:¶

evidence\_id source\_id source\_instance acquisition\_time\_or\_interval received\_time resource\_reference measurement\_definition observed\_value\_or\_event observation\_quality source\_state\_version topology\_context\_reference parent\_evidence\_references uncertainty\_information evidence\_status¶

An agent-generated summary may retain references to its original observations.¶

This permits the processor to distinguish derived reports from independent evidence.¶

10.6.3. Diagnostic Hypothesis¶

DiagnosticHypothesis:¶

hypothesis\_id incident\_id proposing\_agent fault\_type proposed\_causal\_resources assumed\_network\_state predicted\_observations supporting\_evidence\_ids contradicting\_evidence\_ids unresolved\_evidence\_requirements causal\_model\_reference confidence\_model\_reference hypothesis\_status¶

A confidence score is optional.¶

It must not be treated as a substitute for establishing a required technical predicate.¶

10.6.4. Agent Analysis Record¶

AgentAnalysis:¶

agent\_id analysis\_id incident\_id source\_hypothesis\_references examined\_evidence\_ids derived\_evidence\_references model\_or\_rule\_version assumptions conclusion unresolved\_questions¶

The record distinguishes the agent's conclusion from the source observations upon which it depends.¶

10.6.5. Repair Plan¶

RepairPlan:¶

repair\_plan\_id incident\_id target\_hypothesis\_ids target\_resource\_ids proposed\_operations operation\_dependencies required\_network\_preconditions¶

expected\_state\_transitions expected\_service\_effects verification\_requirements adverse\_effect\_checks relevant\_concurrent\_interventions plan\_version applicability\_status¶

The plan identifies the technical operation and the conditions under which it is considered applicable.¶

It is not an execution authorization credential.¶

10.6.6. Intervention State¶

InterventionState:¶

intervention\_id repair\_plan\_id operation\_reference receiving\_interface submitted\_operation submission\_status acceptance\_status application\_status resulting\_resource\_state\_reference evidence\_references unresolved\_conditions¶

The representation permits independent processing statuses.¶

It does not assume that submission, acceptance, and physical application are identical.¶

10.6.7. Repair Verification Record¶

RepairVerification:¶

verification\_id repair\_plan\_id intervention\_id required\_recovery\_conditions post\_intervention\_observation\_ids required\_observation\_coverage observed\_resource\_state adverse\_effect\_evaluations verification\_status unresolved\_requirements result\_version¶

10.6.8. Incident Dependency Graph¶

IncidentDependencyGraph:¶

incident\_nodes observation\_nodes source\_nodes hypothesis\_nodes resource\_nodes repair\_plan\_nodes intervention\_nodes verification\_nodes relationship\_edges applicability\_versions completeness\_status¶

A graph is illustrative, not mandatory.¶

Equivalent relationships may be implemented through relational tables, event histories, constraint structures, or source-specific records.¶

10.7. COMMON PSEUDOCODE - B-ALG-01¶

10.7.1. Three-Valued Evaluation¶

The algorithm distinguishes established technical correspondence from contradictory or missing information.¶

ENUM Verdict:¶

COMPATIBLE INCOMPATIBLE UNRESOLVED¶

FUNCTION COMBINE\_MANDATORY\_PREDICATES(results):¶

IF any result == INCOMPATIBLE:¶

RETURN INCOMPATIBLE¶

IF any result == UNRESOLVED:¶

RETURN UNRESOLVED¶

RETURN COMPATIBLE¶

A demonstrated violation of a mandatory requirement makes the corresponding evaluated relationship incompatible.¶

Where a required property is unknown and no mandatory violation has been established, the combined result remains unresolved.¶

10.7.2. Incident Candidate Construction¶

FUNCTION BUILD\_INCIDENT\_CANDIDATE(¶

trigger, topology\_service, evidence\_repository ):¶

source\_definition =¶

RESOLVE\_TRIGGER\_DEFINITION(trigger)¶

IF source\_definition unavailable:¶

RETURN UNRESOLVED(¶

"TRIGGER\_DEFINITION\_UNKNOWN" )¶

resource =¶

RESOLVE\_AFFECTED\_RESOURCE(¶

trigger, topology\_service )¶

context =¶

LOAD\_APPLICABLE\_NETWORK\_CONTEXT(¶

trigger.acquisition\_time, resource )¶

candidates =¶

FIND\_POSSIBLY\_RELATED\_INCIDENTS(¶

resource, trigger, context )¶

relationships = []¶

FOR EACH candidate IN candidates:¶

relation =¶

EVALUATE\_INCIDENT\_RELATIONSHIP(¶

trigger, candidate, context )¶

ADD relation TO relationships¶

IF exactly one existing incident is established¶

as matching the applicable criteria:¶

ASSOCIATE\_TRIGGER\_WITH\_INCIDENT(¶

trigger, existing\_incident )¶

RETURN existing\_incident¶

IF relationship remains ambiguous:¶

CREATE\_PROVISIONAL\_INCIDENT\_LINKS(¶

trigger, candidates )¶

RETURN UNRESOLVED(¶

"INCIDENT\_IDENTITY\_AMBIGUOUS" )¶

RETURN CREATE\_NEW\_INCIDENT\_CANDIDATE(¶

trigger, resource, context )¶

Incident merging is not based solely upon identical alarm names or arrival-time proximity.¶

10.7.3. Evidence Interpretation and Dependency Evaluation¶

FUNCTION EVALUATE\_INCIDENT\_EVIDENCE(¶

incident, evidence\_records, source\_registry, topology\_history ):¶

interpreted = []¶

FOR EACH record IN evidence\_records:¶

definition =¶

source\_registry.RESOLVE\_DEFINITION(¶

record )¶

IF definition unavailable:¶

MARK\_UNRESOLVED(¶

record, "SOURCE\_SEMANTICS\_UNKNOWN" )¶

CONTINUE¶

resource =¶

RESOLVE\_RESOURCE\_AT\_EVENT\_CONTEXT(¶

record, topology\_history )¶

timing =¶

EVALUATE\_EVENT\_TIME\_RELATIONSHIP(¶

record, incident )¶

dependencies =¶

RESOLVE\_EVIDENCE\_ANCESTORS(¶

record )¶

ADD {¶

record, definition, resource, timing, dependencies } TO interpreted¶

independent\_groups =¶

BUILD\_SOURCE\_DEPENDENCY\_GROUPS(¶

interpreted )¶

completeness =¶

CHECK\_REQUIRED\_EVIDENCE\_COVERAGE(¶

incident, interpreted )¶

RETURN {¶

interpreted, independent\_groups, completeness }¶

The algorithm preserves shared source dependencies.¶

It does not equate the number of agents with the number of independent observations.¶

10.7.4. Hypothesis Evaluation¶

FUNCTION EVALUATE\_HYPOTHESIS(¶

hypothesis, evidence\_context, network\_model ):¶

requirements =¶

LOAD\_HYPOTHESIS\_TEST\_REQUIREMENTS(¶

hypothesis )¶

evaluations = []¶

FOR EACH requirement IN requirements:¶

available =¶

SELECT\_RELEVANT\_EVIDENCE(¶

requirement, evidence\_context )¶

IF available is insufficient:¶

ADD UNRESOLVED TO evaluations CONTINUE relation =¶

TEST\_HYPOTHESIS\_PREDICTION(¶

hypothesis, requirement, available, network\_model )¶

ADD relation TO evaluations¶

IF any mandatory prediction is demonstrably¶

contradicted:¶

RETURN INCOMPATIBLE(¶

"HYPOTHESIS\_CONTRADICTED" )¶

IF mandatory discriminating evidence is missing:¶

RETURN UNRESOLVED(¶

"HYPOTHESIS\_NOT\_DISTINGUISHED" )¶

RETURN COMPATIBLE(¶

"HYPOTHESIS\_CONSISTENT\_WITH\_REQUIRED\_TESTS" )¶

A compatible hypothesis is not necessarily a uniquely established causal explanation.¶

Multiple hypotheses may remain compatible with the same observations.¶

10.7.5. Multiagent Hypothesis Reconciliation¶

FUNCTION RECONCILE\_AGENT\_HYPOTHESES(¶

incident, agent\_reports, evidence\_repository ):¶

hypotheses =¶

EXTRACT\_SUPPORTED\_HYPOTHESIS\_RECORDS(¶

agent\_reports )¶

source\_dependencies =¶

RESOLVE\_COMMON\_EVIDENCE\_ANCESTORS(¶

hypotheses, evidence\_repository )¶

evaluations = []¶

FOR EACH hypothesis IN hypotheses:¶

result = EVALUATE\_HYPOTHESIS(¶

hypothesis, evidence\_repository, incident.network\_model )¶

ADD {¶

hypothesis, result, source\_dependencies } TO evaluations compatible =¶

FILTER\_NONCONTRADICTED\_HYPOTHESES(¶

evaluations )¶

relationships =¶

EVALUATE\_COEXISTENCE\_AND\_CONFLICT(¶

compatible, incident )¶

IF one diagnosis is sufficiently established¶

under the specified diagnostic model:¶

RETURN QUALIFIED\_DIAGNOSIS(¶

diagnosis, supporting\_evidence, remaining\_uncertainty )¶

discriminating =¶

FIND\_TESTS\_DISTINGUISHING\_HYPOTHESES(¶

compatible, relationships )¶

RETURN UNRESOLVED(¶

"COMPETING\_DIAGNOSES", candidates = compatible, requested\_evidence = discriminating )¶

The system does not resolve contradictory diagnoses through a simple majority vote unless the application uses an explicitly justified model for that decision.¶

10.7.6. Repair Applicability Evaluation¶

FUNCTION EVALUATE\_REPAIR\_APPLICABILITY(¶

plan, diagnostic\_state, network\_state, intervention\_registry ):¶

diagnosis\_relation =¶

CHECK\_PLAN\_DIAGNOSIS\_CORRESPONDENCE(¶

plan, diagnostic\_state )¶

resource\_relation =¶

CHECK\_TARGET\_RESOURCE\_IDENTITY(¶

plan,¶

network\_state )¶

preconditions =¶

CHECK\_REPAIR\_STATE\_PRECONDITIONS(¶

plan, network\_state )¶

concurrent =¶

CHECK\_CONCURRENT\_INTERVENTION\_RELATIONSHIPS(¶

plan, intervention\_registry )¶

result = COMBINE\_MANDATORY\_PREDICATES([¶

diagnosis\_relation, resource\_relation, preconditions, concurrent ])¶

IF result != COMPATIBLE:¶

RETURN result verification =¶

BUILD\_PLAN\_SPECIFIC\_VERIFICATION\_REQUIREMENTS(¶

plan )¶

IF mandatory verification specification¶

cannot be established:¶

RETURN UNRESOLVED(¶

"REPAIR\_VERIFICATION\_UNDEFINED" )¶

RETURN {¶

status: COMPATIBLE, applicable\_network\_state: network\_state, verification\_requirements: verification }¶

This procedure establishes technical correspondence only.¶

Any separate permission, approval, protected capability, or Finality Sink verification remains external to the algorithm.¶

10.7.7. Outstanding Intervention Reconciliation¶

FUNCTION RECONCILE\_INTERVENTION(¶

intervention, receiving\_interface, resource\_observer ):¶

IF intervention.status is conclusively REJECTED under the receiving contract:¶

RETURN INCOMPATIBLE(¶

"INTERVENTION\_NOT\_APPLIED" )¶

receiver\_status =¶

receiving\_interface.QUERY\_STATUS(¶

intervention.operation\_reference )¶

resource\_status =¶

resource\_observer.GET\_RELEVANT\_STATE(¶

intervention.target\_resource )¶

comparison =¶

COMPARE\_EXPECTED\_AND\_OBSERVED\_TRANSITION(¶

intervention.expected\_transition, receiver\_status, resource\_status )¶

IF comparison proves required state transition:¶

RETURN COMPATIBLE(¶

"EXPECTED\_STATE\_TRANSITION\_ESTABLISHED" )¶

IF comparison proves a required transition¶

did not occur under conclusive coverage: RETURN INCOMPATIBLE(¶

"EXPECTED\_TRANSITION\_NOT\_ESTABLISHED" )¶

RETURN UNRESOLVED(¶

"INTERVENTION\_OUTCOME\_UNKNOWN" )¶

An unknown submission result shall not automatically cause unconditional retry.¶

10.7.8. Post-Repair Verification¶

FUNCTION VERIFY\_REPAIR(¶

incident, plan, intervention, verification\_observations ):¶

transition =¶

RECONCILE\_INTERVENTION(¶

intervention )¶

required\_recovery =¶

EVALUATE\_REQUIRED\_RECOVERY\_PREDICATES(¶

plan.verification\_requirements, verification\_observations )¶

adverse\_effects =¶

EVALUATE\_SPECIFIED\_ADVERSE\_EFFECTS(¶

plan, verification\_observations )¶

IF any mandatory recovery condition¶

is demonstrably violated:¶

RETURN INCOMPATIBLE(¶

"INCIDENT\_NOT\_VERIFIED\_RESOLVED" )¶

IF required observations are missing¶

OR transition is unresolved OR required adverse checks are unresolved:¶

RETURN UNRESOLVED(¶

"RECOVERY\_VERIFICATION\_INCOMPLETE" )¶

IF all required recovery conditions are established¶

AND specified adverse conditions are satisfied:¶

RETURN COMPATIBLE(¶

"VERIFIED\_RESOLVED\_UNDER\_DEFINED\_TESTS" )¶

RETURN UNRESOLVED¶

The procedure verifies the repair under the specified technical checks.¶

It does not prove that no future recurrence or unrelated failure is possible.¶

10.8. Particular Technical Invariants¶

The following invariants apply to the common workflow where the corresponding requirements are mandatory.¶

B-I01 - Incident-name equality is not incident identity. Two alarms with identical names¶

do not necessarily represent the same physical fault.¶

B-I02 - Event arrival is not event occurrence. Receiver arrival order cannot automatically¶

establish the order of underlying network faults.¶

B-I03 - Evidence retrieval is not evidence correctness. Successfully retrieving a record¶

does not establish its accuracy, completeness, or applicability.¶

B-I04 - Source authentication is not causal proof. A signed alarm does not independently¶

establish the root cause of an incident.¶

B-I05 - Agent agreement is not independent corroboration. Multiple reports derived¶

from one source are not automatically multiple independent observations.¶

B-I06 - Correlation is not automatically causation. Temporally associated symptoms¶

may require a separate causal model or discriminating evidence.¶

B-I07 - Hypothesis compatibility is not unique diagnosis. Several explanations may be¶

consistent with the available evidence.¶

B-I08 - Missing evidence is not contradicting evidence. Absence of a report is¶

contradictory only where the source's coverage and reporting semantics support that conclusion.¶

B-I09 - Current topology is not necessarily historical topology. Incident observations¶

shall be interpreted against the applicable network context.¶

B-I10 - Diagnosis confidence is not repair authority. Statistical confidence or agent¶

ranking cannot independently authorize a consequential operation.¶

B-I11 - Repair-plan identity is not plan applicability. A plan may remain associated with¶

the same incident while its required network-state conditions change.¶

B-I12 - Individually applicable repairs may conflict jointly. Shared resources and¶

competing interventions require the specified interaction evaluation.¶

B-I13 - Submission is not application. A successful submission or acknowledgement does¶

not automatically establish that a requested network change took effect.¶

B-I14 - Timeout is not proof of rejection. A missing response cannot establish that an¶

earlier operation was not applied.¶

B-I15 - Partial repair is not complete repair. Completion of one step does not establish¶

the success of a multi-step remediation.¶

B-I16 - Alarm clearing is not necessarily service recovery. Repair verification shall use¶

the outcomes required by the incident-specific procedure.¶

B-I17 - Verification failure is not always repair failure. Missing or unavailable¶

verification evidence shall be distinguished from a demonstrated unsuccessful repair.¶

B-I18 - Recovery of one service is not universal recovery. Relevant adverse effects or¶

dependencies shall be evaluated according to the plan.¶

B-I19 - Unresolved evidence shall not be silently promoted. A mandatory unknown¶

condition remains unresolved unless additional evidence or an expressly supported alternative procedure resolves it.¶

B-I20 - Diagnostic probes may change the system. A probe that modifies network state¶

shall not be treated as a purely passive measurement.¶

B-I21 - Historical diagnoses remain contextual. A diagnosis may remain accurate for an¶

earlier network state without justifying a current repair.¶

B-I22 - Replay does not guarantee new evidence. Replaying the same source events does¶

not create additional independent observations.¶

B-I23 - Causal models have applicability limits. A model's conclusions shall be¶

interpreted under its actual assumptions and available evidence.¶

B-I24 - Repair outcomes require receiving-interface semantics. Exactly-once¶

application or safe retry is not established by a local identifier alone.¶

B-I25 - Compensation is not automatic rollback. An inverse-looking operation may fail¶

to restore the original system state after intervening changes.¶

B-I26 - Recurrence requires an identified relationship. A later similar symptom is not¶

automatically the same incident.¶

B-I27 - Resource changes require materiality evaluation. An unrelated state change need¶

not invalidate every diagnosis or repair plan.¶

B-I28 - Diagnostic consistency is separate from effectuation authorization. Existing¶

protected execution-finality controls remain independently applicable.¶

10.9. Six Principal Workflow Variations¶

These variations form the technical basis for Embodiments 1.B through 6.B.¶

B-V1 - Incident Identity and Repeated-Alarm Correlation¶

Embodiment 1.B¶

The procedure addresses repeated, delayed, duplicated, or related alarm observations whose incident identity is initially uncertain.¶

Its distinct mechanisms will include incident merging and splitting, resource-generationaware alarm identity, source dependency tracking, and the distinction between one recurring fault and several concurrent faults.¶

The embodiment will examine alternative implementations using source event identities, topology-aware correlation, temporal windows, probabilistic clustering, and event-graph reconstruction.¶

B-V2 - Contradictory Multiagent Diagnoses¶

Embodiment 2.B¶

The procedure addresses several diagnostic agents producing incompatible or partially overlapping hypotheses.¶

Its distinct mechanisms will include evidence dependency analysis, contradiction testing, competing causal models, discriminating evidence selection, and prevention of unjustified confidence aggregation.¶

Alternative implementations will include rule-based, model-based, probabilistic, causalgraph, and distributed evidence reconciliation.¶

B-V3 - Cascading Faults and Cross-Domain Causality¶

Embodiment 3.B¶

The procedure addresses faults whose symptoms propagate across routing, transport, application, and infrastructure dependencies.¶

Its distinct mechanisms will include versioned dependency-graph reconstruction, propagation constraints, upstream/downstream causal hypotheses, multi-fault coexistence, and crossdomain evidence incompleteness.¶

Alternative implementations will include event-ordering models, topology graphs, factor graphs, structural causal models, and domain-local diagnostic processing.¶

B-V4 - Asynchronous Repairs and Competing Interventions¶

Embodiment 4.B¶

The procedure addresses repairs submitted by several agents or interfaces, including pending, delayed, rejected, duplicated, and unknown operations.¶

Its distinct mechanisms will include intervention identity, operation-state correspondence, concurrent-plan dependency evaluation, and status reconciliation.¶

Alternative implementations will include transactional management interfaces, native conditional operations, idempotent receivers, asynchronous workflows, and versioned intervention histories.¶

B-V5 - Partial Repair and Compensating Recovery¶

Embodiment 5.B¶

The procedure addresses repairs involving several dependent steps, only some of which succeed.¶

Its distinct mechanisms will include partial-state reconstruction, state-specific recovery planning, compensation feasibility, intervening-change detection, and selective replanning.¶

Alternative implementations will include native transactions, confirmed commit where supported, compensating actions, staged deployment, and device-side status reconstruction.¶

B-V6 - Post-Repair Verification and Recurrence Continuity¶

Embodiment 6.B¶

The procedure addresses verification of a repair through multiple observations and subsequent recurrence or related incidents.¶

Its distinct mechanisms will include repair-specific outcome predicates, post-intervention observation coverage, verification uncertainty, regression detection, and continuity of incident evidence across diagnostic handoffs.¶

Alternative implementations will include active and passive verification, service-level probes, bounded observation windows, source-verified event completeness, and distributed verification agents.¶

10.10. Specific Group-Level Design-Around Alternatives¶

The following alternatives describe substantively different technical ways to implement the common workflow.¶

They provide shared implementation support for the six detailed embodiments.¶

B-ALT-01 - Centralized Incident-Evidence Processor¶

Mechanism: A central service receives incident observations and agent hypotheses.¶

Implementation: The processor maintains the relevant evidence relationships, topology¶

context, diagnostic hypotheses, repair plans, and verification conditions in a shared repository.¶

Technical limitation: A central database does not inherently guarantee complete observation¶

history or current external network state.¶

Failure handling: Missing or conflicting source information remains explicitly unresolved.¶

Verification: Introduce delayed evidence and contradictory agent conclusions while the¶

central service operates.¶

B-ALT-02 - Distributed Peer-to-Peer Evidence Reconciliation¶

Mechanism: Several agents exchange sufficient evidence and hypothesis references to¶

evaluate a shared incident without requiring one central reasoning engine.¶

Implementation: Each agent retains its source observations and communicates the necessary¶

dependency relationships. A defined reconciliation procedure determines whether mandatory diagnostic conditions are established.¶

Technical limitation: Agreement among agents does not establish completeness of the¶

underlying evidence.¶

Failure handling: Missing peer responses or inconsistent evidence versions produce¶

appropriately qualified conclusions.¶

Verification: Partition one agent from the others and compare the result with a complete¶

reference history.¶

B-ALT-03 - Event-Sourced Incident Reconstruction¶

Mechanism: The system reconstructs the incident state from an initial state and retained¶

events.¶

Implementation: Alarm records, topology changes, diagnoses, interventions, and¶

verification events are interpreted according to supported ordering rules.¶

Technical limitation: Event replay cannot recover information never recorded.¶

Failure handling: Missing material event segments cause unresolved reconstruction.¶

Verification: Replay controlled histories containing duplicate, delayed, and missing events.¶

B-ALT-04 - Versioned State Snapshots¶

Mechanism: Incident analysis uses snapshots of relevant network topology and operational¶

state.¶

Implementation: Each observation or diagnostic result is associated with a snapshot or¶

corresponding versioned state reference.¶

Technical limitation: A management snapshot may not represent simultaneously applied¶

external network state.¶

Failure handling: Material version inconsistencies trigger reevaluation.¶

Verification: Change routing or configuration state during snapshot acquisition.¶

B-ALT-05 - Hybrid Snapshot and Event History Mechanism: A system reconstructs the relevant network state from a known snapshot and¶

later events.¶

Implementation: The processor selects a suitable baseline and applies the necessary changes¶

in their supported order.¶

Technical limitation: The event sequence and snapshot must refer to compatible resource¶

identities and versions.¶

Failure handling: Incomplete change history prevents unsupported conclusions.¶

Verification: Compare hybrid reconstruction with a controlled full event history.¶

B-ALT-06 - Graph-Based Incident Diagnosis Mechanism: Incidents, resources, symptoms, hypotheses, and dependencies are represented¶

as graph nodes and relationships.¶

Implementation: The processor traverses graph relationships to identify candidate causes,¶

shared dependencies, and affected downstream services.¶

Technical limitation: Missing edges may represent incomplete knowledge rather than¶

physical independence.¶

Failure handling: Graph coverage requirements are checked.¶

Verification: Remove a material topology relationship and verify that the resulting¶

uncertainty is preserved.¶

B-ALT-07 - Relational Evidence and Hypothesis Tables¶

Mechanism: The same relationships are implemented through relational records rather than a¶

graph database.¶

Implementation: Tables associate observations with source records, hypotheses, repairs, and¶

verification predicates.¶

Technical limitation: Queries must retain the required dependency and version relationships.¶

Failure handling: Missing mandatory joins or references produce qualified results.¶

Verification: Compare classifications with an equivalent graph implementation.¶

B-ALT-08 - Rule-Based Diagnostic Engine¶

Mechanism: Diagnostic hypotheses are generated and tested using defined fault rules.¶

Implementation: Rules identify symptoms, required resource relationships, contradictory¶

conditions, and expected causal implications.¶

Technical limitation: A rule set may omit relevant fault classes or depend upon unsupported¶

assumptions.¶

Failure handling: No matching rule does not automatically prove that no incident exists.¶

Verification: Exercise supported and unsupported fault patterns.¶

B-ALT-09 - Probabilistic Diagnosis¶

Mechanism: A probabilistic model evaluates candidate fault hypotheses.¶

Implementation: The system uses specified likelihoods, priors or learned parameters under¶

an identified model.¶

Technical limitation: Probabilistic confidence depends upon the model and its dependence¶

assumptions.¶

Failure handling: Missing calibration or unsupported extrapolation is identified.¶

Verification: Compare estimates with controlled labeled incidents and test cases involving¶

correlated evidence sources.¶

B-ALT-10 - Structural Causal Model¶

Mechanism: A causal model represents hypothesized relationships among faults, network¶

states and observed symptoms.¶

Implementation: The system evaluates evidence against the model and may identify¶

interventions or observations capable of distinguishing hypotheses.¶

Technical limitation: Observational data alone may not identify the causal structure¶

uniquely.¶

Failure handling: Causal conclusions exceeding the supported assumptions remain¶

qualified.¶

Verification: Use controlled simulations with known causal relationships and confounding¶

conditions.¶

B-ALT-11 - Agent Debate with Evidence-Constrained Adjudication¶

Mechanism: Several agents propose and challenge diagnostic explanations.¶

Implementation: Each claim is associated with source evidence and explicit assumptions.¶

An evaluator checks the supported relationships rather than merely counting votes.¶

Technical limitation: Multiple agents may repeat the same unsupported claim.¶

Failure handling: Claims lacking required evidence remain unresolved.¶

Verification: Provide several agreeing agents that all rely upon one inaccurate source and¶

one agent with independently verifiable contradictory evidence.¶

B-ALT-12 - Direct Deterministic Diagnosis Without Language Models¶

Mechanism: The workflow operates using deterministic software, monitoring rules, and¶

conventional fault models.¶

Implementation: Modules produce hypotheses and evaluate the same evidence-to-repair¶

conditions without an LLM.¶

Technical limitation: The diagnostic coverage depends upon the implemented fault models.¶

Failure handling: Unsupported incidents are escalated or remain unresolved.¶

Verification: Compare the processing results with a controlled incident reference set.¶

B-ALT-13 - Source-Local Evidence Evaluation¶

Mechanism: Evidence interpretation occurs near the monitored resource.¶

Implementation: Source-local processors emit qualified observations or diagnostic¶

summaries together with sufficient context for the receiving system.¶

Technical limitation: A source-local conclusion may lack cross-domain dependencies.¶

Failure handling: The global processor obtains required additional evidence or preserves¶

uncertainty.¶

Verification: Provide a locally plausible diagnosis contradicted by another network domain.¶

B-ALT-14 - Streaming Incremental Diagnosis Mechanism: Incident hypotheses are updated as new evidence arrives.¶

Implementation: The processor maintains an incident dependency index and reevaluates¶

only the affected relationships.¶

Technical limitation: An incomplete dependency index can omit material changes.¶

Failure handling: Broader recomputation is used where coverage cannot be established.¶

Verification: Compare incremental results with full replay over the same event history.¶

B-ALT-15 - Batch Diagnostic Reconstruction¶

Mechanism: Evidence is processed in defined batches or observation intervals.¶

Implementation: The processor collects the relevant data and performs a complete¶

evaluation of the available batch.¶

Technical limitation: Batch closure does not automatically establish event-stream¶

completeness.¶

Failure handling: Missing source coverage remains unresolved.¶

Verification: Deliver relevant alarms after the nominal batch closes.¶

B-ALT-16 - Active Diagnostic Probing¶

Mechanism: The system obtains additional observations through targeted diagnostic tests.¶

Implementation: The probe's expected observation, target scope, timing, and potential state-¶

changing behavior are identified.¶

Technical limitation: A probe may alter network load or operational state.¶

Failure handling: Tests requiring separate authorization or unsafe under the current¶

conditions are not treated as passive evidence acquisition.¶

Verification: Compare passive measurements with controlled probe results under known¶

fault conditions.¶

B-ALT-17 - Passive Diagnostic Observation Only¶

Mechanism: The system relies upon existing telemetry, configuration and log information.¶

Implementation: It evaluates the available sources without initiating additional network¶

probes.¶

Technical limitation: Some competing causal explanations may remain indistinguishable.¶

Failure handling: The workflow retains multiple hypotheses or an unresolved diagnosis.¶

Verification: Construct incidents observationally indistinguishable without intervention.¶

B-ALT-18 - Native Transactional Repair Interface¶

Mechanism: A remediation is submitted through an interface supporting suitable native¶

transaction or conditional-update guarantees.¶

Implementation: The plan records the applicable preconditions and expected state. The¶

interface's native operation semantics are used.¶

Technical limitation: Transaction guarantees may apply only to one datastore or device.¶

Failure handling: The system separately reconciles external operational effects and cross-¶

device dependencies.¶

Verification: Introduce concurrent configuration updates and test the actual interface¶

guarantees.¶

B-ALT-19 - Asynchronous Repair Workflow Mechanism: A remediation comprises operations with independently observable pending¶

and completion states.¶

Implementation: A workflow record associates steps with dependencies, submission¶

references, and observed outcomes.¶

Technical limitation: A workflow engine cannot automatically make multiple external¶

devices transactionally atomic.¶

Failure handling: Partial and unknown outcomes are reconstructed.¶

Verification: Fail a multi-step repair after the first step becomes effective.¶

B-ALT-20 - Conditional Repair with Current-State Reassessment¶

Mechanism: A proposed repair is reassessed when the relevant network state changes.¶

Implementation: The processor compares the plan's required resource attributes with the¶

available current or applicable state.¶

Technical limitation: An application-level state check does not guarantee atomic external¶

application unless the receiving interface supports it.¶

Failure handling: Material changes cause replanning, rejection, or another supported¶

response.¶

Verification: Change a required route or resource version between plan generation and¶

submission.¶

B-ALT-21 - Compensating Repair Workflow¶

Mechanism: The system identifies corrective operations for a partially applied repair.¶

Implementation: It reconstructs the actual state after the completed steps and determines¶

which compensating operations remain applicable.¶

Technical limitation: Compensation may not restore the exact original state.¶

Failure handling: Irreversible or conflicted operations are recorded as unresolved or require¶

another recovery procedure.¶

Verification: Apply an unrelated configuration change between the original repair and¶

attempted compensation.¶

B-ALT-22 - Verification by Multiple Independent Observation Classes¶

Mechanism: Repair success is evaluated through several required measurements or state¶

observations.¶

Implementation: The verifier checks the expected recovery conditions and relevant adverse¶

effects using source-aware evidence.¶

Technical limitation: The observation classes may share upstream dependencies and may¶

not be statistically independent.¶

Failure handling: Incomplete mandatory verification remains unresolved.¶

Verification: Clear an alarm while preserving a measurable service failure.¶

B-ALT-23 - Hardware-Assisted and In-Band Network Verification¶

Mechanism: Suitable devices, interfaces, or hardware monitors supply additional¶

operational-state or telemetry evidence.¶

Implementation: The system interprets the source's actual reporting and timing semantics¶

and relates the measurements to the incident and repair.¶

Technical limitation: Device-local observations may not establish end-to-end service¶

behavior.¶

Failure handling: Unsupported cross-domain conclusions remain qualified.¶

Verification: Compare local device health with end-to-end service measurements during a¶

controlled incident.¶

B-ALT-24 - Quantum-Related Sensing and Hybrid Computational Analysis¶

Mechanism: An implementation may incorporate suitable quantum-enhanced timing or¶

measurement information, or use an optional hybrid computational procedure for defined optimization subproblems.¶

Implementation: The source supplies the applicable measurement context and uncertainty.¶

Any proposed repair or diagnostic result remains subject to the ordinary evidence and statecorrespondence checks.¶

Technical limitation: No inherent quantum advantage, superior diagnosis, or universal¶

timing guarantee is presumed.¶

Failure handling: Unsupported measurement assumptions or unverified computational¶

outputs remain unresolved.¶

Verification: Compare outputs against conventional reference observations and¶

independently verified constraints.¶

10.11. Specific Design-Around Correspondence¶

The disclosed alternatives are not intended to require all network-incident systems to use the same architecture.¶

A centralized graph service may implement the relevant evidence relationships.¶

A distributed system may instead maintain source-local evidence and exchange sufficient dependency information.¶

A relational database may represent the same relationships without explicit graph nodes.¶

A deterministic rule engine may generate diagnostic hypotheses without a language model.¶

A probabilistic model may rank hypotheses while preserving uncertainty.¶

A causal model may propose additional discriminating observations.¶

A source-side analyzer may summarize local evidence.¶

A destination-side evaluator may independently reconstruct the underlying observations.¶

A native transactional configuration interface may provide stronger guarantees for certain repair operations.¶

An asynchronous workflow may instead need to reconstruct partial outcomes.¶

A compensation procedure may correct some effects but cannot automatically undo all previous operations.¶

An alarm-management interface may provide incident evidence without proving the root cause.¶

A notification replay mechanism may recover previously published events without proving that every physical event was detected.¶

A quantum-related sensor may supply measurement information without resolving causal ambiguity.¶

These different pathways may support some common technical functions but are not equivalent in every respect.¶

The shared technical relationship is the establishment of incident-specific correspondence¶

among evidence, diagnostic hypotheses, repair preconditions, observed interventions, and verification outcomes.¶

Where an alternative cannot establish a mandatory relationship, it cannot be treated as supplying that relationship merely because it uses a different algorithm or processing location.¶

The particular functional limitations supporting a general processing mechanism must be identified and enabled.¶

10.12. Worked Technical Example 1 - Competing Diagnoses¶

Consider a hypothetical packet network in which users experience elevated packet loss.¶

Three diagnostic agents investigate.¶

Agent A observes 92% utilization on a network interface and proposes congestion as the root¶

cause.¶

Agent B observes repeated routing adjacency changes and proposes routing instability.¶

Agent C observes a configuration change preceding the symptoms and proposes that the¶

change caused route oscillation, which increased traffic on the measured interface.¶

The agents report different diagnoses.¶

The common workflow does not select Agent A merely because its utilization measurement is numerically high.¶

It does not select Agent B merely because routing events occurred.¶

Instead, the processor reconstructs the relevant event, topology and configuration relationships.¶

Assume the following hypothetical event times under an adequately comparable clock basis:¶

Time Event¶

10:00:00 Routing policy configuration change applied 10:00:05 Routing adjacency begins oscillating 10:00:08 Traffic shifts to alternate path¶

10:00:10 Alternate interface utilization increases 10:00:12 Packet loss increases¶

This ordering is consistent with Agent C's causal explanation.¶

However, temporal ordering alone does not prove causation.¶

The system may seek configuration differences, route-state evidence, controlled historical comparisons, or other supported information.¶

Suppose subsequent evidence establishes that the policy change introduced a route-selection condition responsible for the observed oscillation under the applicable network model.¶

The system may then identify Agent C's hypothesis as sufficiently supported for the specified diagnostic procedure.¶

Congestion may still be a valid proximate symptom rather than the initiating root cause.¶

A repair targeting utilization alone may fail to address the routing instability.¶

The workflow preserves the distinction between the root-cause hypothesis, the resulting network symptoms, and the repair that addresses the relevant fault.¶

10.13. Worked Technical Example 2 - Concurrent Repair Changes¶

Assume a routing incident affects resource R.¶

Agent A proposes repair plan P1 based upon configuration version 14.¶

Agent B independently proposes repair plan P2 based upon the same incident.¶

P1 changes the route preference.¶

P2 changes an interface-specific configuration that may affect the same forwarding behavior.¶

Before either plan is applied, the plans may each appear technically applicable when evaluated separately against version 14.¶

However, if P1 changes a resource attribute required by P2, P2 may no longer be applicable after P1.¶

The intervention compatibility processor therefore identifies the dependency.¶

If the receiving network-management interface supports an appropriate conditional update against a specified state version, that capability may assist with conflict detection.¶

If no atomic current-state condition is available, an application-level recheck alone does not guarantee that a concurrent update cannot occur.¶

The processor records the actual interface guarantees.¶

Suppose P1 is applied and P2 subsequently becomes inapplicable.¶

The system reevaluates P2 instead of blindly submitting it using the earlier resource assumptions.¶

If P2 had already been submitted and its status is unknown, the system separately reconciles the actual operation outcome.¶

This example illustrates why diagnosis-to-repair correspondence must account for other ongoing interventions.¶

10.14. Worked Technical Example 3 - Repair Acknowledged but Recovery Unverified¶

A network-management service accepts a request to correct a routing configuration.¶

The management interface returns an acknowledgement.¶

The system records the acknowledgement as evidence of the corresponding submission or acceptance stage, according to the receiving interface's semantics.¶

It does not immediately classify the incident as resolved.¶

The repair verification specification requires:¶

1. The intended routing configuration to be established as applied. 2. The affected forwarding path to be operationally available. 3. Packet loss to remain within the specified acceptable range during an adequate observation interval. 4. The specified dependent service to meet its applicable recovery requirement.¶

Assume the configuration becomes applied and the route is restored.¶

However, the dependent service continues to experience packet loss above the required level.¶

The repair cannot be classified verified resolved under the stated verification specification.¶

The system may identify partial remediation and continue diagnosis.¶

If the packet-loss observation source becomes unavailable, the system instead reports incomplete verification rather than inventing a satisfactory measurement.¶

10.15. PHOSITA Implementation Architecture¶

A practical reference implementation may contain the following components.¶

Incident generator: Produces controlled alarm and service-degradation events.¶

Topology simulator: Maintains network resource and dependency relationships.¶

Configuration-state service: Provides intended and observed network configuration¶

information.¶

Telemetry and event repository: Stores source measurements and event records.¶

Evidence interpreter: Converts observations into technically interpretable source records.¶

Incident correlator: Associates potentially related symptoms with incident candidates.¶

Diagnostic Agent A: Applies one fault-analysis method.¶

Diagnostic Agent B: Applies a different fault-analysis method.¶

Evidence reconciliation processor: Identifies shared dependencies and evaluates competing¶

hypotheses.¶

Repair planner: Produces state-dependent remediation candidates.¶

Intervention registry: Maintains pending and observed remediation operations.¶

Simulated management interface: Represents accepted, rejected, applied, partial and¶

unknown operation outcomes.¶

Verification processor: Compares the observed recovery behavior with the plan's¶

verification requirements.¶

Incident-history repository: Maintains the relevant diagnostic and repair relationships.¶

The core workflow can be demonstrated entirely through software simulation.¶

A real router, switch, radio controller or production network is not required for initial validation.¶

The implementation may later use appropriate network-management interfaces.¶

10.16. PHOSITA Implementation Procedure¶

A PHOSITA may implement the common workflow through the following practical development stages.¶

Stage 1 - Define a Reference Network¶

Create a small network topology with several interconnected resources and at least one dependent service.¶

The topology should support controlled changes to routing, interface state, and resource utilization.¶

Stage 2 - Define Incident Types¶

Implement at least three incident patterns, such as link failure, routing instability, and resource congestion.¶

Each incident should have known underlying causes in the simulator.¶

Stage 3 - Implement Observation Sources¶

Produce source events and measurements having identifiable resource references, acquisition context, and reporting characteristics.¶

Support configurable delivery delays and duplicate records.¶

Stage 4 - Implement the Incident Evidence Model¶

Store source observations independently of agent interpretations.¶

Maintain evidence dependency references.¶

Stage 5 - Implement Two Diagnostic Methods¶

Use two distinct analysis methods, for example a deterministic rule engine and a topologydependent fault model.¶

The methods should be able to disagree under selected test conditions.¶

Stage 6 - Implement Hypothesis Reconciliation¶

Evaluate each hypothesis against the available evidence.¶

Identify shared source dependencies and contradictory observations.¶

Stage 7 - Implement Discriminating Evidence Requests¶

Define observations capable of distinguishing at least two competing explanations.¶

The system may obtain additional simulated telemetry.¶

Stage 8 - Implement State-Dependent Repair Plans¶

Associate each proposed repair with the relevant resource and configuration preconditions.¶

Stage 9 - Implement Competing Repairs¶

Generate two repair proposals affecting a shared resource.¶

Change the resource state after one proposal is created.¶

Stage 10 - Implement Intervention-State Tracking¶

Represent submission, acceptance, application, partial effect, rejection and unknown status separately.¶

Stage 11 - Implement Post-Repair Verification¶

Define incident-specific recovery requirements.¶

Generate verification observations under successful, partial and unsuccessful repair conditions.¶

Stage 12 - Implement Recurrence Detection¶

Introduce a later similar alarm.¶

Determine whether it corresponds to the earlier incident or represents a distinct event under the implemented relationship rules.¶

Stage 13 - Implement Alternative Architectures¶

Compare centralized and distributed evidence processing, or rule-based and probabilistic diagnosis, under equivalent controlled incident histories.¶

Stage 14 - Test Bounded Computation¶

Limit diagnostic search or repair-plan evaluation.¶

Verify that processing timeout is not incorrectly classified as proof that no valid diagnosis or repair exists.¶

Stage 15 - Record Test Results¶

Record actual classifications, source dependencies, processing latency and recovery outcomes.¶

Do not claim improved performance or successful validation without executing the corresponding experiments.¶

10.17. Common Group B Validation Plan¶

The following are proposed tests, not completed tests.¶

ID¶

B- T01¶

Test condition¶

One isolated alarm with sufficient source context¶

B- T02 Duplicate alarm delivery B- Identical alarm names from different T03 resources¶

B- T04 Delayed alarm from earlier incident¶

B- T05 Alarm source restart¶

B- T06 Related cascading alarms¶

B- T07 Unrelated simultaneous alarms¶

B- T08 Missing required topology edge B- T09 Topology changed after incident began¶

B- T10 Configuration requested but not applied¶

B- T11 Several agents copy one source report¶

B- T12 Agent reports without source evidence¶

B- T13 Two agents propose different diagnoses B- T14 Two compatible faults coexist¶

Required technical behavior¶

Incident candidate created¶

No artificial independent fault¶

Correct identity distinction¶

Event-time context preserved¶

Source generations distinguished¶

Relationships evaluated¶

No automatic incident merging¶

Unresolved topology relationship¶

Historical context interpreted¶

Lifecycle distinction preserved¶

Shared ancestry detected¶

Unsupported conclusion qualified¶

Conflict evaluated¶

Multi-fault possibility preserved¶

B- Hypothesis contradicted by reliable¶

Rejected under specified model T15 observation B- Missing alarm without source completeness¶

No unsupported contradiction T16 guarantee¶

B- T17 High correlation without causal evidence Causal conclusion qualified¶

B- T18 Known simulated causal dependency Applicable relationship detected¶

B- T19 Confounding network event Alternative cause considered¶

B- T20 Uncalibrated probability output No unsupported confidence guarantee¶

B- T21 Probabilistic model with dependent evidence Dependency assumptions examined¶

B- T22 Diagnostic probe changes network state Probe effect identified B- Passive evidence cannot distinguish¶

Unresolved outcome T23 hypotheses B- New evidence resolves one competing¶

Affected evaluation revised T24 hypothesis¶

B- T25 Repair plan based on outdated configuration Material mismatch detected B- Repair plan with incomplete resource¶

Applicability unresolved T26 mapping¶

B- T27 Two repairs modify same attribute Conflict evaluation¶

B- T28 Two repairs affect unrelated resources No unnecessary global invalidation¶

B- T29 Repair submission accepted but not applied Stages distinguished¶

B- T30 Operation rejected conclusively B- T31 Operation response lost¶

Rejection recorded¶

Outcome unknown until reconciled¶

B- T32 Receiver supports documented idempotency Retry follows contract¶

B- T33 Receiver lacks safe retry semantics B- T34 Multi-step repair partially applied¶

No blind safe-retry claim¶

Partial state reconstructed¶

B- T35 Compensation conflicts with later change B- Confirmed commit reverts under supported T36 conditions B- Repair clears alarm but service remains T37 degraded B- Repair restores service and required checks T38 pass¶

Reassessment required¶

Actual interface behavior observed¶

Not verified resolved¶

Verified under defined criteria¶

B- T39 Verification source unavailable Incomplete verification¶

B- T40 Repair affects dependent service adversely Relevant adverse effect detected¶

B- T41 Late post-repair observation Affected result reevaluated B- Similar fault returns under same resource¶

Recurrence relationship evaluated T42 generation B- Similar alarm occurs after resource¶

New incident possibility considered T43 reassignment¶

B- T44 Incident initially merged incorrectly Corrected incident relationships retained¶

B- T45 One incident later splits into two¶

B- T46 Source replay has incomplete retention¶

B- T47 Central versus distributed evaluator B- Graph versus relational evidence T48 representation¶

Evidence references preserved¶

Coverage limitation reported¶

Comparable result under same assumptions Equivalent required relationships evaluated¶

B- Unresolved rather than false T49 Diagnostic solver times out¶

impossibility¶

B- T50 Historical diagnosis after topology change Historical/current distinction maintained¶

B- T51 Authenticated but stale incident observation Staleness still evaluated¶

B- T52 Conflicting operational-state sources Reconciliation or unresolved B- Hardware-generated alarm with unsupported¶

No automatic semantic acceptance T53 interpretation¶

B- T54 Quantum-related timing source used B- Prior repair applied but acknowledgement T55 lost B- Multiagent agreement from one inaccurate T56 source B- Repair successful locally but fails end-to- T57 end requirement B- Recovery observation spans configuration T58 transition B- New incident overlaps unresolved previous T59 incident B- Result supplied to existing protected T60 enforcement facility¶

Actual uncertainty applied¶

External status not inferred from timeout¶

No false independent corroboration¶

Incomplete resolution recognized¶

Context compatibility evaluated¶

Distinct or related status evaluated¶

Technical applicability and authority remain separate¶

A reference implementation should preserve the initial scenario, source evidence, agent conclusions, event history, expected outcome, and actual outcome for every test.¶

The tests should be automated where practical.¶

A test result shall not be marked passed until the implementation has actually executed the corresponding case and the observed output has been checked.¶

10.18. Relationship to Established Network-Management Technologies¶

10.18.1. RFC 8632 - Alarm Management¶

RFC 8632 defines a YANG data model for alarm management.¶

A PHOSITA may use alarm-list information, notifications, and applicable alarm-management operations as incident evidence.¶

The source model does not independently establish the causal relationship between all observed alarms.¶

The Group B workflow supplies the separate incident-specific diagnostic interpretation.¶

10.18.2. RFC 8639 - Subscribed Notifications¶

RFC 8639 provides a mechanism for subscribing to event streams and includes optional replay functionality.¶

A PHOSITA may use subscriptions to obtain fault-related event records.¶

Replay capabilities and retention depend upon the publisher.¶

The workflow shall not assume that every source provides complete historical event records.¶

10.18.3. RFC 8641 - YANG-Push¶

Where supported, YANG-Push may provide subscribed datastore updates relevant to fault diagnosis and configuration-state tracking.¶

A PHOSITA may use the records as input to the incident evidence processor.¶

The system must still establish their source, timing, resource and semantic applicability.¶

10.18.4. RFC 8342 - Network Management Datastore Architecture¶

The Network Management Datastore Architecture distinguishes intended configuration, applied configuration, and operational state under the applicable model.¶

A PHOSITA may use those distinctions when evaluating repair preconditions and observed repair outcomes.¶

A configuration change being accepted or present in an intended datastore does not automatically prove that the desired operational effect occurred.¶

10.18.5. RFC 6241 - NETCONF¶

NETCONF provides established network configuration management operations.¶

Where supported, candidate configuration, confirmed commit, locking, and other mechanisms may provide useful facilities for implementing a repair.¶

A confirmed commit has specific protocol behavior and does not by itself establish end-toend service recovery.¶

A multi-device repair may require additional status and state reconciliation beyond any one device's configuration transaction.¶

10.18.6. Conventional Telemetry and Distributed Tracing¶

Operational logs, metrics, traces, topology information and diagnostic events may supply source evidence.¶

The system may use suitable tracing or correlation identifiers to associate reports with related processing events.¶

A trace identifier alone does not establish that the events share one physical root cause.¶

The incident model must interpret the relationship.¶

10.18.7. AI Agents and Conventional Analysis Engines¶

A diagnostic component may be implemented using a language model, rules engine, machine-learning model, graph algorithm, constraint solver, or ordinary program.¶

The workflow is not dependent upon a particular model vendor or AI architecture.¶

Agent-generated conclusions are associated with their evidentiary basis and applicable assumptions.¶

The system does not treat the mere presence of an AI-generated explanation as proof that the explanation is technically correct.¶

The foundational disclosure includes extensive mechanisms involving autonomous agents, network operations, protected execution finality, evidence governance, state continuity, remediation, and consequential operation enforcement.¶

Those earlier mechanisms are not reintroduced as new inventions merely because they may participate in the Group B workflow.¶

The proposed additional technical subject is the detailed incident-specific correspondence among:¶

| x | source observations; | |---|---| | x | causal and competing diagnostic hypotheses; | | x | independent and shared evidence dependencies; | | x | evolving network topology and resource state; | | x | repair plans and their technical preconditions; |¶

| x | competing interventions; | |---|---| | x | observed post-submission state transitions; | | x | repair-specific verification conditions; and | | x | recurrence or subsequent incident classification. |¶

The workflow can operate as an informational diagnostic and repair-consistency system without independently possessing authority to apply live network changes.¶

Where protected effectuation is required, the existing authority and Finality Sink mechanisms may remain separately applicable.¶

The presence of a distinct application context or additional wording does not itself establish novelty.¶

The exact scope of incremental subject matter must be determined by a detailed comparison against the foundational disclosure, other earlier filings, and applicable public prior art.¶

In particular, known alarm correlation, causal diagnosis, state-version checking, transaction processing, agent coordination, and repair verification techniques must not be characterized as individually new without supporting analysis.¶

10.20. Common Technical Effects¶

Depending upon implementation, the Group B workflow may provide one or more of the following technical effects:¶

(a) improved distinction between duplicated alarms and distinct fault events;¶

(b) more reliable association of symptoms with incident-specific topology and resource context;¶

(c) improved identification of shared evidence dependencies among agents;¶

(d) reduced unsupported aggregation of correlated agent conclusions;¶

(e) clearer distinction between causal hypotheses and observed correlations;¶

(f) more consistent handling of contradictory or incomplete diagnostic evidence;¶

(g) improved selection of diagnostic observations capable of distinguishing competing explanations;¶

(h) more accurate determination of repair-plan applicability after network-state changes;¶

(i) improved detection of conflicting concurrent repairs;¶

(j) more reliable differentiation of repair submission, acceptance, application, and observed service recovery;¶

(k) improved handling of unknown and partially applied interventions;¶

(l) more precise repair verification using incident-specific outcomes;¶

(m) better differentiation between failed repair and incomplete verification;¶

(n) improved association of recurring symptoms with historical incident context;¶

(o) support for multiple agent architectures and conventional deterministic implementations;¶

(p) improved reconstruction of incident histories under distributed or delayed information; and¶

(q) optional integration with existing network-management and protected effectuation systems.¶

The stated effects depend upon the actual implementation.¶

No particular reduction in mean time to repair, incident frequency, packet loss, service downtime, false positives, or diagnostic error rate is claimed without corresponding experimental evidence.¶

10.21. Non-Limiting Implementation and Design-Around Disclosure¶

The common workflow may be implemented through software, firmware, hardware-assisted monitoring, distributed computing systems, network-management services, or combinations thereof.¶

Incident evidence may be represented through event histories, graph records, relational tables, structured data objects, source-specific logs, or other suitable forms.¶

Diagnostic hypotheses may be generated through conventional rules, causal models, probabilistic models, AI agents, or other technical methods.¶

Repair plans may be represented through structured operations, configuration transactions, orchestration procedures, conditional updates, or other supported mechanisms.¶

The workflow does not require a universal incident ontology, one global clock, one controller, one central database, one AI model, or one network-management protocol.¶

Its specific technical functions may be distributed among agents and devices.¶

However, a technical requirement shall not be considered established merely because a differently named mechanism is present.¶

A system using probabilistic diagnosis must apply its actual uncertainty and evidence dependency assumptions.¶

A system using transactional repair must observe the real transaction guarantees.¶

A system using event replay must account for incomplete event histories.¶

A system using compensation must account for intervening state changes and operations that cannot be exactly reversed.¶

A system using quantum-related measurement technology must interpret the actual measured quantity and uncertainty.¶

The described alternatives supply concrete technical implementation pathways.¶

The scope of any subsequent claim shall depend upon its specific technical limitations, supporting disclosure, and the applicable prior art.¶

The central technical subject of Invention Group B is the establishment and continued evaluation of incident-specific correspondence between network observations, distributed diagnostic hypotheses, proposed repairs, actual network-state transitions, and verified operational outcomes.¶

4.2. Embodiment 1.B: Incident Identity and Repeated Alarms (Original lines 25482-28401)

EMBODIMENT 1.B - INCIDENT-IDENTITY RECONSTRUCTION AND REPEATED-ALARM CORRELATION UNDER DELAYED, DUPLICATED, AND INCOMPLETE NETWORK EVIDENCE¶

1.B.1. Embodiment Identification and Technical Scope Invention Group: B - Multiagent Network Incident Diagnosis and Repair.¶

Embodiment Number: 1.B - First Embodiment of Invention Group 2.¶

Technical Title: Systems and Methods for Establishing and Revising Network-Incident¶

Identity Across Repeated Alarm Occurrences, Source-Generation Changes, Delayed Notifications, and Incomplete Topology Evidence.¶

Common Workflow: B-CW-01.¶

Principal Variation: B-V1 - Incident Identity and Repeated-Alarm Correlation Under¶

Delayed or Incomplete Evidence.¶

Embodiment Algorithm: B-ALG-02.¶

Specific Design-Around Alternatives: B-1-DA-01 through B-1-DA-26.¶

Relationship to the Common Workflow: This embodiment elaborates the incident-¶

identification, observation-association, evidence-dependency, and incident-history functions of B-CW-01, particularly B-CW-01.01-B-CW-01.12 and B-CW-01.35-B-CW-01.36.¶

1.B.1.1. Principal Technical Subject¶

The present embodiment describes a network-incident correlation system configured to determine whether multiple alarm notifications, state-change events, and performance observations represent:¶

(a) duplicate delivery of one source event; (b) separate observations of one continuing alarm condition;¶

(c) repeated occurrences of an alarm condition within one continuing network incident;¶

(d) recurrence of a previously resolved or apparently resolved incident;¶

(e) different symptoms associated with a common underlying network fault;¶

(f) causally related downstream consequences of an upstream fault;¶

(g) independent faults occurring during overlapping time periods; or¶

(h) an unresolved relationship for which the available evidence is insufficient.¶

The system may receive observations from network devices, monitoring systems, event brokers, fault-management services, software agents, topology repositories, or other available information sources.¶

Observations may arrive out of order, be duplicated, be omitted, be corrected retrospectively, or be produced under changing resource identities.¶

The system is configured to maintain the information needed to distinguish the identity of a source alarm from the identity of the incident to which that alarm may belong.¶

The embodiment further provides methods for revising earlier incident-association decisions when additional evidence establishes that previously grouped observations should be separated or that previously separate incident records represent the same incident under the applicable relationship rules.¶

An implementation may perform correlation centrally, at an observation source, through distributed processors, or through a combination of components.¶

The embodiment does not require an AI model, a particular alarm-management protocol, a single global clock, one topology database, or a specific statistical classification algorithm.¶

1.B.1.2. Technical Boundary¶

The invention is concerned with incident-identity correspondence and its reconstruction from technical evidence.¶

It does not require that the processor establish a definitive physical root cause before constructing a provisional incident record.¶

Root-cause hypothesis reconciliation is elaborated separately in Embodiment 2.B.¶

Cross-domain cascading-fault reconstruction is elaborated separately in Embodiment 3.B.¶

Repair application and verification remain separate operations addressed by the remainder of Group B.¶

The present embodiment does not redefine earlier execution-finality mechanisms, including protected authority, finality capabilities, protected-state consumption, or Finality Sink operation.¶

1.B.2. Specific Technical Problems¶

1.B.2.1. Alarm Type Is Not Incident Identity¶

An alarm-management system may maintain an alarm identity using a resource reference, alarm type, and an applicable qualifier.¶

This identity is useful for referring to a particular alarm condition within the source's alarm model.¶

However, a resource may experience the same alarm condition on multiple occasions.¶

Those occurrences may belong to one continuing incident or to separate incidents.¶

Likewise, one incident may produce many alarm types on multiple resources.¶

A system that equates the alarm key with incident identity may incorrectly merge distinct failures or fail to correlate related secondary symptoms.¶

The present embodiment establishes a distinct incident-association relationship.¶

1.B.2.2. Repeated Delivery Is Not Repeated Occurrence¶

A source may transmit an alarm notification more than once because of replay, retry, redundant delivery, subscription reconnection, or another transport behavior.¶

Such delivery may represent one event record.¶

A monitoring system may also generate multiple actual event records describing a continuing or recurring alarm condition.¶

These are different circumstances.¶

A receiver that counts every delivery as a new alarm occurrence may create artificial incident frequency.¶

Conversely, a receiver that suppresses every repeated alarm name may lose evidence of actual fault recurrence.¶

The system must interpret the source event identity and lifecycle semantics.¶

1.B.2.3. Severity Updates Can Be Misinterpreted as New Faults¶

A source may change the severity or state of an existing alarm condition.¶

A critical alarm may become major or minor.¶

An alarm may become acknowledged, shelved, cleared, or otherwise updated under the applicable source model.¶

These events may describe changes to an existing alarm state rather than new physical fault occurrences.¶

A processor that treats each state update as a separate incident may overcount faults.¶

However, a clear followed by a new raise may represent a new occurrence.¶

The relevant distinction depends upon the source's state transition semantics and incident relationship.¶

1.B.2.4. Delayed Clear Events Can Reverse Apparent Incident Ordering¶

An alarm source may report an event at its occurrence time but deliver the notification later.¶

A clear event may be received after a subsequent raise.¶

An application using delivery order as occurrence order could incorrectly conclude that the most recent alarm is already cleared.¶

The processor must distinguish event ordering, source state transitions, and receiver arrival order.¶

Where occurrence ordering cannot be established, the system preserves the ambiguity.¶

1.B.2.5. Resource Identifier Reuse Can Merge Different Incidents¶

A device interface may be removed or reassigned.¶

The source may later reuse the same interface name or resource index.¶

An alarm generated before reassignment and another generated after reassignment may have identical source-local resource references.¶

Nevertheless, they may refer to different resource instances.¶

The system must evaluate resource-generation and mapping information where material to incident identity.¶

1.B.2.6. Related Symptoms May Appear on Different Resources¶

A physical-link failure may trigger routing adjacency loss, service degradation, tunnel failure, or other downstream symptoms.¶

A system using resource-name equality may record these as unrelated incidents.¶

A system using only temporal proximity may wrongly group independent events.¶

The processor therefore needs a technically supported relationship among source observations, resources, network topology, and incident hypotheses.¶

Such a relationship need not establish a final root cause.¶

1.B.2.7. Similar Symptoms May Have Independent Causes¶

Two links may independently fail within the same observation interval.¶

Both may produce a linkDown alarm.¶

A regional service outage may also coincide with an unrelated interface fault.¶

The incidents may overlap temporally without sharing an underlying cause.¶

Therefore, a temporal correlation window is a candidate-generation mechanism, not automatic proof of shared incident identity.¶

1.B.2.8. Incident Grouping Can Be Nontransitive¶

Suppose alarm A is temporally close to alarm B.¶

Alarm B is temporally close to alarm C.¶

Alarm A may nevertheless be too far apart from C or inconsistent with C's resource state.¶

Accordingly, pairwise similarity is not necessarily a transitive incident-identity relation.¶

A system that forms incident groups solely by computing connected components of a similarity graph may overmerge observations through chains of weak relationships.¶

The implementation must specify the group-level criteria under which candidate associations become established.¶

1.B.2.9. Incomplete Source History Can Conceal Separate Occurrences¶

A source may report an alarm raise, disconnect, and later report another alarm raise.¶

If a clear event occurred during the disconnected period but was not retained, the receiver may be unable to determine whether one condition continued or two distinct occurrences happened.¶

The processor shall not treat a missing clear as proof that the condition remained continuously active unless the source guarantees permit that inference.¶

1.B.2.10. Post-Correlation Evidence May Require Revision¶

A processing system may initially group several alarms as one incident.¶

Later observations may establish that one alarm came from a different resource generation or represented an unrelated fault.¶

The earlier grouping must be revisable.¶

However, deleting or overwriting the earlier record without preserving its references can break diagnostic histories, repair-plan associations, and auditability.¶

The embodiment therefore provides versioned revision of incident membership and relationship information.¶

1.B.3. Particular Technical Contribution Sought¶

The proposed technical contribution concerns a procedure that establishes incident identity through an explicit separation of source-event identity, alarm-state occurrence, resource context, and incident membership.¶

A representative implementation may perform the following functions:¶

1. Resolve a source event's identity within its reporting domain. 2. Determine whether a received record is a duplicate delivery of an existing event. 3. Interpret a nonduplicate record as an alarm-state transition or another observation. 4. Resolve the source resource instance and relevant state history. 5. Determine the occurrence interval or supported ordering relationship. 6. Associate observations with provisional incident candidates. 7. Obtain evidence of shared resource, topology, service, or causal relationships. 8. Evaluate mandatory conditions for same-incident classification. 9. Distinguish established relationships from contradictions and unresolved conditions. 10. Prevent pairwise similarity chains from causing unsupported group-level merging. 11. Preserve alternative candidate associations where identity remains ambiguous. 12. Support retrospective grouping or separation when new evidence becomes available. 13. Distinguish recurrence from uninterrupted fault continuity. 14. Associate downstream diagnostic and repair records with incident membership versions. 15. Preserve correction history without claiming that the earlier classification was necessarily correct.¶

The system may use a three-valued compatibility model.¶

A compatible outcome establishes that the corresponding incident relationship meets the specified technical rules.¶

An incompatible outcome establishes a mandatory violation.¶

An unresolved outcome indicates that the available evidence does not determine the required relationship.¶

The procedure is not asserted as a universal solution to causal fault diagnosis.¶

Its technical purpose is to make incident-association decisions reconstructible, qualified, and applicable to the observed network context.¶

1.B.4. Distinguishing Technical Objects¶

1.B.4.1. Source Event Identity¶

A source event identity identifies an event record within the reporting source's supported identity domain.¶

The representation may include a publisher identifier, source instance, stream generation, source event identifier, or another technically supported reference.¶

A numeric sequence value alone may not uniquely identify an event across source restarts.¶

1.B.4.2. Alarm Key¶

An alarm key identifies an alarm condition according to the applicable alarm-management model.¶

It may contain a resource, alarm type, and qualifier.¶

An alarm key is not necessarily an occurrence identifier or incident identifier.¶

1.B.4.3. Alarm Occurrence¶

An alarm occurrence is a distinguishable interval, event sequence, or lifecycle instance of the alarm condition.¶

A particular alarm key may have several occurrences over time.¶

An occurrence may include a raise, subsequent updates, and a clear where those events are established.¶

1.B.4.4. Incident Candidate¶

An incident candidate is an initial grouping or hypothesis that one or more observations may be associated with a common operational incident.¶

Its membership may remain provisional.¶

A candidate identifier need not assert an established root cause.¶

1.B.4.5. Incident Membership Relationship¶

An incident membership relationship associates an observation or alarm occurrence with an identified incident under specified technical criteria.¶

The relationship may be established, excluded, or unresolved.¶

It may identify the evidence and state version supporting its classification.¶

1.B.4.6. Incident Revision¶

An incident revision is a version of an incident record, including its current membership, interpretation, and relevant relationships.¶

A revision may result from added evidence, source correction, resource-state change, incident merge, or incident split.¶

The revision does not automatically erase earlier diagnostic or intervention records.¶

1.B.4.7. Recurrence Relationship¶

A recurrence relationship links distinct occurrences or incident episodes where the applicable technical evidence supports their recurrence classification.¶

Recurrence does not necessarily mean that the original fault remained continuously active.¶

1.B.4.8. Correlation Coverage¶

Correlation coverage describes the source observations, topology information, and historical intervals for which the processor has evidence sufficient to perform the specified incidentidentity evaluation.¶

Coverage may be complete only with respect to particular sources or requirements.¶

1.B.5. System Components¶

1.B.5.1. Multi-Source Event Receiver¶

The receiver obtains alarm notifications and related observations.¶

It may ingest push notifications, polled state changes, log records, subscribed events, or source-local alarm histories.¶

The receiver records the source identity and delivery context.¶

A delivery acknowledgement is not treated as proof of event uniqueness.¶

1.B.5.2. Source Event Identity Resolver¶

The resolver determines whether two received records refer to the same source event.¶

It applies source-defined identity semantics, including event identifiers and source generations.¶

Where the source cannot supply a reliable unique event reference, the resolver may use a qualified matching rule.¶

The rule's limitations are recorded.¶

1.B.5.3. Alarm Lifecycle Interpreter¶

The interpreter determines whether an event represents a new alarm occurrence, update to an existing occurrence, clear transition, acknowledgement, or other alarm-state change.¶

Its rules depend upon the source alarm model.¶

An implementation may support several alarm models simultaneously.¶

1.B.5.4. Resource Identity and Topology Resolver¶

This component obtains the resource-instance and topology context associated with each event.¶

It may resolve interface aliases, device generations, virtual resources, service paths, or source-specific object identifiers.¶

It does not assume that two resources with matching names are the same physical instance.¶

1.B.5.5. Event-Time Reconstruction Processor¶

The processor evaluates source event time, arrival time, clock uncertainty, and supported event-ordering relationships.¶

It may construct bounded occurrence intervals or logical ordering constraints.¶

It preserves uncertainty where exact event ordering cannot be established.¶

1.B.5.6. Incident Candidate Index¶

The candidate index identifies incidents potentially related to a newly received observation.¶

It may use event type, resource scope, topology locality, service association, occurrence interval, or another relevant feature.¶

Candidate retrieval is separate from confirmation of incident membership.¶

1.B.5.7. Incident Relationship Evaluator¶

The evaluator determines whether the observation satisfies the relationship requirements of each candidate incident.¶

It may consider:¶

| x | resource identity; | |---|---| | x | temporal relationships; | | x | alarm lifecycle; | | x | topology connectivity; | | x | service dependencies; | | x | known causal constraints; | | x | independent evidence; and | | x | incident-specific exclusion conditions. |¶

It returns established, incompatible, or unresolved status.¶

1.B.5.8. Group-Level Consistency Evaluator¶

The group-level evaluator checks whether proposed incident membership remains compatible with the incident's complete required context.¶

This prevents a chain of pairwise similarities from automatically merging observations with mutually inconsistent resource or timing conditions.¶

The evaluator may use group-level predicates, constrained clustering, a compatibility graph, or another adequate method.¶

1.B.5.9. Incident Revision Manager¶

The revision manager records supported changes to incident membership.¶

It may create merge or split revisions while preserving earlier membership records.¶

It maintains the relationship between revised incident identities and downstream diagnosis or remediation records.¶

1.B.5.10. Recurrence and Continuity Processor¶

The processor distinguishes continuing alarm conditions from separate occurrences and potentially recurring incidents.¶

It evaluates clear events, later raises, observation gaps, source-state changes, and other relevant information.¶

A recurrence classification may remain unresolved when the history is incomplete.¶

1.B.5.11. Source Completeness and Recovery Component¶

This component obtains source replay or historical records where supported.¶

It evaluates the available coverage under the source's actual reporting contract.¶

The absence of a source event is interpreted as negative evidence only where the source's coverage permits that conclusion.¶

1.B.5.12. Incident Dependency Interface¶

This interface supplies incident-membership revisions to other diagnostic and repair components.¶

A downstream component may determine whether its earlier analysis depended upon an incident grouping that has since changed.¶

This interface does not itself apply a network repair.¶

1.B.6. Representative Data Structures¶

1.B.6.1. Source Event Record¶

SourceEventRecord:¶

event\_record\_id publisher\_id publisher\_instance stream\_id stream\_generation source\_event\_id source\_sequence alarm\_resource\_reference alarm\_type alarm\_qualifier alarm\_transition\_type reported\_severity source\_event\_time receiver\_arrival\_time acquisition\_uncertainty source\_schema\_version original\_record\_reference replay\_or\_delivery\_context¶

The source event record distinguishes acquisition from delivery.¶

A source sequence is interpreted within its documented ordering domain.¶

1.B.6.2. Alarm Occurrence Record¶

AlarmOccurrence:¶

occurrence\_id source\_alarm\_key resource\_instance\_id source\_generation raised\_event\_references update\_event\_references clear\_event\_references earliest\_possible\_start latest\_possible\_start earliest\_possible\_end latest\_possible\_end occurrence\_status supporting\_evidence¶

An occurrence may have an unknown beginning or ending interval.¶

The presence of a clear event is interpreted according to the source's alarm lifecycle model.¶

1.B.6.3. Incident Membership Record¶

IncidentMembership:¶

membership\_id incident\_id incident\_revision member\_observation\_or\_occurrence\_id relation\_type relation\_status supporting\_evidence\_ids conflicting\_evidence\_ids unresolved\_conditions evaluated\_at\_state\_reference relation\_rule\_version¶

The relation type may distinguish same occurrence, shared incident, downstream symptom, recurrence, or another defined relationship.¶

1.B.6.4. Incident Candidate State¶

IncidentCandidate:¶

incident\_id incident\_generation current\_revision candidate\_members established\_members excluded\_members unresolved\_candidate\_members resource\_scope¶

topology\_context temporal\_extent incident\_state diagnosis\_references intervention\_references prior\_revision\_references¶

Candidate and established members are not conflated.¶

1.B.6.5. Incident Revision Record¶

IncidentRevision:¶

incident\_id previous\_revision new\_revision revision\_reason added\_members removed\_members revised\_relationships source\_correction\_references topology\_version\_references affected\_downstream\_records supersession\_relationships¶

The revision can preserve historical interpretation without treating an obsolete grouping as the currently established one.¶

1.B.6.6. Source Coverage Record¶

SourceCoverage:¶

publisher\_id source\_instance stream\_generation reporting\_filter required\_event\_classes covered\_sequence\_or\_time\_range available\_replay\_range known\_missing\_ranges source\_completeness\_contract evidence\_references coverage\_verdict¶

A replay-completed signal, when provided by a protocol, is interpreted only within the scope of the relevant stream and replay semantics.¶

1.B.6.7. Incident Correlation Result¶

IncidentCorrelationResult:¶

evaluation\_id input\_event\_id¶

duplicate\_delivery\_status alarm\_occurrence\_id candidate\_incident\_ids established\_incident\_id relation\_classification confidence\_or\_quality\_information unresolved\_conditions membership\_revision supporting\_evidence¶

The optional confidence information is not a substitute for mandatory compatibility evidence.¶

1.B.7. Detailed Operational Sequence¶

The following stages supplement the incident-identification and evidence-association portions of B-CW-01.¶

They focus on technical functions unique to incident identity reconstruction.¶

1.B-01 - Receive an Alarm or Related Event¶

The processor receives an event record.¶

It identifies the reporting source, source instance, event reference, alarm parameters, and available timing information.¶

The system distinguishes the incoming delivery from the underlying event it may represent.¶

Result: Source-qualified event candidate.¶

1.B-02 - Validate the Reporting Representation¶

The processor parses the source record under the applicable schema or event definition.¶

It checks relevant mandatory fields and their technical interpretation.¶

A valid syntax does not prove incident identity.¶

Result: Structurally interpretable event record.¶

1.B-03 - Establish Source Event Identity¶

The processor determines the identity domain in which the source event reference is meaningful.¶

It evaluates the publisher, stream generation, event identifier, and applicable source contract.¶

Where those elements establish equality with an existing event, the record is classified as another delivery of the same source event.¶

Where identity cannot be determined conclusively, the relationship remains unresolved.¶

Result: Source-event identity classification.¶

1.B-04 - Distinguish Duplicate Delivery¶

If the incoming record is an established duplicate of an existing source event, the processor may update delivery metadata without creating a new physical alarm occurrence.¶

The duplicate may be retained for transport diagnostics.¶

It is not treated as independent incident evidence.¶

Result: Deduplicated event association.¶

1.B-05 - Interpret the Alarm State Transition¶

For a nonduplicate source event, the processor determines whether it represents raise, update, severity change, clear, acknowledgement, or another transition.¶

The interpretation follows the source alarm model.¶

The system does not assume that every notification starts a new fault episode.¶

Result: Alarm-lifecycle transition.¶

1.B-06 - Resolve the Source Alarm Key¶

The processor identifies the applicable alarm key.¶

Where the source uses resource, alarm type, and qualifier, these are interpreted according to the source model.¶

The key identifies the alarm condition, not automatically the occurrence or incident.¶

Result: Alarm-key association.¶

1.B-07 - Resolve the Resource Instance¶

The processor determines the resource instance to which the event applies.¶

It considers source namespace, identity generation, mapping history, and relevant topology information.¶

If the source identifier was reused and the instance cannot be determined, incident association may remain unresolved.¶

Result: Resource-instance relationship.¶

1.B-08 - Determine Event Occurrence Context¶

The processor evaluates the available event timestamp, timing uncertainty, logical ordering, and relevant source sequence information.¶

It distinguishes occurrence time from receiver arrival time.¶

Where an event was delayed, the original applicable time context is retained.¶

Result: Supported event-time or ordering context.¶

1.B-09 - Reconstruct the Alarm Occurrence¶

The processor associates the event with an alarm occurrence where the lifecycle and resource relationships support that association.¶

A severity update may belong to an existing occurrence.¶

A clear may close an occurrence under the source's model.¶

A new raise after an established clear may create a distinct occurrence.¶

Where the clear or ordering evidence is incomplete, the relationship may remain provisional.¶

Result: Alarm occurrence or unresolved occurrence candidates.¶

1.B-10 - Determine Source History Coverage¶

The processor checks whether the available source records cover the interval or event sequence required to interpret the occurrence.¶

It may identify gaps caused by subscription interruption, retention expiry, filtering, source restart, or missing source information.¶

The absence of a clear event is not automatically proof of uninterrupted alarm activity when the relevant history is incomplete.¶

Result: Occurrence-coverage classification.¶

1.B-11 - Identify Candidate Incidents¶

The processor searches for existing incident candidates potentially related to the occurrence.¶

Candidate selection may use resource locality, event-time interval, service dependency, topology relationships, or other supported features.¶

Candidate retrieval does not establish membership.¶

Result: Candidate incident set.¶

1.B-12 - Evaluate Temporal Incident Relationships¶

The processor determines whether the occurrence interval has the temporal relationship required by a candidate incident.¶

It may use certain overlap, possible overlap, known precedence, or another specified relation.¶

A bounded time window may identify candidates but cannot alone prove same-incident identity where other mandatory conditions apply.¶

Result: Temporal relationship classification.¶

1.B-13 - Evaluate Resource and Topology Relationships¶

The processor identifies the relevant resource and topology context.¶

It determines whether the occurrence belongs to the candidate incident's applicable resource scope or has a supported dependency relationship.¶

Two alarms on the same resource may be separate incidents.¶

Two alarms on different resources may be related.¶

The actual correlation rule governs the decision.¶

Result: Resource/topology relationship classification.¶

1.B-14 - Evaluate Alarm Lifecycle Continuity¶

The processor determines whether the new occurrence represents an uninterrupted condition, a distinguishable recurrence, or a separate alarm episode.¶

It considers established raise/clear transitions, source-generation boundaries, observation gaps, and the relevant continuity model.¶

A later raise following an established clear is not automatically a continuation.¶

Result: Occurrence continuity classification.¶

1.B-15 - Evaluate Shared Incident Evidence¶

The processor obtains any available technical evidence connecting the occurrence with the incident candidate.¶

This may include shared fault signatures, resource dependency information, common upstream state changes, or a source-provided incident relationship.¶

A correlation identifier supplied by a source is interpreted according to its documented semantics.¶

It is not necessarily proof of a shared physical root cause.¶

Result: Shared-incident evidence evaluation.¶

1.B-16 - Evaluate Exclusion Conditions¶

The processor checks conditions that would contradict same-incident membership.¶

Examples may include different resource generations, incompatible event intervals under the defined incident model, established independent fault causes, or mutually exclusive resource conditions.¶

An exclusion must be based upon a required relationship rather than a superficial difference in alarm label.¶

Result: Explicit contradiction findings.¶

1.B-17 - Evaluate Missing Evidence¶

Where a mandatory condition remains unknown, the processor identifies the missing information.¶

The system may request replay, source state, topology history, or another supported record.¶

It does not convert absence of evidence into proof of the desired association.¶

Result: Evidence sufficiency evaluation.¶

1.B-18 - Classify the Pairwise Incident Relationship¶

The processor combines the applicable temporal, resource, lifecycle, and incident-evidence predicates.¶

The result may be:¶

| x | established relationship; | |---|---| | x | demonstrated incompatibility; or | | x | unresolved relationship. |¶

The result is associated with the specific candidate incident and evidence used.¶

Result: Pairwise candidate membership classification.¶

1.B-19 - Evaluate the Complete Candidate Incident¶

Before establishing new membership, the processor determines whether adding the occurrence preserves the incident's required group-level conditions.¶

The evaluation may consider incident temporal extent, resource scope, independent-fault exclusions, and any constraints involving more than two observations.¶

The processor does not treat a chain of weak pairwise similarities as sufficient proof of one incident.¶

Result: Group-level compatibility classification.¶

1.B-20 - Establish or Retain Provisional Membership¶

Where the required relationships are established, the processor may associate the occurrence with an existing incident revision.¶

Where several plausible incidents remain and no unique association is supported, the processor may retain provisional candidate links.¶

Where existing candidates are demonstrably incompatible, the processor may create a new incident candidate.¶

Result: Qualified incident association.¶

1.B-21 - Detect Potential Merge Conditions¶

The system determines whether evidence establishes that two existing incident candidates represent one incident under the applicable model.¶

The evaluation considers their complete membership and relevant state histories.¶

An overlapping observation is not sufficient by itself.¶

Result: Merge eligibility determination.¶

1.B-22 - Create an Incident Merge Revision¶

Where merge requirements are established, the revision manager creates a new incident grouping or membership revision.¶

It records the earlier incident references and the evidence supporting the merge.¶

Downstream records continue to identify the incident versions under which they were originally produced.¶

Result: Versioned merged incident relationship.¶

1.B-23 - Detect Potential Split Conditions¶

New evidence may establish that a prior incident grouping combined incompatible alarm occurrences.¶

The processor identifies the observations or relationships causing the conflict.¶

It determines which memberships require reassessment.¶

Result: Split eligibility determination.¶

1.B-24 - Create an Incident Split Revision¶

Where the supported evidence establishes separate groupings, the processor creates new incident memberships or successor incident records.¶

It retains links to the earlier grouping and its affected diagnostic records.¶

The procedure does not simply delete the prior record.¶

Result: Versioned split and supersession relationships.¶

1.B-25 - Evaluate Recurrence¶

Where a later occurrence resembles a previously resolved or apparently resolved incident, the processor evaluates whether it represents recurrence under the applicable technical rule.¶

The rule may consider resource identity, fault signature, source coverage, intervening stable state, or other relevant relationships.¶

Recurrence need not mean uninterrupted operation of the original fault.¶

Result: Recurrence relationship or unresolved classification.¶

1.B-26 - Reevaluate Downstream Dependencies¶

Where incident membership changes, the system identifies diagnoses, repair plans, or verification records that depended upon the previous grouping.¶

The downstream components determine whether the change materially affects their conclusions.¶

The incident correlator does not automatically execute or reverse a repair.¶

Result: Affected dependency set.¶

1.B-27 - Preserve the Incident Identity History¶

The processor records incident association decisions, source evidence, membership revisions, merge/split relationships, unresolved conditions, and recurrence links.¶

The storage format may be event-sourced, relational, graph-based, or another suitable representation.¶

Result: Reconstructible incident-identity history.¶

1.B.8. Technical Invariants¶

B1-I01 - Source Delivery Identity Is Distinct from Event Identity¶

Multiple deliveries of one source event shall not automatically become multiple source events.¶

B1-I02 - Source Event Identity Is Distinct from Alarm Occurrence Identity¶

Several distinct source events may describe one continuing alarm occurrence.¶

B1-I03 - Alarm Occurrence Is Distinct from Incident Identity¶

Multiple alarm occurrences may belong to one incident, while identical alarm keys may belong to separate incidents.¶

B1-I04 - Alarm Name Equality Is Not Same-Incident Proof¶

Matching alarm types or names do not establish shared incident identity without the required context.¶

B1-I05 - Arrival Order Is Not Occurrence Order¶

Delayed delivery shall not be used to invent event chronology.¶

B1-I06 - Source Generation Bounds Identifier Meaning¶

Event identifiers and sequence numbers shall be compared within their supported identity and ordering domains.¶

B1-I07 - Resource Alias Equality Is Not Resource-Instance Equality¶

A reused or ambiguous resource identifier shall not be interpreted as one persistent resource instance without evidence.¶

B1-I08 - A Clear Event Must Be Interpreted Under Its Source Model¶

An alarm clear may terminate an occurrence, but it does not automatically prove that every associated service or incident recovered.¶

B1-I09 - Missing Clear Is Not Proof of Continuous Fault¶

When source-history coverage is incomplete, absence of a clear event does not establish uninterrupted occurrence.¶

B1-I10 - Candidate Similarity Does Not Establish Membership¶

A temporal, resource, or signature similarity may justify candidate retrieval without establishing incident identity.¶

B1-I11 - Pairwise Similarity Is Not Necessarily Transitive¶

The implementation shall not use ordinary similarity-connected components as incident proof unless its group-level relationship actually supports transitivity.¶

B1-I12 - Group-Level Constraints Must Be Preserved¶

A newly associated observation shall not cause an established mandatory incident constraint to be violated without reevaluation.¶

B1-I13 - Related Symptoms Are Not Automatically One Root Cause¶

Cross-resource alarm correlation does not independently establish causal fault identity.¶

B1-I14 - A Correlation Identifier Is Interpreted According to Its Contract¶

Source-supplied grouping fields are not treated as proof of physical causation unless their semantics support that conclusion.¶

B1-I15 - Duplicate Evidence Shall Not Multiply Corroboration¶

Repeated transmission or derivation of one observation shall not create unsupported independent evidence.¶

B1-I16 - Incomplete Coverage Is Not Complete Coverage¶

A successfully connected subscription does not establish the historical completeness of every required event class.¶

B1-I17 - Replay Does Not Recreate Events Never Recorded¶

An event replay facility cannot establish acquisition of events outside its actual recording and retention guarantees.¶

B1-I18 - Incident Membership May Be Revised¶

New relevant evidence may require a supported change to an earlier incident association.¶

B1-I19 - Merge and Split History Must Remain Interpretable¶

An implementation maintaining historical diagnoses or repairs shall retain the relationship to the incident membership revision under which those records were produced.¶

B1-I20 - Recurrence Is Not Necessarily Continuous Presence¶

A subsequent similar alarm may represent a separate occurrence linked by a recurrence relationship.¶

B1-I21 - Timeout Does Not Prove Incident Nonexistence¶

Exhaustion of a correlation search budget does not prove that no matching incident exists.¶

B1-I22 - Incidental Topology Changes Are Not Universally Material¶

An unrelated topology update need not invalidate every prior incident association.¶

B1-I23 - Historical Membership and Current Incident State Differ¶

A historical event may remain correctly associated with an earlier incident revision even after network resources change.¶

B1-I24 - Source Authentication Does Not Establish Correlation Correctness¶

An authentic alarm may still be delayed, incomplete, ambiguous, or incorrectly associated with an incident.¶

B1-I25 - Incident Correlation Is Not Repair Authorization¶

Establishing an incident relationship does not independently authorize diagnosis-changing probes, configuration changes, or remediation operations.¶

1.B.9. Mathematical Implementation Relationships¶

The separate Group B mathematical specification provides the common notation.¶

The following embodiment-specific equations explain the incident-association algorithm.¶

These expressions are non-limiting and shall be interpreted under the stated assumptions.¶

1.B.9.1. Event Identity Domain¶

Let (e) be a received event record.¶

Define its source-qualified identity as:¶

(p\_e,g\_e,u\_e), ]¶

where:¶

x x x¶

(p\_e) is the publisher identity; (g\_e) is the source generation or other required identity scope; (u\_e) is a source event identifier.¶

The tuple establishes an identity only where the source contract guarantees the necessary uniqueness properties.¶

Otherwise, additional information or an unresolved status is required.¶

1.B.9.2. Duplicate Delivery¶

For two records (e\_i,e\_j), duplicate source-event identity may be established as:¶

[ D(e\_i,e\_j)= \\begin{cases} T,&\\operatorname{SID}(e\_i)=\\operatorname{SID}(e\_j) \\text{ under a valid uniqueness contract},\\ F,&\\text{distinctness is established},\\ U,&\\text{identity cannot be determined}. \\end{cases} ]¶

A bytewise identical payload is not necessarily sufficient to prove duplicate event identity.¶

Two separately generated events may legitimately contain identical fields.¶

1.B.9.3. Alarm Occurrence Mapping¶

Let (a(e)) denote the source alarm key of event (e).¶

Let (\\omega(e)) denote the alarm occurrence to which (e) belongs, if established.¶

Different source event records may satisfy:¶

[ a(e\_i)=a(e\_j) ]¶

while:¶

[ \\omega(e\_i)\\ne\\omega(e\_j). ]¶

Conversely, a raise and subsequent severity update may satisfy:¶

[ \\omega(e\_i)=\\omega(e\_j). ]¶

1.B.9.4. Incident Membership¶

Let (I) be a candidate incident, and (o) an alarm occurrence.¶

Define:¶

[ M(o,I)=¶

R(o,I)\\land T(o,I)\\land L(o,I)\\land E(o,I), ]¶

where:¶

| x | (R) denotes the required resource/topology relationship; | |---|---| | x | (T) denotes the required temporal relationship; | | x | (L) denotes lifecycle compatibility; | | x | (E) denotes the applicable incident-evidence condition. |¶

Each predicate is evaluated in three-valued logic.¶

Additional predicates may be required by the selected incident model.¶

The expression does not imply that all fault types use the same membership criteria.¶

1.B.9.5. Group-Level Membership¶

Let (O\_I) be the existing member set of incident (I).¶

Let (G(O\_I\\cup{o})) represent all mandatory group-level conditions.¶

Membership may be established only when:¶

[ M(o,I)=T \\quad\\text{and}\\quad G(O\_I\\cup{o})=T. ]¶

A pairwise similarity score is insufficient if the mandatory group-level conditions are violated or unresolved.¶

1.B.9.6. Nontransitive Similarity Example¶

Let (s(i,j)) indicate that two events are within a time-window threshold of 10 time units.¶

Consider events at times:¶

[ t\_A=0,\\quad t\_B=8,\\quad t\_C=16. ]¶

Then:¶

[ s(A,B)=T,\\qquad s(B,C)=T, ]¶

but:¶

[ s(A,C)=F. ]¶

A connected-component algorithm on the pairwise similarity graph would group A, B, and C.¶

Whether that grouping is valid depends upon the complete incident model.¶

If the required group temporal diameter is at most 10 units, the three-event group is invalid.¶

1.B.9.7. Occurrence Interval Uncertainty¶

Let an occurrence start be constrained to an interval:¶

[ t\_{\\mathrm{start}}\\in[s^-,s^+]. ]¶

Let the occurrence end be constrained to:¶

[ t\_{\\mathrm{end}}\\in[f^-,f^+]. ]¶

A clear event may narrow the end interval.¶

An incomplete source history may leave one bound unknown.¶

The processor shall not replace an unknown bound with an arbitrary exact timestamp.¶

1.B.9.8. Negative Evidence Under Coverage¶

Let (H) be a hypothesis that a required source event occurred.¶

Let (C) represent established source coverage under which the event would have been reported according to the source contract.¶

Only where that reporting property is established may the absence of the event be used as relevant negative evidence.¶

Formally, an inference of the form:¶

[ \\neg\\operatorname{Observed}(e) \\Rightarrow \\neg\\operatorname{Occurred}(e) ]¶

is generally invalid without additional source completeness and detection assumptions.¶

1.B.9.9. Incident Revision¶

Let (I^{(v)}) be incident (I) at membership revision (v).¶

A new observation or correction may produce:¶

\\operatorname{Revise} \\left(I^{(v)},\\Delta E,\\Delta R,\\Delta T\\right), ]¶

where the deltas represent relevant evidence, resource, or temporal changes.¶

The revision retains a reference to its predecessor where historical reconstruction is required.¶

1.B.9.10. Merge and Split Relationships¶

For proposed incident merge:¶

[ I\_{AB}= \\operatorname{Merge}(I\_A,I\_B) ]¶

only where the full group-level criteria are established.¶

A split may be represented by:¶

{I\_1,I\_2,\\ldots,I\_k}, ]¶

with the original membership revision retained as a historical referent.¶

These functions do not imply that every incident partition has one uniquely provable decomposition.¶

1.B.10. Detailed Implementable Pseudocode - B-ALG-02¶

1.B.10.1. Core Types¶

ENUM Verdict:¶

COMPATIBLE INCOMPATIBLE UNRESOLVED¶

ENUM RelationType:¶

DUPLICATE\_DELIVERY SAME\_ALARM\_OCCURRENCE SAME\_INCIDENT¶

DOWNSTREAM\_SYMPTOM RECURRENCE DISTINCT\_INCIDENT UNKNOWN\_RELATIONSHIP¶

ENUM AlarmTransition:¶

RAISE UPDATE SEVERITY\_CHANGE CLEAR ACKNOWLEDGE OTHER UNKNOWN¶

TYPE Event:¶

publisher\_id publisher\_instance stream\_generation source\_event\_id source\_sequence alarm\_key resource\_reference transition event\_time\_interval arrival\_time payload\_reference¶

TYPE Incident:¶

incident\_id revision established\_occurrences provisional\_occurrences excluded\_occurrences group\_constraints resource\_context temporal\_context history\_references¶

1.B.10.2. Resolve Duplicate Source Events¶

FUNCTION RESOLVE\_DUPLICATE\_DELIVERY(¶

incoming, source\_contract, event\_store ):¶

identity = BUILD\_SOURCE\_QUALIFIED\_EVENT\_ID(¶

incoming, source\_contract )¶

IF identity is established:¶

existing = event\_store.GET(identity)¶

IF existing exists:¶

RECORD\_ADDITIONAL\_DELIVERY(¶

existing, incoming.arrival\_time )¶

RETURN {¶

status: COMPATIBLE, relation: DUPLICATE\_DELIVERY, original\_event: existing }¶

RETURN {¶

status: INCOMPATIBLE, relation: NOT\_DUPLICATE\_OF\_KNOWN\_EVENT } candidates = FIND\_POSSIBLE\_DUPLICATES(¶

incoming, event\_store )¶

IF no possible candidates:¶

RETURN UNRESOLVED(¶

"NO\_RELIABLE\_UNIQUE\_SOURCE\_EVENT\_ID" )¶

RETURN UNRESOLVED(¶

"SOURCE\_EVENT\_IDENTITY\_AMBIGUOUS", candidates )¶

Absence from the current store does not prove that no matching event exists in unavailable historical storage.¶

The returned INCOMPATIBLE status in this example applies only to the tested duplicate relationship with known indexed records; it is not a general proof of global event uniqueness.¶

1.B.10.3. Reconstruct Alarm Occurrences¶

FUNCTION RECONSTRUCT\_ALARM\_OCCURRENCE(¶

event, alarm\_history, resource\_history,¶

source\_contract ):¶

resource = RESOLVE\_RESOURCE\_INSTANCE(¶

event, resource\_history )¶

IF resource unresolved:¶

RETURN UNRESOLVED(¶

"RESOURCE\_INSTANCE\_UNKNOWN" )¶

key = RESOLVE\_ALARM\_KEY(¶

event, source\_contract )¶

candidates = FIND\_OCCURRENCE\_CANDIDATES(¶

key, resource, event.event\_time\_interval )¶

FOR EACH occurrence IN candidates:¶

lifecycle = CHECK\_LIFECYCLE\_RELATION(¶

occurrence, event, source\_contract )¶

temporal = CHECK\_SUPPORTED\_EVENT\_ORDER(¶

occurrence, event, source\_contract )¶

IF lifecycle and temporal establish¶

same occurrence:¶

RETURN ASSOCIATE\_WITH\_OCCURRENCE(¶

event, occurrence )¶

IF one or more candidates remain unresolved:¶

RETURN UNRESOLVED(¶

"OCCURRENCE\_IDENTITY\_AMBIGUOUS",¶

candidates )¶

IF source evidence establishes a new¶

occurrence boundary:¶

RETURN CREATE\_NEW\_OCCURRENCE(¶

event, resource, key )¶

RETURN UNRESOLVED(¶

"NEW\_OCCURRENCE\_NOT\_ESTABLISHED" )¶

The system may create a provisional record when it cannot establish the lifecycle boundary.¶

1.B.10.4. Evaluate Incident Membership¶

FUNCTION EVALUATE\_INCIDENT\_MEMBERSHIP(¶

occurrence, incident, topology\_history, evidence\_store, incident\_rules ):¶

resource\_relation =¶

CHECK\_INCIDENT\_RESOURCE\_RELATION(¶

occurrence, incident, topology\_history, incident\_rules )¶

temporal\_relation =¶

CHECK\_INCIDENT\_TEMPORAL\_RELATION(¶

occurrence, incident, incident\_rules )¶

lifecycle\_relation =¶

CHECK\_INCIDENT\_LIFECYCLE\_RELATION(¶

occurrence, incident, incident\_rules )¶

evidence\_relation =¶

CHECK\_SHARED\_INCIDENT\_EVIDENCE(¶

occurrence, incident, evidence\_store, incident\_rules )¶

exclusions =¶

CHECK\_MANDATORY\_EXCLUSION\_CONDITIONS(¶

occurrence, incident, incident\_rules )¶

IF exclusions establishes violation:¶

RETURN INCOMPATIBLE result = COMBINE\_MANDATORY\_PREDICATES([¶

resource\_relation, temporal\_relation, lifecycle\_relation, evidence\_relation ])¶

IF result != COMPATIBLE:¶

RETURN result group\_result = CHECK\_GROUP\_LEVEL\_CONSTRAINTS(¶

incident.established\_members + occurrence, incident\_rules )¶

RETURN group\_result¶

The presence and mandatory nature of each predicate depend upon the selected correlation model.¶

1.B.10.5. Candidate Selection Without Transitive Overmerging¶

FUNCTION SELECT\_INCIDENT\_FOR\_OCCURRENCE(¶

occurrence, incident\_index, incident\_rules ):¶

candidates = incident\_index.FIND\_CANDIDATES(¶

occurrence )¶

supported = [] unresolved = []¶

FOR EACH incident IN candidates:¶

result = EVALUATE\_INCIDENT\_MEMBERSHIP(¶

occurrence, incident, incident\_rules )¶

IF result == COMPATIBLE:¶

ADD incident TO supported¶

IF result == UNRESOLVED:¶

ADD incident TO unresolved¶

IF size(supported) == 1¶

AND required uniqueness conditions hold:¶

RETURN ASSIGN\_TO\_INCIDENT(¶

occurrence, supported[0] )¶

IF size(supported) > 1:¶

RETURN RESOLVE\_GROUPING\_AMBIGUITY(¶

supported, occurrence, incident\_rules )¶

IF unresolved is not empty:¶

RETURN CREATE\_PROVISIONAL\_LINKS(¶

occurrence, unresolved )¶

IF incident index and relevant search coverage¶

are complete under the defined scope:¶

RETURN CREATE\_NEW\_INCIDENT\_CANDIDATE(¶

occurrence )¶

RETURN UNRESOLVED(¶

"INCIDENT\_SEARCH\_COVERAGE\_INCOMPLETE" )¶

The result is not determined through the mere count of matching candidate incidents.¶

1.B.10.6. Versioned Incident Merge¶

FUNCTION MERGE\_INCIDENTS(¶

incident\_A, incident\_B, evidence, incident\_rules ):¶

proposed\_members =¶

UNION\_OF\_DISTINCT\_OCCURRENCES(¶

incident\_A.members, incident\_B.members )¶

evaluation = CHECK\_FULL\_GROUP\_RELATION(¶

proposed\_members, incident\_rules, evidence )¶

IF evaluation != COMPATIBLE:¶

RETURN evaluation¶

merged = CREATE\_INCIDENT\_REVISION(¶

members = proposed\_members, predecessor\_incidents = [¶

incident\_A, incident\_B¶

], supporting\_evidence = evidence )¶

MARK\_PREDECESSORS\_SUPERSEDED(¶

incident\_A, incident\_B, merged )¶

NOTIFY\_DEPENDENT\_DIAGNOSTIC\_RECORDS(¶

merged )¶

RETURN merged¶

This creates a new versioned relationship.¶

It does not silently rewrite the historical source events.¶

1.B.10.7. Versioned Incident Split¶

FUNCTION SPLIT\_INCIDENT(¶

incident, new\_evidence, incident\_rules ):¶

conflicts =¶

FIND\_ESTABLISHED\_GROUP\_CONFLICTS(¶

incident.members, new\_evidence )¶

IF conflicts cannot be established:¶

RETURN UNRESOLVED(¶

"SPLIT\_NOT\_JUSTIFIED" )¶

candidate\_partitions =¶

CONSTRUCT\_PARTITIONS(¶

incident.members, conflicts, incident\_rules )¶

valid\_partitions = []¶

FOR EACH partition IN candidate\_partitions:¶

IF ALL\_GROUP\_CONSTRAINTS\_SATISFIED(¶

partition, incident\_rules ):¶

ADD partition TO valid\_partitions¶

IF no justified partition can be selected:¶

RETURN UNRESOLVED(¶

"INCIDENT\_PARTITION\_AMBIGUOUS" )¶

revision = RECORD\_SPLIT\_REVISION(¶

incident, selected\_partition, new\_evidence )¶

IDENTIFY\_AFFECTED\_DOWNSTREAM\_REFERENCES(¶

incident, revision¶

)¶

RETURN revision¶

A technically justified split need not require that every newly created incident possess an established root cause.¶

1.B.10.8. Reevaluate Late-Arriving Events¶

FUNCTION PROCESS\_LATE\_EVENT(¶

late\_event, incident\_store, source\_history ):¶

event\_context = RECONSTRUCT\_EVENT\_CONTEXT(¶

late\_event, source\_history )¶

IF event\_context unresolved:¶

RETURN UNRESOLVED(¶

"LATE\_EVENT\_CONTEXT\_UNKNOWN" )¶

affected\_incidents =¶

FIND\_INCIDENTS\_OVERLAPPING\_EVENT\_CONTEXT(¶

late\_event, event\_context )¶

revised = []¶

FOR EACH incident IN affected\_incidents:¶

result = REEVALUATE\_MEMBERSHIP\_AND\_LIFECYCLE(¶

incident, late\_event, event\_context )¶

IF result materially changes membership:¶

ADD CREATE\_INCIDENT\_REVISION(¶

incident, result ) TO revised¶

RETURN revised¶

A late clear may change an earlier occurrence boundary without necessarily changing every later diagnosis or repair outcome.¶

1.B.10.9. Recurrence Evaluation¶

FUNCTION EVALUATE\_RECURRENCE(¶

earlier\_incident, later\_occurrence, source\_coverage, resource\_history, recurrence\_rules ):¶

identity =¶

CHECK\_RELEVANT\_RESOURCE\_CONTINUITY(¶

earlier\_incident, later\_occurrence, resource\_history )¶

signature =¶

CHECK\_RECURRENCE\_SIGNATURE(¶

earlier\_incident, later\_occurrence, recurrence\_rules )¶

boundary =¶

CHECK\_ESTABLISHED\_INCIDENT\_EPISODE\_BOUNDARY(¶

earlier\_incident, later\_occurrence, source\_coverage )¶

IF mandatory recurrence exclusion established:¶

RETURN INCOMPATIBLE¶

IF all required recurrence conditions established:¶

RETURN COMPATIBLE(¶

relation = RECURRENCE )¶

RETURN UNRESOLVED(¶

"RECURRENCE\_RELATIONSHIP\_NOT\_ESTABLISHED" )¶

A system may create a new incident record while retaining a supported recurrence link to the earlier incident.¶

1.B.10.10. Bounded Correlation Search¶

FUNCTION CORRELATE\_WITH\_BUDGET(¶

event, candidate\_index, budget ):¶

examined = 0¶

FOR EACH candidate IN candidate\_index:¶

IF examined >= budget:¶

RETURN UNRESOLVED(¶

"CANDIDATE\_SEARCH\_INCOMPLETE" )¶

examined = examined + 1¶

EVALUATE\_CANDIDATE\_RELATIONSHIP(¶

event, candidate )¶

RETURN COMBINE\_COMPLETED\_EVALUATIONS()¶

A completed bounded search may support conclusions within its defined scope.¶

Failure to examine the complete relevant candidate space does not prove that no matching incident exists.¶

1.B.11. Worked Technical Examples¶

1.B.11.1. Example 1 - Duplicate Delivery Versus New Alarm Occurrence¶

A network device reports event E-104 identifying a link-down condition.¶

The monitoring system receives E-104 twice because of transport retry.¶

Both deliveries originate from the same source generation and source event identity.¶

The processor classifies the second delivery as a duplicate.¶

Later, after a confirmed clear and a new source raise, the device reports event E-119 for the same alarm key.¶

E-119 is a different source event and may represent a new alarm occurrence.¶

The two occurrences may still be related through a recurring incident.¶

Their alarm-key equality does not make their source events identical.¶

1.B.11.2. Example 2 - Severity Change During One Occurrence¶

A device reports a raised alarm at severity major.¶

The device subsequently reports severity critical for the same active occurrence.¶

The processor interprets the second event as a state transition where the source model establishes that relationship.¶

It does not automatically create a second incident.¶

If the source model instead reports two distinct alarm conditions, the implementation evaluates those semantics separately.¶

1.B.11.3. Example 3 - Late Clear After a New Raise¶

At source event time 12:00:00, an alarm is raised.¶

At 12:00:10, it is cleared.¶

At 12:00:20, a new occurrence is raised.¶

Because of delivery delay, the receiver sees the notifications in the following order:¶

| Arrival order | Source event time | Transition | |---|---|---| | 1 | 12:00:00 | Raise | | 2 | 12:00:20 | Raise | | 3 | 12:00:10 | Clear |¶

The processor shall not infer that the 12:00:20 raise was cleared merely because the clear notification arrived last.¶

The source event-time and lifecycle relationships determine the interpretation.¶

If the source event-time values are not sufficiently comparable, the status remains qualified until the required ordering can be established.¶

1.B.11.4. Example 4 - Two Independent Link Failures¶

Two unrelated network interfaces report link-down events within the same second.¶

The interfaces have no relevant shared dependency under the incident model, and available evidence establishes distinct physical fault causes.¶

A time-window correlator might group both alarms.¶

The disclosed group-level evaluator instead considers resource and fault relationships.¶

The alarms may be assigned to separate incidents.¶

1.B.11.5. Example 5 - Common Upstream Fault with Downstream Symptoms¶

A network interface fails.¶

A connected routing adjacency subsequently becomes unavailable.¶

A dependent service experiences packet loss.¶

The corresponding observations occur on different resource objects and use different alarm types.¶

An incident candidate may associate them through the applicable topology and event history.¶

However, the processor distinguishes a technically supported relationship from a confirmed root-cause conclusion.¶

A later diagnostic embodiment may evaluate the causal explanation in greater depth.¶

1.B.11.6. Example 6 - Reused Interface Identifier¶

A device removes interface index 8.¶

A replacement interface later receives index 8.¶

A link-down alarm from the original interface and an alarm from the replacement interface contain the same source-local index.¶

The processor consults the resource-generation information.¶

Where the records refer to different resource instances, identifier equality does not establish the same occurrence or incident.¶

1.B.11.7. Example 7 - Missing Clear During Source Disconnection¶

An alarm is raised.¶

The monitoring system loses connection to the source.¶

After reconnection, the receiver observes the alarm active.¶

The source does not provide complete historical state transitions.¶

The processor cannot determine whether the alarm remained continuously active or cleared and was raised again during disconnection.¶

It retains the unresolved lifecycle relationship.¶

A complete source history could later resolve the ambiguity.¶

1.B.11.8. Example 8 - Nontransitive Correlation Chain¶

Three alarm events occur at times 0, 8, and 16.¶

A simple time-window rule treats events within 10 time units as similar.¶

The first and second events match.¶

The second and third events match.¶

The first and third do not.¶

If the incident model requires all members to fall within a maximum temporal diameter of 10 units, the three-event set is incompatible.¶

The processor does not create one incident merely by taking the connected component of the pairwise similarity graph.¶

1.B.11.9. Example 9 - Retrospective Incident Split¶

An initial topology record incorrectly indicates that two alarm resources share the same parent device.¶

The processor provisionally groups their alarms.¶

A corrected topology record establishes that the resources belong to separate physical devices with independent faults.¶

The processor reevaluates the incident.¶

It creates a revised grouping and preserves references to the earlier membership decision.¶

Any earlier diagnostic result remains associated with the grouping revision it used.¶

1.B.11.10. Example 10 - Retrospective Incident Merge¶

Two alarm clusters are initially treated as separate incidents because a cross-domain topology relationship is unavailable.¶

Later, a supported topology and event record establishes that the clusters satisfy the model's shared-incident criteria.¶

The processor creates a merged incident revision.¶

It preserves references to the earlier incident candidates and the evidence supporting the merge.¶

A merge is not performed merely because both clusters have similar alarm names.¶

1.B.11.11. Example 11 - Recurrence After Verified Recovery¶

A network incident is classified resolved under a defined verification procedure.¶

Hours later, the same resource experiences a similar fault.¶

The later observation represents a new occurrence.¶

The system may create a new incident identity while associating it with the earlier incident through a supported recurrence relationship.¶

Recurrence does not imply that the original fault remained continuously active throughout the intervening period.¶

1.B.11.12. Example 12 - Source Correlation Identifier with Limited Meaning¶

An observation source attaches one correlation identifier to a group of notifications.¶

The source documentation defines the identifier as a reporting-session or event-batch reference.¶

The processor may use it to retrieve related reports.¶

However, the field does not establish a common root cause.¶

The incident-membership evaluator therefore applies the remaining required technical conditions.¶

1.B.12. Specific Alternative Implementation and Design-Around Pathways¶

The following 26 alternatives provide detailed implementation options for incident identity and repeated-alarm correlation.¶

They are not generic statements that different technologies are equivalent.¶

Each alternative identifies the technical arrangement, necessary implementation details, limitations, failure behavior, and representative verification.¶

B-1-DA-01 - Source-Assigned Unique Event Identity¶

Technical arrangement: The reporting source assigns a unique identifier to each event¶

within a defined source identity domain.¶

Implementation: The processor combines the event identifier with its publisher and¶

generation context and deduplicates repeated deliveries.¶

Alternative property: The system may recognize duplicate events without comparing¶

complete payloads.¶

Limitation: The uniqueness guarantee may terminate when the source restarts or its identifier¶

counter wraps.¶

Failure behavior: An unsupported cross-generation comparison remains unresolved.¶

Verification: Deliver one event several times, restart the source, and reuse the numeric event¶

identifier.¶

B-1-DA-02 - Composite Identity from Source and Lifecycle Context¶

Technical arrangement: A source lacks globally unique event identifiers but provides¶

resource identity, alarm key, lifecycle state, and sequence context.¶

Implementation: The processor constructs a source-qualified candidate identity using those¶

fields.¶

Alternative property: Event association can operate without a globally unique event UUID.¶

Limitation: Identical field values may describe separate events.¶

Failure behavior: Ambiguous matches are retained provisionally instead of forcibly¶

deduplicated.¶

Verification: Generate separate events with identical visible payloads.¶

B-1-DA-03 - Event-Log Position as Identity¶

Technical arrangement: Events are identified by positions within a durable event log or¶

partition.¶

Implementation: The processor uses the log identity, partition and position as the event¶

reference under the log's documented guarantees.¶

Alternative property: An independent event-ID field may be unnecessary.¶

Limitation: Log positions are not automatically comparable across different logs, partitions,¶

or source migrations.¶

Failure behavior: Cross-partition or rewritten-log ambiguity is explicitly handled.¶

Verification: Replay events from one partition and introduce another event at the same¶

numeric offset in a different partition.¶

B-1-DA-04 - Alarm-Lifecycle State Machine Technical arrangement: A state machine reconstructs alarm occurrences from raise, update,¶

clear, and related transitions.¶

Implementation: Each alarm key has an occurrence state, transition history, and source¶

generation.¶

Alternative property: Occurrences are distinguished through transitions rather than an¶

independently assigned occurrence identifier.¶

Limitation: Missing transitions may leave the lifecycle unresolved.¶

Failure behavior: An unsupported clear or repeated raise is interpreted according to the¶

source contract rather than assumed to follow a universal sequence.¶

Verification: Supply normal, missing, duplicated, delayed, and reordered transitions.¶

B-1-DA-05 - Source-Native Occurrence Identifiers¶

Technical arrangement: The alarm source supplies a distinct occurrence identifier.¶

Implementation: The processor associates events with the source's occurrence reference and¶

independently evaluates incident membership.¶

Alternative property: Occurrence reconstruction may require less inference.¶

Limitation: Source-defined occurrence identity is not necessarily global incident identity.¶

Failure behavior: Identifier reuse or incompatible source generations remain qualified.¶

Verification: Produce several occurrences of the same alarm key and compare the source¶

identifiers.¶

B-1-DA-06 - Event-Time Interval Correlation Technical arrangement: Event association uses occurrence-time intervals with bounded¶

uncertainty.¶

Implementation: The processor evaluates certain overlap, possible overlap, established¶

ordering, and configured incident interval constraints.¶

Alternative property: Arrival-time ordering and a common exact timestamp are¶

unnecessary.¶

Limitation: Large uncertainty intervals may prevent a determinate association.¶

Failure behavior: Potential overlap is not automatically treated as certain overlap.¶

Verification: Delay delivery and vary timestamp uncertainty around the permitted window¶

boundary.¶

B-1-DA-07 - Causal-Ordering Correlation¶

Technical arrangement: The system uses source-supported causal or logical ordering¶

relationships rather than exact physical time.¶

Implementation: Events are associated through message, process, or network-state¶

dependencies relevant to the incident model.¶

Alternative property: Common wall-clock synchronization may be unnecessary for certain¶

relations.¶

Limitation: Causal ordering alone does not establish a quantitative elapsed-time bound.¶

Failure behavior: Concurrent events without a known causal relationship remain unordered.¶

Verification: Generate concurrent, causally related, and unrelated event sequences.¶

B-1-DA-08 - Fixed-Window Candidate Index with Independent Group Verification¶

Technical arrangement: A fixed time window is used to retrieve candidate incidents¶

quickly.¶

Implementation: The processor identifies candidates within a configured interval, then¶

checks resource, lifecycle, and group-level constraints separately.¶

Alternative property: An efficient window-based index can coexist with stricter¶

membership verification.¶

Limitation: The chosen window may exclude relevant long-delay causal symptoms unless¶

the incident model provides another discovery method.¶

Failure behavior: A completed search is interpreted only within its stated candidate scope.¶

Verification: Produce a downstream alarm outside the window but within a known service¶

dependency.¶

B-1-DA-09 - Adaptive Temporal Candidate Windows¶

Technical arrangement: The candidate search interval varies according to source delay,¶

alarm class, or applicable network behavior.¶

Implementation: The processor uses technical delay bounds or model parameters to¶

determine a candidate interval.¶

Alternative property: A single universal fixed window is unnecessary.¶

Limitation: Adaptive windows can still be too narrow or too broad when source behavior is¶

poorly characterized.¶

Failure behavior: Unsupported delay assumptions result in unresolved coverage or broader¶

candidate search.¶

Verification: Vary measured source delays and compare candidate retrieval with a complete¶

reference set.¶

B-1-DA-10 - Topology-Constrained Alarm Grouping Technical arrangement: Candidate membership depends upon network resource¶

relationships.¶

Implementation: The processor obtains the applicable topology version and evaluates¶

whether alarm resources satisfy the incident's topology criteria.¶

Alternative property: Physical or logical dependency relationships may distinguish similar¶

alarms on unrelated resources.¶

Limitation: Topology adjacency does not prove one shared fault.¶

Failure behavior: Missing topology history remains unresolved where material.¶

Verification: Create alarms on adjacent resources with independent faults and on distant¶

resources connected through one service dependency.¶

B-1-DA-11 - Service-Dependency Graph Correlation¶

Technical arrangement: A service graph identifies how faults on one resource may affect¶

dependent services.¶

Implementation: The processor associates alarm occurrences with service dependency nodes¶

and evaluates the relationships required by the incident model.¶

Alternative property: Incident grouping need not rely solely upon device-level topology.¶

Limitation: Service dependencies may change dynamically or be incompletely reported.¶

Failure behavior: Incomplete dependency graphs do not establish causal independence.¶

Verification: Introduce a resource failure that affects two services through distinct¶

dependency paths.¶

B-1-DA-12 - Constraint-Based Incident Partitioning¶

Technical arrangement: Alarm occurrences are grouped according to explicit incident¶

constraints.¶

Implementation: The processor assigns occurrences to candidate incidents while enforcing¶

required resource, temporal, lifecycle, and exclusion predicates.¶

Alternative property: Grouping need not be based on a similarity threshold alone.¶

Limitation: Solving the complete partition problem may be computationally expensive.¶

Failure behavior: Solver timeout without proof returns an unresolved result.¶

Verification: Compare small problem instances against exhaustive partition enumeration.¶

B-1-DA-13 - Hypergraph Incident Correlation¶

Technical arrangement: Relationships involving several alarms or resources are represented¶

as hyperedges.¶

Implementation: Each hyperedge describes a required group relationship, such as a shared¶

dependency or higher-order exclusion.¶

Alternative property: Multi-observation constraints need not be reduced to pairwise edges.¶

Limitation: Hypergraph representation alone does not prove causation or solve grouping.¶

Failure behavior: Missing hyperedge conditions remain unresolved.¶

Verification: Construct three observations whose pairwise similarity is high but whose¶

group-level state is incompatible.¶

B-1-DA-14 - Relational Incident-Membership Tables¶

Technical arrangement: Incident membership is maintained through relational tables.¶

Implementation: Observation, occurrence, incident, revision, and evidence relationships are¶

stored using explicit keys and version references.¶

Alternative property: A specialized graph database is not mandatory.¶

Limitation: Queries must enforce the same mandatory relationship conditions.¶

Failure behavior: Missing joins or stale membership revisions are detected or qualified.¶

Verification: Compare results with an equivalent graph implementation.¶

B-1-DA-15 - Event-Sourced Incident History¶

Technical arrangement: Incident state is reconstructed from immutable source and¶

classification-change events.¶

Implementation: A processor replays alarm events and incident membership revisions to¶

obtain the state required by a query.¶

Alternative property: Historical incident revisions can be reconstructed without overwriting¶

original source records.¶

Limitation: Missing history segments may prevent complete reconstruction.¶

Failure behavior: Incomplete event history is reported explicitly.¶

Verification: Replay a sequence containing a merge followed by a split.¶

B-1-DA-16 - Versioned Snapshot with Incremental Revisions¶

Technical arrangement: The incident repository retains snapshots of membership together¶

with later revision records.¶

Implementation: The processor loads a relevant snapshot and applies subsequent changes¶

needed to reconstruct the current or historical incident state.¶

Alternative property: Full event replay need not be required for every query.¶

Limitation: The snapshot and revision history must refer to compatible incident and source¶

identity domains.¶

Failure behavior: Missing or inconsistent revision sequences prevent unsupported¶

reconstruction.¶

Verification: Compare snapshot-plus-revision results with a complete reference event¶

history.¶

B-1-DA-17 - Probabilistic Incident Association¶

Technical arrangement: An implementation assigns likelihoods or probabilities to potential¶

incident memberships.¶

Implementation: The model uses specified features, source characteristics, and applicable¶

dependence assumptions.¶

Alternative property: Candidate ranking may incorporate uncertain evidence.¶

Limitation: A probability does not establish a hard incident-identity predicate unless the¶

operating policy expressly defines a probabilistic criterion.¶

Failure behavior: Uncalibrated or out-of-distribution estimates are qualified.¶

Verification: Test calibrated scenarios and cases involving strongly dependent observations.¶

B-1-DA-18 - Graph-Based Machine-Learning Association¶

Technical arrangement: A model proposes related alarm clusters using resource and event¶

relationships.¶

Implementation: The model receives a graph or corresponding representation and returns¶

candidate associations, which are checked against the mandatory group-level conditions.¶

Alternative property: Candidate generation may be learned instead of rule-based.¶

Limitation: A model score does not prove source-event identity or causal commonality.¶

Failure behavior: Mandatory contradictions override a favorable candidate score.¶

Verification: Supply nontransitive similarity chains and resource-generation mismatches.¶

B-1-DA-19 - Source-Local Correlation with Cross-Domain Reconciliation¶

Technical arrangement: Sources or domain-local processors correlate their own alarm¶

events.¶

Implementation: Each source reports local occurrence relationships and supporting¶

evidence. A cross-domain processor evaluates whether local groups belong to a wider incident.¶

Alternative property: A central processor need not receive every raw event.¶

Limitation: Source-local groups may conceal distinctions required by cross-domain analysis.¶

Failure behavior: Missing supporting information prevents unsupported global merges.¶

Verification: Create a local alarm group containing one event from an independent fault and¶

evaluate the global association.¶

B-1-DA-20 - Federated Incident Identity Reconciliation¶

Technical arrangement: Different administrative domains maintain separate incident¶

identifiers.¶

Implementation: A federation layer records relationships among domain-local incidents¶

without requiring one domain to replace its identifiers.¶

Alternative property: One universal incident-numbering authority is unnecessary.¶

Limitation: Cross-domain evidence and topology information may be incomplete.¶

Failure behavior: Relationships remain provisional until sufficient information is available.¶

Verification: Correlate an end-to-end service outage spanning two domains with different¶

incident naming systems.¶

B-1-DA-21 - Incremental Streaming Correlation¶

Technical arrangement: Incident associations are updated as events arrive.¶

Implementation: A dependency index identifies candidate incidents affected by each new¶

event or correction. The processor updates the relevant memberships.¶

Alternative property: Full replay need not occur for every event.¶

Limitation: An incomplete dependency index can omit affected incidents.¶

Failure behavior: Broader reevaluation is performed where required.¶

Verification: Compare incremental classifications with full batch reconstruction.¶

B-1-DA-22 - Batch Reconstruction with Late-Event Correction¶

Technical arrangement: The processor evaluates incident groups over defined batches or¶

time ranges.¶

Implementation: Initial grouping uses the available events. Later source records may trigger¶

revisions to completed batches.¶

Alternative property: The system need not operate continuously in streaming mode.¶

Limitation: Closing a batch does not establish source completeness unless supported.¶

Failure behavior: A late material event causes a revised membership rather than being¶

ignored solely because the batch closed.¶

Verification: Deliver a clear event after the processing window ends.¶

B-1-DA-23 - Source-Completeness-Assisted Negative Evidence¶

Technical arrangement: The processor uses source reporting guarantees to interpret missing¶

events.¶

Implementation: A coverage record identifies the event classes, filters, time ranges, and¶

acquisition conditions for which source completeness is established.¶

Alternative property: The absence of an expected alarm may provide relevant evidence¶

where the required reporting conditions are supported.¶

Limitation: Complete delivery of generated records does not prove that the source detected¶

every physical fault.¶

Failure behavior: Unsupported detection or reporting coverage does not justify negative¶

causal inference.¶

Verification: Compare cases with complete source history and cases with known missing¶

intervals.¶

B-1-DA-24 - Incident Revision and Dependency Reconciliation¶

Technical arrangement: Changes to incident membership are represented through versioned¶

records.¶

Implementation: A merge or split creates a revision and identifies diagnostic or repair¶

records referring to the superseded grouping.¶

Alternative property: Correlation decisions can be corrected without overwriting the¶

historical context.¶

Limitation: A downstream system that ignores revisions may continue using an outdated¶

grouping.¶

Failure behavior: Revision notifications or queries expose the affected dependencies.¶

Verification: Split an incident after a repair plan has been prepared and verify that the plan's¶

incident reference remains interpretable.¶

B-1-DA-25 - Hardware-Assisted Event Identity and Monitoring¶

Technical arrangement: A device or hardware-associated monitor produces event identity,¶

ordering, or resource-generation information.¶

Implementation: The processor uses the hardware-supplied context under its supported¶

semantics.¶

Alternative property: Selected identity information may originate near the physical¶

monitored resource.¶

Limitation: Hardware-generated records may still be delayed, incomplete, or semantically¶

ambiguous.¶

Failure behavior: Missing source-history coverage is not overridden by the hardware origin.¶

Verification: Restart the device, reuse event counters, and introduce delayed alarm¶

messages.¶

B-1-DA-26 - Optional Quantum-Related Timing and Hybrid Correlation Computation¶

Technical arrangement: A deployment may use suitable atomic or quantum-enhanced¶

timing information or an optional hybrid optimization facility for candidate grouping.¶

Implementation: Timing information is interpreted with its actual source uncertainty. Any¶

candidate grouping generated by the computational facility is checked against mandatory incident constraints.¶

Alternative property: The timing source or computational substrate can differ without¶

changing the incident identity requirements.¶

Limitation: Quantum-related technology does not inherently establish event completeness,¶

shared root cause, or computational superiority.¶

Failure behavior: Incomplete source evidence and invalid candidate groupings remain¶

unresolved or incompatible.¶

Verification: Compare proposed groupings against a controlled incident history and¶

independently checked constraints.¶

1.B.13. Design-Around Functional Correspondence¶

The implementation alternatives provide different ways of establishing incident-association relationships.¶

A source-assigned event identifier can distinguish duplicate deliveries where the source guarantees uniqueness.¶

A composite identity can perform a corresponding function under adequate source context.¶

A lifecycle state machine can reconstruct alarm occurrences without a source-generated occurrence UUID.¶

A source-native occurrence identifier can reduce this reconstruction work.¶

Event-time correlation can evaluate delay and uncertainty through time intervals.¶

Causal-order correlation can evaluate certain ordering relationships without a synchronized physical clock.¶

Topology-constrained grouping can identify resource relationships that temporal correlation alone cannot establish.¶

Constraint-based partitioning and hypergraph processing can enforce group-level requirements that pairwise similarity may violate.¶

A learned model may propose candidates but must not be treated as establishing mandatory resource or lifecycle conditions merely because its confidence score is high.¶

Event sourcing can preserve a full classification history.¶

Snapshot-plus-revision storage may provide more efficient access to selected historical states.¶

Distributed local correlation can reduce central data movement but may lose cross-domain distinctions unless sufficient supporting information is preserved.¶

Federated correlation can preserve existing domain-local incident identities while constructing a higher-level relationship.¶

Source completeness may support particular negative-evidence inferences but only within the source's reporting and detection guarantees.¶

These architectures are technically different.¶

They do not provide identical guarantees in all circumstances.¶

The common requirement is that the processor evaluates the incident identity relationship using evidence adequate for the selected incident model.¶

Where the required relationship cannot be established, the result must not be represented as conclusively compatible.¶

The alternatives provide technical disclosure for subsequent claim development.¶

1.B.14. PHOSITA Implementation Using Existing Technologies¶

1.B.14.1. Alarm Management - RFC 8632¶

A PHOSITA may use the alarm model of RFC 8632, including resource references, alarm types, qualifiers, alarm state information, and related-alarm references.¶

The model provides an established basis for representing alarm conditions.¶

The present embodiment separately processes source-event delivery, alarm occurrences, and incident identity.¶

The technical contribution being investigated must not be characterized as invention of the standard alarm key or ordinary alarm-state management.¶

1.B.14.2. ITU-T X.733 Alarm Reporting¶

The relevant alarm reporting concepts of ITU-T Recommendation X.733 may provide source information including alarm-related causes, event attributes, and correlation information.¶

Such fields may be incorporated into the incident evidence model.¶

They do not automatically establish the physical relationship between every pair of notifications.¶

The processor evaluates the applicable source semantics and incident relationship.¶

1.B.14.3. Subscribed Notifications - RFC 8639¶

A PHOSITA may receive alarm events through a suitable subscription under RFC 8639.¶

Where the publisher supports replay, the system may use the available history to recover certain earlier event records.¶

The replay-completed notification is interpreted within the protocol's replay semantics.¶

It does not establish that an event source detected and recorded every underlying physical fault.¶

Replay retention and filtering constraints remain relevant.¶

1.B.14.4. YANG and Operational State¶

Suitable YANG models may represent resource identities, topology references, operational state, and alarm information.¶

The implementation may obtain historical or current resource mappings from available management systems.¶

A present operational-state value does not automatically represent the state applicable to an earlier alarm.¶

1.B.14.5. Network Topology Models¶

The processor may employ a network topology data model, graph repository, routing-state service, or other technically adequate source.¶

The source's historical coverage and update semantics determine whether it can establish the resource relationships applicable to an earlier event.¶

A network topology graph is not mandatory to every embodiment variation.¶

1.B.14.6. Conventional Database Technologies¶

An incident repository may use relational tables, key-value stores, event logs, graph databases, or document storage.¶

A PHOSITA may implement versioned incident memberships using ordinary transactions and revision references.¶

The choice of database does not independently establish correct incident grouping.¶

1.B.14.7. AI and Non-AI Correlation Engines¶

Candidate incident association may be proposed by rules, clustering algorithms, probabilistic models, graph models, language-model agents, or deterministic programs.¶

The selected engine remains subject to the required technical checks.¶

A model-generated explanation is not automatically accepted as root-cause evidence.¶

1.B.14.8. Cryptographic and Hardware Evidence¶

Where appropriate, source records may be authenticated or integrity-protected.¶

Hardware-associated event generation may supply additional source context.¶

The incident association procedure still evaluates the source's identity, timing, lifecycle, and resource relationships.¶

A verified signature alone does not establish correct correlation.¶

1.B.15. Reference Implementation Architecture¶

A practical reference prototype may comprise the following services.¶

Alarm generator: Produces controlled alarm transitions for several simulated network¶

resources.¶

Event transport simulator: Introduces duplication, delay, reordering, replay, and loss.¶

Source identity registry: Maintains publisher generations and event identity contracts.¶

Alarm lifecycle processor: Reconstructs occurrence intervals and state transitions.¶

Resource identity service: Supports resource aliases, instance generations, and¶

reassignment.¶

Topology history service: Supplies time-relevant resource relationships.¶

Incident candidate index: Retrieves potentially related incidents.¶

Incident relationship evaluator: Applies mandatory evidence, resource, lifecycle, and¶

temporal rules.¶

Group consistency evaluator: Prevents unsupported transitive overmerging.¶

Incident revision store: Supports merges, splits, supersession, and historical queries.¶

Recurrence processor: Distinguishes new incidents from continuing or recurring fault¶

patterns.¶

Test harness: Validates classification against known simulated ground truth.¶

The implementation may run on one computer using multiple software modules.¶

No production network equipment is required for initial validation.¶

1.B.16. PHOSITA Implementation Procedure¶

Phase 1 - Create the Reference Network¶

Simulate at least four resources arranged in a topology containing both connected and independent components.¶

Include one shared service dependency.¶

Phase 2 - Define the Alarm Source Model¶

Specify alarm keys, event identifiers, source generations, and lifecycle transitions.¶

Include raise, update, severity change, and clear events.¶

Phase 3 - Create Source Event Ground Truth¶

Maintain an independent reference history identifying which underlying faults and alarm occurrences produced each simulated event.¶

Do not expose the ground truth directly to the correlation algorithm.¶

Phase 4 - Implement Event Delivery Disturbances¶

Configure delayed, duplicated, reordered, and missing messages.¶

Include source restart and replay.¶

Phase 5 - Implement Source Event Deduplication¶

Apply the source identity contract.¶

Ensure repeated deliveries do not become repeated occurrences.¶

Phase 6 - Implement Alarm Occurrence Reconstruction¶

Associate valid lifecycle transitions into occurrences.¶

Preserve ambiguity when required clear or ordering evidence is missing.¶

Phase 7 - Implement Incident Candidate Retrieval¶

Use one or more candidate-index methods, such as time windows and resource locality.¶

Keep candidate retrieval separate from membership verification.¶

Phase 8 - Implement Incident Membership Predicates¶

Evaluate temporal, resource, lifecycle, and required incident evidence.¶

Use three-valued outcomes.¶

Phase 9 - Implement Group-Level Constraints¶

Construct a nontransitive similarity chain.¶

Verify that pairwise similarity does not cause an unsupported group merge.¶

Phase 10 - Implement Versioned Incident Merge¶

Generate two provisional incident records and later provide evidence establishing a shared incident.¶

Preserve historical references.¶

Phase 11 - Implement Versioned Incident Split¶

Introduce corrected evidence that invalidates an earlier grouping.¶

Create revised memberships while retaining the original record.¶

Phase 12 - Implement Recurrence Detection¶

Generate a fault, an established recovery interval, and a similar later fault.¶

Evaluate the configured recurrence conditions.¶

Phase 13 - Implement Incomplete-History Recovery¶

Remove an alarm clear from the receiver history.¶

Test behavior with and without supported source replay.¶

Phase 14 - Implement Alternative Correlation Strategies¶

Compare at least two approaches, such as constrained grouping and topology-based candidate selection, against the same ground-truth events.¶

Phase 15 - Test Resource Identifier Reuse¶

Reuse an interface reference for a different simulated resource instance.¶

Ensure the system does not silently merge incidents based on the reused name.¶

Phase 16 - Test Downstream Dependency Revisions¶

Associate a mock diagnosis with a preliminary incident version.¶

Split the incident and verify that the downstream record can identify its superseded membership context.¶

Phase 17 - Evaluate Performance¶

Record runtime, storage, candidate recall, false merges, false splits, unresolved classifications, and processing overhead.¶

Do not infer performance improvement without actual measurements.¶

Phase 18 - Preserve Reproducibility¶

Store input event histories, source contracts, topology revisions, expected groupings, actual classifications, and test results.¶

1.B.17. Failure and Recovery Conditions¶

F1 - Source event identifier missing: Unique event identity may remain unresolved.¶

F2 - Event identifier reused after restart: Source generation prevents unsupported¶

equality.¶

F3 - Duplicate transport delivery: Delivery duplication does not create another source¶

event.¶

F4 - Identical payload from distinct acquisitions: The processor does not incorrectly¶

deduplicate solely by payload equality.¶

F5 - Severity update: Alarm lifecycle semantics distinguish update from new occurrence.¶

F6 - Clear received after later raise: Event ordering is reconstructed rather than using¶

arrival order.¶

F7 - Source clock uncertainty: Ambiguous temporal relationships remain unresolved.¶

F8 - Monitoring subscription interrupted: Missing source coverage is recorded.¶

F9 - Replay retention expired: Historical events are not fabricated.¶

F10 - Source reports a clear under a changed alarm model: The transition semantics are¶

reevaluated.¶

F11 - Resource identifier reassigned: Resource instances are distinguished.¶

F12 - Topology history unavailable: A mandatory topology relationship remains¶

unresolved.¶

F13 - Independent faults overlap temporally: Group-level checks prevent automatic¶

merging.¶

F14 - Cascading fault spans multiple resources: Candidate grouping can use supported¶

dependency relationships.¶

F15 - Pairwise similarity forms a transitive-looking chain: Mandatory group-level¶

constraints are evaluated.¶

F16 - Candidate index omits a relevant long-delay symptom: Search coverage¶

limitations are recorded.¶

F17 - Incident merge conflicts with an established exclusion: Merge is not accepted.¶

F18 - Later evidence contradicts an earlier grouping: Incident membership is revised.¶

F19 - Incident split affects a prior diagnosis: The earlier dependency remains traceable.¶

F20 - Similar alarm recurs after recovery: A new occurrence is distinguished from a¶

continuing one.¶

F21 - Missing clear event: Continuous activity is not assumed without adequate coverage.¶

F22 - Several agents repeat one alarm report: Independent evidence is not artificially¶

multiplied.¶

F23 - Source reports a correlation ID with batch-only meaning: The ID is not treated as¶

causal proof.¶

F24 - Probabilistic model yields high similarity despite mandatory conflict: The¶

conflict is not ignored.¶

F25 - Candidate search budget exhausted: Search incompleteness remains unresolved.¶

F26 - Incident record schema revised: Earlier membership remains interpretable under its¶

recorded definition.¶

F27 - Cryptographically valid but stale source event: Authentication does not override¶

contextual mismatch.¶

F28 - Hardware event counter resets: Counter-generation relationships are evaluated.¶

F29 - Recurrence relation cannot be established: Similarity alone does not prove¶

recurrence.¶

F30 - Corrected source data arrives after incident closure: Historical association may be¶

revised without assuming physical rollback or repair reversal.¶

1.B.18. Representative Test Suite¶

The following 55 tests are proposed implementation cases. They are not reported as executed.¶

Test ID¶

B1- T01¶

Controlled scenario¶

One valid raise event¶

Expected behavior¶

New occurrence candidate¶

B1-¶

Same event delivered twice Duplicate delivery recognized T02¶

B1-¶

Same payload in two valid distinct events No erroneous deduplication T03¶

B1- Same event identifier in different source¶

Identity scopes distinguished T04 generations¶

B1-¶

Two publishers use same numeric event ID No cross-publisher collision T05¶

B1- T06 B1- T07 B1- T08 B1- T09 B1- T10 B1- T11 B1- T12 B1- T13 B1- T14 B1- T15 B1- T16¶

Raise followed by severity update¶

Raise followed by acknowledgement¶

Raise followed by supported clear¶

Clear delayed beyond next raise¶

Clear missing during history gap¶

Repeated raise with ambiguous source semantics¶

Source event replay¶

Replay history incomplete¶

Subscription connects successfully but historical gap exists Identical alarm key on different resource instances Resource alias changes without instance change¶

Existing occurrence updated¶

No automatic new fault¶

Occurrence boundary established¶

Correct chronology if supported¶

Continuity unresolved¶

Occurrence ambiguity retained¶

Original source identity preserved¶

Coverage limitation reported¶

No false completeness¶

Resource identity distinguished¶

Supported continuity maintained¶

B1-¶

Resource identifier reused after reassignment Separate instances recognized T17¶

B1- T18 B1- T19¶

Different alarm types from one supported incident¶

Similar alarms from independent faults¶

Cross-type association evaluated¶

No unsupported merge¶

B1- T20 B1- T21 B1- T22 B1- T23 B1- T24 B1- T25 B1- T26 B1- T27 B1- T28 B1- T29 B1- T30 B1- T31 B1- T32 B1- T33¶

Similar alarms on dependent services Dependency relationship evaluated¶

Candidate, not automatic Temporal proximity only¶

membership¶

Certain temporal overlap Time predicate established¶

Possible but uncertain overlap Unresolved when certainty required¶

Event occurrence order differs from arrival order Independent source clocks without known offset¶

Occurrence time used¶

No invented chronology¶

Supported logical causal order Ordering used appropriately¶

Nontransitive similarity chain Group-level overmerging prevented¶

Pairwise compatible but group constraint¶

Candidate group rejected violated¶

Group constraint missing necessary data Membership unresolved¶

Two provisional incidents later supported as¶

Versioned merge one¶

Proposed merge violates exclusion Merge withheld¶

One incident later proven to contain two faults Versioned split¶

Incident partition remains ambiguous No fabricated unique split¶

B1-¶

Earlier diagnosis refers to pre-split version Dependency remains traceable T34¶

B1-¶

New evidence arrives after incident close Historical revision supported T35¶

B1- T36 B1- T37¶

Recurrence after verified stable period¶

Continuous fault with repeated severity changes¶

New occurrence with supported recurrence link¶

No false recurrence¶

B1-¶

Similar alarm after resource replacement Recurrence relationship evaluated T38¶

B1- T39¶

Source correlation ID denotes only message¶

No causal proof inferred batch¶

B1-¶

Multiple agents quote one event No duplicate corroboration T40¶

B1-¶

Topology changes after source event Historical topology used T41¶

B1- Current topology incorrectly substituted for¶

Misassociation detected T42 historical¶

B1-¶

Missing topology dependency Unresolved if material T43¶

B1- T44¶

Negative evidence evaluated within Source coverage supports absence inference¶

contract¶

B1-¶

Detection completeness unknown Absence not treated as proof T45¶

B1- T46 B1- T47 B1- T48 B1- T49 B1- T50¶

Probabilistic candidate conflicts with hard identity rule Graph clustering versus constrained partitioning Event-sourced versus snapshot-plus-revision history¶

Incremental versus batch evaluation¶

Bounded search stops before complete candidate scan¶

Hard mismatch retained¶

Differences explained by constraints¶

Equivalent reconstruction under same data Same classification under equivalent evidence¶

Unresolved coverage¶

B1-¶

Hardware source generation restarts Identity domain updated T51¶

B1- Signed alarm refers to obsolete resource¶

Staleness detected¶

| T52 | mapping | |---|---| | B1- | Quantum-related timestamp has uncertainty | | Uncertainty applied | | | T53 | bound | | B1- | Incident membership changes after repair |¶

Affected dependency identified T54 proposal¶

B1-¶

Correlation result supplied to diagnostic agent No independent authority to repair T55¶

For every test, the reference implementation should record the source event identities, alarm lifecycle ground truth, resource history, topology state, expected incident membership, actual membership, unresolved conditions, and any membership revisions.¶

Passing a duplicate-delivery test does not establish correct root-cause correlation.¶

Passing a time-window test does not establish group-level membership correctness.¶

Passing a merge test does not establish the correctness of all future revisions.¶

1.B.19. Relationship to Group B Mathematical Specification¶

The common Group B mathematical specification provides a general framework for incident evidence, three-valued relations, source dependency, causal uncertainty, and incident-state processing.¶

Embodiment 1.B extends that framework with distinct identity and grouping relationships:¶

1. Source event identity scoped by publisher and generation. 2. Alarm keys separated from occurrence instances. 3. Occurrence intervals under incomplete transition histories. 4. Incident membership predicates. 5. Nontransitive similarity and group-level constraint checks. 6. Qualified source completeness and negative evidence. 7. Versioned incident merges and splits. 8. Recurrence relationships. 9. Calculation-budget handling and unresolved candidate search. 10. Dependency propagation after historical membership revisions.¶

These models may be further developed in a mathematical supplement.¶

Any added formal equation should retain the relevant assumptions and should not be used to claim certainty where the source evidence is incomplete.¶

1.B.20. Relationship to the Foundational International Disclosure¶

The foundational disclosure already encompasses extensive autonomous-agent governance, network-state processing, protected effectuation, and operational continuity mechanisms.¶

The present embodiment does not reintroduce those foundational mechanisms as newly invented merely because they may be used within an incident-correlation service.¶

The proposed additional subject matter concerns the specific separation and reconstruction of source-event identity, alarm occurrence, and network-incident membership across delayed, repeated, incomplete, and retrospectively corrected evidence.¶

In particular, the proposed technical focus includes group-level membership conditions and versioned corrections to incident identity.¶

These features must be examined against the actual earlier disclosure and outside prior art.¶

Existing standards already provide alarm identity fields, related-alarm references, and eventreplay mechanisms.¶

The appropriate analysis is whether the particular combined processing procedure has an adequately supported technical distinction that is novel and inventive under applicable law.¶

1.B.21. Particular Technical Effects¶

Depending upon implementation, the embodiment may provide:¶

(a) more accurate distinction between duplicate event deliveries and repeated alarm occurrences;¶

(b) reduced false incident creation caused by repeated notifications;¶

(c) reduced false merging of separate faults sharing alarm names or time windows;¶

(d) improved reconstruction of delayed raise and clear transitions;¶

(e) better treatment of incomplete source histories;¶

(f) more reliable handling of source restart and identifier reuse;¶

(g) improved use of topology and resource-instance relationships in incident correlation;¶

(h) explicit recognition of nontransitive similarity limitations;¶

(i) more accurate distinction between continuing faults and recurring incidents;¶

(j) preservation of unresolved membership where evidence is inadequate;¶

(k) support for retrospective incident merges and splits;¶

(l) improved traceability of earlier diagnostic conclusions after incident membership revisions;¶

(m) support for event-sourced, relational, graph-based, and distributed correlation architectures;¶

(n) compatibility with existing alarm-management models and notification systems;¶

(o) improved ability to evaluate candidate associations under bounded computation; and¶

(p) optional integration with hardware-generated event context or quantum-related timing information.¶

These are potential effects.¶

No particular false-correlation reduction, diagnosis accuracy, storage saving, execution speedup, or mean-time-to-repair improvement is asserted without experimental evidence.¶

1.B.22. Non-Limiting Character and Claim-Supporting Disclosure¶

The present embodiment may be implemented using conventional processors, distributed software services, network devices, event brokers, management systems, persistent repositories, or combinations thereof.¶

Alarm events may be represented through standardized management models, source-specific records, or other technically adequate formats.¶

The system may use source event identifiers, occurrence identifiers, composite keys, eventlog positions, or qualified matching methods.¶

Incident grouping may use deterministic predicates, constrained clustering, graph methods, topology-dependent analysis, probabilistic candidate generation, event histories, or other suitable techniques.¶

No one incident numbering scheme, time synchronization mechanism, database, AI model, or alarm-management protocol is mandatory.¶

However, the technical meaning of a correlation decision depends upon the relationships actually established by the implementation.¶

A system that receives duplicate notifications does not necessarily possess sufficient information to distinguish recurring alarm occurrences.¶

A system that assigns high similarity scores does not necessarily establish same-incident identity.¶

A system that stores an event graph does not necessarily possess complete topology or causal evidence.¶

A system that provides an incident merge function does not necessarily establish that all proposed merges are technically valid.¶

A source that reports a common correlation identifier does not necessarily prove a common physical root cause.¶

The disclosed alternatives therefore identify particular implementation pathways without asserting universal equivalence.¶

The central technical subject of Embodiment 1.B is the evidence-qualified¶

determination, revision, and historical reconstruction of network-incident membership across alarm deliveries, alarm occurrences, resource identities, and incomplete operational context.¶

This concludes Embodiment 1.B without repeating the complete Group B common workflow.¶

4.3. Embodiment 2.B: Evidence Dependency and Contradictory Diagnoses (Original lines 28402-31588)

EMBODIMENT 2.B - EVIDENCE-DEPENDENCY RECONSTRUCTION, CONTRADICTORY MULTIAGENT DIAGNOSIS, AND CONTROL-SPECIFIC HYPOTHESIS RECONCILIATION¶

2.B.1. Identification and Technical Scope¶

Invention Group: B - Multiagent Network Incident Diagnosis and Repair.¶

Embodiment: 2.B - Second Embodiment of Invention Group 2.¶

Technical Title: Systems and Methods for Reconciling Concurrent Network-Fault Diagnoses¶

Through Evidence-Lineage Analysis, Scoped Hypothesis Contradiction, and Discriminating Observation Selection.¶

Common Workflow: B-CW-01.¶

Principal Workflow Variation: B-V2 - Contradictory Multiagent Diagnoses and¶

Evidence-Based Hypothesis Reconciliation.¶

Embodiment Algorithm: B-ALG-03.¶

Specific Technical Alternatives: B-2-DA-01 through B-2-DA-28.¶

Relevant Common Workflow Stages: B-CW-01.07 through B-CW-01.20, B-CW-01.22¶

through B-CW-01.26, and B-CW-01.36.¶

Related Embodiment: 1.B - Incident Identity and Repeated-Alarm Correlation.¶

2.B.1.1. General Subject Matter¶

The present embodiment describes systems and methods for evaluating different diagnostic explanations generated by multiple software agents, network-analysis functions, monitoring systems, or autonomous diagnostic components investigating a network incident.¶

Each diagnostic component may use a different subset of available evidence, a different fault model, an independently maintained network-state representation, or a different inference method.¶

The components may produce identical, overlapping, complementary, mutually exclusive, or otherwise inconsistent diagnostic conclusions.¶

The disclosed procedure represents the technical propositions made by the components, identifies the underlying source evidence and its dependencies, evaluates the propositions under applicable network and temporal contexts, and determines whether the conclusions are supported, contradicted, compatible with one another, or unresolved.¶

The procedure may additionally identify observations or diagnostic tests capable of distinguishing competing hypotheses.¶

The system need not select one unique root cause where the evidence does not justify doing so.¶

It may preserve several hypotheses, including multiple simultaneous causes, hierarchical causes, or hypotheses that are observationally indistinguishable under available measurements.¶

The system may operate with conventional rule engines, model-based diagnostics, statistical inference systems, graph algorithms, language-model agents, or combinations thereof.¶

The technical requirements are defined by the evidence and diagnostic relationships, not by the brand or architecture of the participating agents.¶

2.B.1.2. Scope Relative to the Common Workflow¶

Common Workflow B-CW-01 already describes incident detection, evidence acquisition, hypothesis generation, repair planning, intervention tracking, and outcome verification.¶

Embodiment 2.B does not repeat those complete procedures.¶

Its distinct focus is the internal diagnostic reconciliation problem occurring after several potentially conflicting explanations have been proposed.¶

Embodiment 1.B supplies methods for distinguishing event delivery, alarm occurrence, and incident identity.¶

The present embodiment assumes that an incident candidate or sufficiently identified analysis scope is available, but does not require that the ultimate physical cause has already been determined.¶

Cascading-fault reconstruction and cross-domain propagation are treated more extensively in Embodiment 3.B.¶

Repair execution and protected effectuation remain separate functions.¶

2.B.2. Particular Technical Problems¶

2.B.2.1. Different Diagnostic Outputs May Describe Different Levels of One Fault¶

A network incident may produce a sequence of technical effects.¶

For example:¶

1. A configuration change causes a routing adjacency to become unstable. 2. Routing instability changes forwarding paths. 3. Traffic is concentrated on an alternate link. 4. Congestion increases. 5. Packet loss affects a dependent service.¶

An agent focused on routing may report routing instability.¶

An agent focused on interface counters may report congestion.¶

An application-monitoring agent may report a service-level violation.¶

These reports describe different portions of the same potential causal chain.¶

The reports do not necessarily contradict one another.¶

A system that selects one agent's explanation and discards the others may lose technically useful information.¶

The reconciliation system must therefore distinguish:¶

| x | initiating causes; | |---|---| | x | contributing causes; | | x | intermediate effects; | | x | immediate failure mechanisms; | | x | observed symptoms; and | | x | unrelated concurrent faults. |¶

A diagnostic label alone may not identify which of these roles the agent intended.¶

2.B.2.2. Apparently Opposite Claims May Refer to Different Operating Contexts¶

Agent A may report that interface R was operational.¶

Agent B may report that the same interface was unavailable.¶

Both statements may be accurate if they concern different event intervals, resource generations, interface layers, or state definitions.¶

Before classifying the statements as contradictory, the system must establish that their respective propositions apply to the same relevant context.¶

For example, administrative state up does not necessarily imply operational state up.¶

A routing adjacency may be unavailable while the underlying physical interface remains operational.¶

These observations should not be classified contradictory merely because their naturallanguage descriptions appear opposed.¶

2.B.2.3. Agent Agreement Can Be Produced by Shared Evidence¶

Several agents may independently execute different analytical procedures but rely upon the same underlying source record.¶

For example, an alarm-monitoring agent, a topology-analysis agent, and a language-model assistant may all rely upon one interface counter retrieved from a common telemetry database.¶

If each agent reports congestion, a naïve voting procedure may treat the reports as three independent confirmations.¶

This overstates the amount of independent evidence.¶

The present embodiment distinguishes:¶

(a) different deliveries of the same observation; (b) transformed or summarized versions of the same observation; (c) independent observations from the same physical source; (d) observations from distinct sources with a common upstream dependency; and (e) observations whose independence is sufficiently supported for a specified statistical calculation.¶

Source diversity and evidentiary independence are not automatically equivalent.¶

2.B.2.4. One Agent May Use Stale Topology While Another Uses Current Topology¶

An agent's diagnostic inference may depend upon the network topology known to it.¶

A route may have changed after the observations used by one agent were acquired.¶

Another agent may analyze later measurements under an updated topology.¶

The agents may reach different conclusions because their inputs refer to different operating states.¶

The reconciliation system must evaluate the applicable topology version or state interval before concluding that the diagnostic propositions conflict.¶

2.B.2.5. Missing Evidence May Be Mistaken for Contradiction¶

A fault hypothesis may predict that a corresponding alarm should occur.¶

The relevant alarm may be absent from the available repository.¶

However, the alarm source may have been disconnected, configured with an exclusion filter, subject to sampling limitations, or unable to detect that fault class.¶

The missing alarm does not necessarily contradict the hypothesis.¶

A negative-evidence inference must consider the source's detection and reporting guarantees.¶

The disclosed system separates missing evidence from established contradictory evidence.¶

2.B.2.6. A Diagnostic Agent May Produce an Unsupported Explanation¶

A language-model agent or conventional diagnostic program may infer an explanation that is plausible but not supported by the available observations.¶

For example, a report may assert that a routing policy was changed without identifying a configuration event or other sufficient evidence.¶

The system must not treat fluent reasoning, agent confidence, or agreement with earlier summaries as proof.¶

The proposed procedure associates diagnostic propositions with underlying evidence and explicitly identifies unsupported premises.¶

2.B.2.7. Two Different Faults May Coexist¶

A network resource may experience congestion and a separate optical impairment at the same time.¶

Both may contribute to packet loss.¶

A diagnostic model assuming exactly one root cause would force an incorrect exclusive choice.¶

The embodiment therefore permits hypotheses to coexist where their technical conditions do not exclude one another.¶

An exclusivity constraint is applied only where justified by the diagnostic model or observed network state.¶

2.B.2.8. Probabilistic Scores May Be Improperly Combined¶

Some agents may produce likelihoods, confidence scores, anomaly scores, or probabilities.¶

These values may have different meanings.¶

A classifier score of 0.9 is not necessarily a calibrated 90% probability that the proposed root cause is correct.¶

Multiplying scores from different agents may be mathematically invalid where they rely upon common evidence or incompatible assumptions.¶

The processor must determine the statistical meaning of a score before using it in combined inference.¶

Where that meaning is unavailable, the score may be retained as source metadata rather than treated as an objective probability.¶

2.B.2.9. Diagnostic Disagreement May Be Irresolvable from Existing Observations¶

Two fault hypotheses may predict the same observed network behavior under the available measurement set.¶

For example, some combinations of congestion, link-layer impairment, and route changes may produce similar packet-loss statistics.¶

The system may need a new diagnostic observation to distinguish the hypotheses.¶

A procedure that repeatedly reprocesses the same evidence may not provide new identifying information.¶

The proposed system may identify discriminating measurements or probes whose expected outcomes differ between the hypotheses.¶

2.B.2.10. Diagnostic Probes May Have Operational Effects¶

A probe may generate additional traffic, alter a route, restart a component, or change the conditions being diagnosed.¶

Such an operation is not equivalent to passive telemetry acquisition.¶

The processor must identify whether a proposed diagnostic procedure merely observes state or may change it.¶

A state-changing probe may require separate operational permission and must be interpreted in light of its effect on subsequent measurements.¶

2.B.2.11. New Evidence Can Invalidate an Earlier Consensus¶

Agents may initially agree upon one explanation.¶

A later configuration record, topology correction, or operational measurement may establish a contradiction.¶

The earlier conclusion may then require revision.¶

A system that overwrites the conclusion without preserving its evidentiary context may make previous repair decisions difficult to interpret.¶

The embodiment provides versioned hypothesis evaluations and dependency-aware reassessment.¶

2.B.3. Specific Technical Processing Relationship¶

The embodiment provides a diagnostic reconciliation system configured to perform an evidence-qualified comparison of agent-generated hypotheses.¶

For each hypothesis, the system determines:¶

1. The incident or fault scope. 2. The fault proposition being asserted. 3. The relevant resource instances. 4. The applicable observation or operational-state interval. 5. The network-state assumptions. 6. The expected symptoms or observable consequences. 7. The evidence identified by the originating agent. 8. The original source records supporting that evidence. 9. The transformations or intermediate analyses applied to those records. 10. The known evidence dependencies. 11. The evidence contradicting the hypothesis. 12. The evidence requirements that remain unresolved. 13. The applicable causal or diagnostic model. 14. The conditions under which the hypothesis can coexist with other hypotheses. 15. Any assumptions required to regard the hypothesis as exclusive. 16. The observations or probes capable of distinguishing it from alternatives.¶

The processor may then determine whether two hypotheses are:¶

x equivalent within the relevant model and context;¶

| x | compatible and potentially coexisting; | |---|---| | x | related as cause and consequence; | | x | compatible but not known to be causally related; | | x | contradictory under an established common context; | | x | observationally indistinguishable; or | | x | unresolved because the necessary correspondence information is unavailable. |¶

The relationship classification is separate from the ranking or confidence assigned to each hypothesis.¶

A hypothesis may be strongly supported while a competing hypothesis remains possible.¶

A hypothesis may also be contradicted under a specified diagnostic model without establishing the correctness of any particular alternative.¶

2.B.4. System Components¶

2.B.4.1. Agent Diagnosis Intake Interface¶

The intake interface receives diagnostic reports from participating agents.¶

A report may contain structured fault propositions, resource references, time intervals, source evidence, model identifiers, confidence information, or natural-language explanations.¶

Where the report is unstructured, an interpretation component may extract candidate propositions.¶

The extraction output is treated as a representation requiring validation, not as proof that the agent intended or established each proposition.¶

The system may preserve the original report for later comparison.¶

2.B.4.2. Diagnostic Proposition Normalizer¶

The normalizer maps agent reports into technical propositions having explicit scope.¶

A proposition identifies the relevant fault or state assertion, resource, time or state context, and applicable meaning.¶

For example:¶

Physical interface R is operational during interval I¶

and:¶

Routing adjacency associated with R is unavailable during interval I¶

are different propositions.¶

The system does not classify them contradictory solely because one uses the term operational and the other uses unavailable.¶

2.B.4.3. Evidence Lineage Resolver¶

The lineage resolver traces a diagnostic claim to the original source observations where possible.¶

It identifies intermediate transformations, copied reports, shared summaries, and other evidentiary dependencies.¶

The representation may be a directed acyclic graph for an acyclic derivation history, or another structure capable of representing the actual dependency relationships.¶

A source provenance record does not independently establish that the source observation is true.¶

2.B.4.4. Evidence Independence Evaluator¶

The independence evaluator determines whether evidence items may be treated as independent under the diagnostic model.¶

It may distinguish direct duplication, common-source dependence, shared upstream measurement dependence, and conditional dependence.¶

If the system lacks sufficient information to justify independence, it does not automatically multiply statistical likelihood contributions as though independence were established.¶

Where applicable, a joint likelihood model may account for the dependence.¶

2.B.4.5. Network-State Context Resolver¶

This component associates propositions and observations with the applicable resource and topology states.¶

It identifies relevant configuration versions, resource generations, event intervals, and operational-state definitions.¶

The component distinguishes changes in network state from disagreements about the same state.¶

2.B.4.6. Hypothesis Prediction Evaluator¶

The prediction evaluator identifies what observations would be expected if a hypothesis were correct under its specified model.¶

It compares those predictions with available measurements and operational records.¶

Predictions may be deterministic, bounded, or probabilistic.¶

The evaluator uses the corresponding technical interpretation.¶

2.B.4.7. Contradiction Evaluator¶

The contradiction evaluator determines whether evidence demonstrably violates a mandatory hypothesis condition.¶

It distinguishes:¶

(a) direct contradiction of a technical proposition; (b) inconsistency under a specified model; (c) disagreement arising from different resource or time contexts;¶

(d) missing expected evidence without sufficient reporting guarantees; and¶

(e) disagreement between different causal explanations that could coexist.¶

The evaluator may produce a contradiction witness identifying the precise proposition, observation, and applicable rule.¶

2.B.4.8. Hypothesis Coexistence Evaluator¶

The coexistence evaluator determines whether multiple hypotheses can be simultaneously valid.¶

It may use resource-state constraints, causal dependency models, temporal relationships, fault-class exclusions, or another supported method.¶

The evaluator does not assume a single-fault model unless that assumption is explicitly part of the diagnostic procedure.¶

2.B.4.9. Discriminating Observation Planner¶

The planner identifies additional observations capable of distinguishing unresolved hypotheses.¶

A candidate observation may be a configuration query, passive measurement, source event retrieval, topology comparison, or diagnostic probe.¶

The planner evaluates whether the candidate test has the required distinguishing power under the applicable hypotheses.¶

Where relevant, it considers data availability, timing, cost, uncertainty, and possible effects upon network operation.¶

2.B.4.10. Diagnostic Reconciliation Engine¶

The reconciliation engine combines the evidence evaluations, contradiction findings, coexistence relationships, and available discriminating observations.¶

It produces a qualified diagnostic result.¶

The result may retain several hypotheses rather than producing one forced diagnosis.¶

2.B.4.11. Hypothesis Revision Repository¶

The repository maintains the source and version relationships of diagnostic evaluations.¶

It supports reevaluation after new observations, source corrections, incident membership revisions, or topology changes.¶

Downstream repair plans may reference the diagnostic revision under which they were constructed.¶

2.B.4.12. Optional Agent and Tool Trust Interface¶

Where diagnostic reports originate from external tools, software agents, or text-containing network logs, the implementation may maintain source identity and trust metadata.¶

Untrusted source content is treated as diagnostic data rather than an instruction to override the reconciliation procedure.¶

This mechanism does not replace a separately required execution-authorization architecture.¶

2.B.5. Necessary Local Technical Interpretation¶

The Master Definitions in Section 8 remain applicable.¶

Only the following embodiment-specific explanations are added.¶

2.B.5.1. Scoped Diagnostic Proposition¶

A scoped diagnostic proposition is a technical statement about a fault, state, or causal relationship that identifies the resource, applicable time or state context, and interpretation needed to evaluate the statement.¶

The proposition may be true, false, or unresolved under the selected evidence model.¶

2.B.5.2. Evidence Lineage¶

Evidence lineage describes the derivation relationship between a diagnostic claim and the original observations or other records from which it was constructed.¶

It may identify direct observations, transformed measurements, copied summaries, and intermediate diagnostic outputs.¶

2.B.5.3. Contradiction Witness¶

A contradiction witness identifies the specific observation, proposition, context, and evaluation rule supporting a conclusion that a mandatory diagnostic condition has been violated.¶

It is not merely an agent's declaration that another agent is incorrect.¶

2.B.5.4. Hypothesis Coexistence¶

Hypothesis coexistence is the condition that two or more hypotheses can be simultaneously satisfied under the applicable diagnostic model and network context.¶

Coexistence does not establish that every hypothesis actually occurred.¶

2.B.5.5. Discriminating Observation¶

A discriminating observation is an observation or test result whose possible outcomes differ sufficiently among competing hypotheses to support the specified diagnostic distinction.¶

Its usefulness depends upon the expected observation behavior under those hypotheses.¶

2.B.5.6. Diagnostic Evaluation Revision¶

A diagnostic evaluation revision is a new assessment of a hypothesis or hypothesis set following a change in evidence, network-state interpretation, incident membership, or diagnostic model.¶

The revision may preserve earlier conclusions as historical records without representing them as currently established.¶

2.B.6. Representative Data Structures¶

2.B.6.1. Diagnostic Agent Report¶

DiagnosticAgentReport:¶

report\_id agent\_id agent\_generation incident\_id incident\_revision analysis\_time applicable\_network\_state\_reference proposed\_hypothesis\_ids source\_evidence\_references derived\_evidence\_references model\_or\_rule\_version confidence\_representation¶

declared\_assumptions original\_report\_reference¶

The system distinguishes analysis time from the occurrence time of the evidence.¶

2.B.6.2. Scoped Hypothesis Record¶

ScopedHypothesis:¶

hypothesis\_id incident\_id asserted\_fault\_condition causal\_role resource\_instance\_ids applicable\_time\_or\_state\_context diagnostic\_model\_reference required\_predictions optional\_predictions exclusivity\_conditions coexistence\_conditions supporting\_evidence\_ids contradicting\_evidence\_ids unresolved\_evidence\_requirements evaluation\_status evaluation\_revision¶

A causal role may indicate initiating cause, contributing cause, intermediate mechanism, or symptom where the applicable model supports that classification.¶

2.B.6.3. Evidence Derivation Record¶

EvidenceDerivation:¶

evidence\_id source\_record\_ids parent\_evidence\_ids derivation\_operation derivation\_version transformation\_assumptions source\_resource\_context source\_time\_context uncertainty\_information independence\_information¶

The record may identify one observation as a transformation of another.¶

2.B.6.4. Contradiction Record¶

DiagnosticContradiction:¶

contradiction\_id hypothesis\_id proposition\_id¶

evidence\_id required\_common\_context model\_rule\_reference expected\_condition established\_observation contradiction\_status uncertainty\_information¶

This record supports review and subsequent reevaluation.¶

2.B.6.5. Hypothesis Relationship Record¶

HypothesisRelationship:¶

relationship\_id first\_hypothesis\_id second\_hypothesis\_id relationship\_type applicable\_network\_context supporting\_evidence conflicting\_evidence unresolved\_conditions model\_version¶

Relationships may include equivalent, coexisting, causally related, contradictory, and observationally indistinguishable classifications.¶

2.B.6.6. Diagnostic Test Plan¶

DiagnosticTestPlan:¶

test\_plan\_id incident\_id target\_hypotheses proposed\_observation\_or\_probe predicted\_outcomes\_by\_hypothesis necessary\_resource\_state measurement\_requirements expected\_information\_value estimated\_operational\_cost possible\_network\_side\_effects required\_permissions\_if\_state\_changing execution\_status¶

The information-value estimate is optional and meaningful only under an adequately specified decision or statistical model.¶

2.B.6.7. Reconciliation Result¶

DiagnosticReconciliationResult:¶

incident\_id incident\_revision¶

evaluation\_revision evaluated\_hypotheses supported\_hypotheses contradicted\_hypotheses coexisting\_hypotheses indistinguishable\_hypotheses unresolved\_hypotheses common\_evidence\_dependencies contradiction\_witnesses additional\_evidence\_requirements diagnostic\_model\_versions result\_status¶

The result may contain multiple hypotheses.¶

A unique diagnosis is not required in every case.¶

2.B.7. Detailed Operational Sequence¶

The following 32 steps supplement B-CW-01 and concentrate on diagnostic reconciliation.¶

2.B-01 - Receive Competing Diagnostic Reports¶

The processor receives reports from two or more diagnostic components.¶

It identifies the incident and analysis context associated with each report.¶

The reports need not arrive simultaneously.¶

Output: Agent report set.¶

2.B-02 - Establish Incident Revision Correspondence¶

The processor determines whether the reports refer to the same incident membership revision or to compatible revisions.¶

Where one report uses a superseded incident grouping, the processor identifies any material membership changes.¶

Output: Incident-context correspondence.¶

2.B-03 - Extract Diagnostic Propositions¶

The processor obtains the technical statements asserted by the agents.¶

Where reports contain natural language, the processor may extract structured candidate propositions.¶

Any ambiguous extraction remains subject to validation.¶

Output: Structured propositions.¶

2.B-04 - Normalize Resource and State References¶

The processor resolves source-specific resource references, administrative and operational state distinctions, and applicable network-state versions.¶

Two descriptions referring to different resource instances are not automatically comparable.¶

Output: Resource-scoped propositions.¶

2.B-05 - Establish Temporal Applicability¶

The processor determines the event intervals or state versions to which each proposition applies.¶

It distinguishes an assertion concerning an earlier incident state from an assertion concerning the current network state.¶

Output: Time- and state-scoped propositions.¶

2.B-06 - Identify the Claimed Causal Role¶

Where supported, the processor identifies whether a proposition describes an initiating fault, contributing condition, intermediate mechanism, or observed symptom.¶

A statement about a symptom is not treated as an alternative root-cause diagnosis without further evidence.¶

Output: Causal-role classification.¶

2.B-07 - Retrieve Source Evidence¶

The processor obtains the observations referenced by each agent.¶

It distinguishes direct source observations from intermediate summaries.¶

Where the original observations are unavailable, the corresponding evidentiary limitation is retained.¶

Output: Source evidence collection.¶

2.B-08 - Reconstruct Evidence Derivation Paths¶

The processor traces how each derived result was obtained.¶

A result may depend upon a transformed counter, summarized log, topology join, or another agent's report.¶

The system records the relevant derivation relationships.¶

Output: Evidence-lineage representation.¶

2.B-09 - Identify Shared Evidence Ancestors¶

The processor determines which diagnostic reports depend upon the same underlying observations.¶

It distinguishes repeated delivery from genuinely separate measurement acquisitions.¶

Output: Common evidence-dependency information.¶

2.B-10 - Evaluate Independence Assumptions¶

Where a combined statistical calculation requires independent evidence, the processor determines whether that assumption is supported.¶

If independence is unknown, the system uses an appropriate dependent-evidence model or preserves the limitation.¶

It does not multiply likelihoods under an unsupported independence assumption.¶

Output: Evidence-dependence classification.¶

2.B-11 - Resolve Applicable Source Semantics¶

The system verifies the interpretation of evidence values.¶

For example, interface utilization, error counters, route-state reports, and service latency may require different measurement contexts.¶

A diagnostic claim based upon a semantically invalid transformation is not automatically accepted.¶

Output: Source-semantic assessment.¶

2.B-12 - Evaluate Evidence Freshness and Historical Applicability¶

The processor determines whether each observation is applicable to the state being diagnosed.¶

A later measurement may support an earlier causal hypothesis only where the required historical or causal relationship is established.¶

Output: Evidence-state correspondence.¶

2.B-13 - Retrieve Hypothesis Prediction Rules¶

For each hypothesis, the processor identifies the observations expected under the applicable diagnostic model.¶

It separates mandatory predictions from optional or probabilistic tendencies.¶

Output: Hypothesis test requirements.¶

2.B-14 - Evaluate Supporting Evidence¶

The processor determines whether available observations satisfy the hypothesis's required positive conditions.¶

It records the specific supporting evidence.¶

An agent's conclusion is not counted as an additional independent observation merely because the conclusion agrees with the hypothesis.¶

Output: Support assessment.¶

2.B-15 - Evaluate Potential Contradictions¶

The processor compares the hypothesis's mandatory predictions with available observations.¶

It checks whether the observations apply to the required resource and operating context.¶

A discrepancy is not classified as a contradiction solely because the reports use opposing terminology.¶

Output: Candidate contradiction findings.¶

2.B-16 - Establish Contradiction Witnesses¶

Where a mandatory contradiction is demonstrable, the processor records the relevant proposition, evidence, model rule, and common context.¶

Where any required context remains unresolved, the contradiction may remain unresolved.¶

Output: Established or qualified contradiction records.¶

2.B-17 - Evaluate Missing Expected Evidence¶

The processor determines whether an expected but absent observation can be used as negative evidence.¶

It evaluates source detection, filtering, reporting, retention, and coverage guarantees where relevant.¶

Output: Missing-evidence assessment.¶

2.B-18 - Evaluate Hypothesis Status¶

The processor combines the relevant support, contradiction, and uncertainty findings.¶

A hypothesis may be consistent with the required evidence, contradicted under the specified model, or unresolved.¶

Consistency does not automatically prove unique causation.¶

Output: Individual hypothesis evaluation.¶

2.B-19 - Compare Hypothesis Contexts¶

The processor determines whether two hypotheses refer to the same resource, event interval, state version, and technical meaning.¶

Where they concern different contexts, apparent opposition is not automatically classified as contradiction.¶

Output: Context-comparison result.¶

2.B-20 - Evaluate Mutual Exclusivity¶

The processor identifies whether the hypotheses are mutually exclusive under a justified diagnostic rule.¶

It does not impose a single-root-cause assumption without support.¶

Output: Exclusivity evaluation.¶

2.B-21 - Evaluate Possible Coexistence¶

Where both hypotheses can be satisfied, the processor preserves their possible coexistence.¶

They may represent independent simultaneous faults or different stages of one causal chain.¶

Output: Coexistence relationship.¶

2.B-22 - Evaluate Causal-Chain Compatibility¶

The processor determines whether one explanation may be an upstream cause and the other a downstream effect.¶

It uses an applicable causal or fault-propagation model.¶

Temporal order alone is not sufficient to prove the causal relationship.¶

Output: Causal-relationship assessment.¶

2.B-23 - Identify Observational Equivalence¶

The processor determines whether competing hypotheses predict the same available observations.¶

Where the hypotheses cannot be distinguished under the current measurement set, the processor records that limitation.¶

Output: Indistinguishable hypothesis set.¶

2.B-24 - Construct Discriminating Observation Candidates¶

For unresolved hypotheses, the processor identifies observations whose possible outcomes differ under the competing models.¶

It may select a configuration query, topology comparison, passive measurement, or diagnostic probe.¶

Output: Discriminating test candidates.¶

2.B-25 - Evaluate Test Feasibility and Effects¶

The processor determines whether the required observation can be obtained.¶

It identifies possible network-state changes, resource costs, and measurement uncertainty.¶

A state-changing diagnostic procedure is not classified as ordinary passive evidence acquisition.¶

Output: Test feasibility assessment.¶

2.B-26 - Select an Appropriate Diagnostic Test¶

Where multiple tests are available, the processor selects a test according to the specified diagnostic objective and technical requirements.¶

It may use deterministic discrimination, expected information gain, risk bounds, or another supported criterion.¶

The selected procedure must preserve applicable operational limitations.¶

Output: Selected test or unresolved test-selection result.¶

2.B-27 - Obtain Additional Evidence¶

The system obtains new evidence through an applicable mechanism.¶

For a state-changing test, the effect of the test on the network is recorded and considered when interpreting its observations.¶

Output: Newly acquired evidence.¶

2.B-28 - Reevaluate the Affected Hypotheses¶

The processor updates the relevant evidence and hypothesis relationships.¶

It may avoid reevaluating unrelated hypotheses where their dependencies remain unchanged and the dependency representation is adequate.¶

Output: Revised hypothesis evaluations.¶

2.B-29 - Produce the Diagnostic Reconciliation Result¶

The processor provides the supported, contradicted, coexisting, and unresolved hypotheses.¶

Where the evidence supports a unique conclusion under the specified model, the result may identify it.¶

Otherwise, the system retains a qualified hypothesis set.¶

Output: Reconciled diagnostic result.¶

2.B-30 - Associate Repair Planning with the Diagnostic Revision¶

A repair planner may obtain the current diagnostic result.¶

Each proposed plan may identify the hypothesis or combination of hypotheses it addresses.¶

A plan requiring one unique root cause is not treated as applicable merely because that hypothesis has the highest score.¶

Output: Diagnostic-to-repair relationship.¶

2.B-31 - Detect Material Diagnostic Changes¶

When new source evidence, incident-membership revisions, or resource-state changes occur, the processor determines whether earlier diagnostic evaluations remain applicable.¶

It identifies affected hypotheses and related repair plans.¶

Output: Diagnostic dependency-change assessment.¶

2.B-32 - Preserve the Reconciliation History¶

The processor retains the evaluated hypotheses, evidence references, contradiction witnesses, model versions, and revision relationships needed for reconstruction.¶

The record remains separate from any protected authority or physical-effectuation ledger.¶

Output: Reconstructible diagnostic history.¶

2.B.8. Technical Invariants¶

B2-I01 - Agent output is not source evidence by default. An agent's conclusion shall not¶

automatically be counted as an independent observation supporting that conclusion.¶

B2-I02 - Agreement is not independent corroboration. Multiple agents relying upon the¶

same record do not automatically provide multiple independent measurements.¶

B2-I03 - Provenance does not prove accuracy. A complete evidence lineage can identify¶

the origin of incorrect or stale information without making the information correct.¶

B2-I04 - Hypothesis scope must be established before contradiction. Claims about¶

different resources, states, or times shall not automatically be treated as mutually contradictory.¶

B2-I05 - Different causal levels may coexist. An initiating fault and a resulting symptom¶

are not necessarily competing root causes.¶

B2-I06 - A single-fault assumption is not universal. Multiple independent or interacting¶

faults may coexist.¶

B2-I07 - Correlation is not causal proof. Observational association alone does not¶

generally establish an initiating cause.¶

B2-I08 - Temporal precedence is not sufficient for causation. Earlier occurrence does¶

not automatically imply causal responsibility.¶

B2-I09 - Contradiction requires an applicable model. A violated prediction is¶

meaningful only under the specified prediction and measurement assumptions.¶

B2-I10 - Missing evidence is not automatically negative evidence. Absence is interpreted¶

through the source's applicable detection and reporting guarantees.¶

B2-I11 - Shared evidence cannot be multiplied as independent support. Statistical¶

aggregation shall respect the available evidence-dependence information.¶

B2-I12 - Confidence scores are not automatically probabilities. A model score requires¶

an appropriate interpretation before probabilistic combination.¶

B2-I13 - Probabilities require model assumptions. Posterior calculations depend upon¶

priors, likelihoods, and dependence relationships.¶

B2-I14 - Compatible does not mean uniquely proven. A hypothesis may fit the¶

observations while several other explanations remain possible.¶

B2-I15 - A contradiction under one model is not universal disproof. The conclusion¶

remains associated with the applicable model and assumptions.¶

B2-I16 - Observational equivalence may be irreducible without new evidence.¶

Repeated reasoning over unchanged data cannot guarantee identification of a unique cause.¶

B2-I17 - Discriminating tests require predicted outcome differences. A test need not be¶

selected where its results would not materially distinguish the relevant hypotheses.¶

B2-I18 - Diagnostic probe effects must be accounted for. A state-changing probe cannot¶

automatically be interpreted as observing an untouched network state.¶

B2-I19 - Source quality and agent reliability are distinct. A reliable reporting component¶

may faithfully transmit an incorrect upstream measurement.¶

B2-I20 - Contradiction witnesses must be reconstructible. Where a contradiction is¶

asserted, the applicable proposition, evidence, and rule shall be identifiable.¶

B2-I21 - Changed network state may change the diagnostic question. Later observations¶

shall not be applied to an earlier context without adequate correspondence.¶

B2-I22 - Different resource-state definitions are not interchangeable. Administrative¶

state, operational state, routing state, and service health may represent distinct properties.¶

B2-I23 - Material new evidence may revise earlier consensus. An earlier agreement need¶

not remain current after a supporting premise is contradicted.¶

B2-I24 - Diagnostic revision history shall remain interpretable. Earlier downstream¶

decisions must remain associable with the diagnostic version they used where required for reconstruction.¶

B2-I25 - An unresolved hypothesis is not necessarily false. Insufficient evidence shall not¶

automatically eliminate a candidate explanation.¶

B2-I26 - Diagnostic ranking is not repair authorization. A preferred hypothesis does not¶

independently authorize an external network change.¶

B2-I27 - A computational deadline is not an infeasibility proof. Exhaustion of a¶

diagnostic search budget shall not automatically establish that no compatible explanation exists.¶

B2-I28 - Existing effectuation controls remain separate. Diagnosis reconciliation does¶

not replace any previously disclosed protected authority, state-consumption, or Finality Sink mechanism.¶

2.B.9. Embodiment-Specific Mathematical Relationships¶

The Group B mathematical specification provides the shared equations, symbol definitions, and assumptions.¶

The following relations identify the mathematical mechanisms particularly relevant to Embodiment 2.B.¶

These formulas are illustrative rather than mandatory to every implementation.¶

2.B.9.1. Scoped Diagnostic Proposition¶

Represent a hypothesis proposition as:¶

[ h=(f,r,I,s,m,a) ]¶

where:¶

| x | (f) identifies the asserted fault or state condition; | |---|---| | x | (r) identifies the applicable resource or resource set; | | x | (I) identifies the relevant time interval or event context; | | x | (s) identifies the applicable network-state context; | | x | (m) identifies the diagnostic model; | | x | (a) identifies the necessary assumptions. |¶

Two propositions are not automatically comparable merely because their fault labels appear opposite.¶

Their scope and semantics must first be reconciled.¶

2.B.9.2. Evidence Lineage¶

Let:¶

[ G\_E=(V\_E,E\_E) ]¶

represent an evidence derivation graph.¶

Each vertex identifies an observation, transformation, or derived diagnostic record.¶

An edge:¶

[ e\_i\\rightarrow e\_j ]¶

indicates that record (e\_j) depends upon record (e\_i).¶

Where a record has several source ancestors, the relationship may be represented by several incoming edges or an equivalent multi-input derivation record.¶

A shared ancestor identifies a known dependency.¶

It does not by itself quantify the complete statistical dependence.¶

2.B.9.3. Shared Evidence Ancestors¶

Let (A(e)) denote the set of source observation ancestors of evidence record (e).¶

Two records have an identified shared source dependency where:¶

[ A(e\_i)\\cap A(e\_j)\\neq\\varnothing. ]¶

This establishes a relevant derivation relationship.¶

It does not prove that records with disjoint recorded ancestors are statistically independent, because unrecorded common causes may exist.¶

2.B.9.4. Hypothesis Prediction Compatibility¶

Let (P\_h) represent the mandatory predictions of hypothesis (h).¶

For prediction (p), define:¶

[ V(p,E)\\in{T,F,U}. ]¶

Here:¶

| x | (T): the required prediction is established as satisfied under the applicable evidence and model; | |---|---| | x | (F): the prediction is demonstrably violated; | | x | (U): the available information is insufficient. |¶

A deterministic hypothesis compatibility evaluation may be represented as:¶

[ C(h,E)=\\bigwedge\_{p\\in P\_h}V(p,E). ]¶

A result of (T) establishes consistency with the required tests, not necessarily unique causal truth.¶

A probabilistic hypothesis requires the corresponding statistical decision procedure instead of blindly applying deterministic rules.¶

2.B.9.5. Contextual Contradiction¶

Let (\\Gamma(h\_i,h\_j)) represent the conditions required for comparing two hypotheses, including common resource identity, temporal applicability, state interpretation, and any justified exclusivity constraints.¶

A contradiction may be established where:¶

[ \\Gamma(h\_i,h\_j)=T ]¶

and the propositions cannot both satisfy the applicable model.¶

Where the common context remains unknown, the processor shall not infer contradiction solely from opposing words.¶

2.B.9.6. Multi-Fault Hypothesis¶

Let (H={h\_1,h\_2,\\ldots,h\_k}) be a proposed set of coexisting causes.¶

A multi-fault evaluation must determine whether the joint model permits those conditions and explains the required observations.¶

Individual compatibility of every (h\_i) is not sufficient where the joint model contains additional resource, causal, or exclusivity constraints.¶

2.B.9.7. Probabilistic Evidence Combination¶

For a hypothesis (H) and evidence (E), Bayes' rule is:¶

\\frac{P(E\\mid H)P(H)} {P(E)}. ]¶

For two observations (E\_1,E\_2), the factorization¶

P(E\_1\\mid H)P(E\_2\\mid H) ]¶

is valid only under conditional independence given (H).¶

If the reports are duplicates of the same source observation, treating them as conditionally independent generally overcounts the evidence.¶

A correct implementation uses the actual joint model or treats a duplicate as one observation.¶

2.B.9.8. Discriminating Test Information¶

Where a calibrated probabilistic model is available, a candidate test (T) may be evaluated by expected information gain:¶

\mathcal{H}(H\mid E)¶

\\mathbb{E}\_{t} \\left[ \\mathcal{H}(H\\mid E,T=t)¶

\\right]. ]¶

Here (\\mathcal{H}) is Shannon entropy over the defined hypothesis distribution, and (t) ranges over possible test outcomes.¶

The expectation requires a valid predictive distribution.¶

A higher information-gain estimate does not automatically justify a test that creates unacceptable operational effects.¶

2.B.9.9. Diagnostic Revision¶

A revised hypothesis evaluation may be represented as:¶

\\operatorname{Evaluate} \\left( H,E\\cup\\Delta E,S\_{v+1},M\_{v+1} \\right), ]¶

where:¶

| x | (H) is the hypothesis set; | |---|---| | x | (E) is the previously available evidence; | | x | (\\Delta E) is additional or corrected evidence; | | x | (S_{v+1}) is the applicable network-state context; | | x | (M_{v+1}) is the applicable diagnostic model. |¶

The revision need not be monotonic.¶

New information may support, weaken, or contradict an earlier conclusion.¶

2.B.10. Implementable Pseudocode - B-ALG-03¶

2.B.10.1. Basic Types¶

ENUM Verdict:¶

COMPATIBLE INCOMPATIBLE UNRESOLVED¶

ENUM HypothesisRelation:¶

EQUIVALENT COEXISTING CAUSE\_AND\_CONSEQUENCE MUTUALLY\_EXCLUSIVE OBSERVATIONALLY\_INDISTINGUISHABLE¶

UNRESOLVED\_RELATION¶

TYPE Proposition:¶

proposition\_id fault\_or\_state\_assertion resource\_instances time\_or\_event\_context network\_state\_reference diagnostic\_model causal\_role assumptions¶

TYPE Evidence:¶

evidence\_id source\_record\_id parent\_evidence\_ids resource\_context observation\_context measurement\_semantics uncertainty reporting\_contract¶

TYPE Hypothesis:¶

hypothesis\_id proposition\_set mandatory\_predictions optional\_predictions exclusivity\_conditions source\_evidence\_ids model\_version assumptions¶

TYPE Reconciliation:¶

incident\_id supported\_hypotheses contradicted\_hypotheses unresolved\_hypotheses coexistence\_relationships contradiction\_witnesses common\_evidence\_ancestors proposed\_diagnostic\_tests evaluation\_revision¶

2.B.10.2. Normalize Agent Propositions¶

FUNCTION NORMALIZE\_AGENT\_DIAGNOSIS(¶

agent\_report, incident\_context, resource\_registry, model\_registry¶

):¶

propositions = EXTRACT\_CANDIDATE\_PROPOSITIONS(¶

agent\_report )¶

normalized = []¶

FOR EACH proposition IN propositions:¶

resource = RESOLVE\_RESOURCE\_SCOPE(¶

proposition, resource\_registry )¶

state = RESOLVE\_APPLICABLE\_STATE\_CONTEXT(¶

proposition, incident\_context )¶

model = model\_registry.RESOLVE(¶

proposition.model\_reference )¶

IF resource unresolved¶

OR state unresolved OR model required but unknown:¶

ADD UNRESOLVED\_PROPOSITION(¶

proposition¶

) TO normalized¶

CONTINUE¶

ADD BUILD\_SCOPED\_PROPOSITION(¶

proposition, resource, state, model ) TO normalized¶

RETURN normalized¶

The normalization stage does not assign causal truth to a proposition.¶

2.B.10.3. Reconstruct Evidence Lineage¶

FUNCTION RECONSTRUCT\_EVIDENCE\_LINEAGE(¶

hypothesis, evidence\_repository¶

):¶

visited = EMPTY\_SET() roots = EMPTY\_SET() unresolved = EMPTY\_SET()¶

PROCEDURE WALK(evidence\_id):¶

IF evidence\_id IN visited:¶

RETURN¶

ADD evidence\_id TO visited¶

record = evidence\_repository.GET(evidence\_id)¶

IF record unavailable:¶

ADD evidence\_id TO unresolved RETURN¶

IF record is an original source observation:¶

ADD record.source\_identity TO roots RETURN¶

IF record.parent\_evidence\_ids empty¶

AND original source status unestablished:¶

ADD evidence\_id TO unresolved RETURN¶

FOR EACH parent IN record.parent\_evidence\_ids:¶

WALK(parent)¶

FOR EACH evidence\_id¶

IN hypothesis.source\_evidence\_ids:¶

WALK(evidence\_id)¶

RETURN {¶

source\_ancestors: roots, visited\_records: visited, missing\_dependencies: unresolved }¶

A production implementation must handle malformed derivation cycles and conflicting record identifiers.¶

A cycle may be rejected or separately represented, but it cannot be silently treated as independent corroboration.¶

2.B.10.4. Evaluate Evidence Independence¶

FUNCTION EVALUATE\_EVIDENCE\_DEPENDENCE(¶

evidence\_A, evidence\_B, lineage\_repository, statistical\_model ):¶

ancestors\_A = GET\_SOURCE\_ANCESTORS(¶

evidence\_A, lineage\_repository )¶

ancestors\_B = GET\_SOURCE\_ANCESTORS(¶

evidence\_B, lineage\_repository )¶

IF source ancestry incomplete:¶

RETURN UNRESOLVED(¶

"EVIDENCE\_ANCESTRY\_INCOMPLETE" )¶

IF INTERSECTION(ancestors\_A, ancestors\_B) is not empty:¶

RETURN {¶

status: DEPENDENT\_OR\_SHARED\_SOURCE, common\_ancestors:¶

INTERSECTION(ancestors\_A, ancestors\_B) }¶

IF statistical\_model establishes¶

applicable conditional independence:¶

RETURN CONDITIONALLY\_INDEPENDENT\_UNDER\_MODEL¶

RETURN UNRESOLVED(¶

"INDEPENDENCE\_NOT\_ESTABLISHED" )¶

Disjoint recorded source ancestors are not, by themselves, a statistical independence proof.¶

2.B.10.5. Evaluate an Individual Hypothesis¶

FUNCTION EVALUATE\_HYPOTHESIS(¶

hypothesis, evidence\_repository, network\_context ):¶

predictions = LOAD\_REQUIRED\_PREDICTIONS(¶

hypothesis )¶

verdicts = [] witnesses = []¶

FOR EACH prediction IN predictions:¶

evidence = FIND\_APPLICABLE\_EVIDENCE(¶

prediction, evidence\_repository, network\_context )¶

IF evidence unavailable:¶

coverage = CHECK\_SOURCE\_DETECTION\_AND\_REPORTING(¶

prediction, network\_context )¶

IF coverage does not support¶

a negative-evidence inference:¶

ADD UNRESOLVED TO verdicts CONTINUE¶

verdict = EVALUATE\_PREDICTION(¶

prediction, evidence, hypothesis.model\_version )¶

ADD verdict TO verdicts¶

IF verdict == INCOMPATIBLE:¶

ADD BUILD\_CONTRADICTION\_WITNESS(¶

hypothesis, prediction, evidence ) TO witnesses¶

IF any verdict == INCOMPATIBLE:¶

RETURN {¶

status: INCOMPATIBLE, contradiction\_witnesses: witnesses }¶

IF any verdict == UNRESOLVED:¶

RETURN {¶

status: UNRESOLVED, unresolved\_requirements:¶

FIND\_UNRESOLVED(predictions) }¶

RETURN {¶

status: COMPATIBLE, interpretation:¶

"CONSISTENT\_WITH\_REQUIRED\_PREDICTIONS" }¶

The compatible result does not assert that no alternative hypothesis could explain the same observations.¶

2.B.10.6. Evaluate Hypothesis-to-Hypothesis Relationships¶

FUNCTION COMPARE\_HYPOTHESES(¶

hypothesis\_A, hypothesis\_B, network\_context, causal\_model ):¶

scope = COMPARE\_PROPOSITION\_SCOPES(¶

hypothesis\_A, hypothesis\_B, network\_context )¶

IF scope unresolved:¶

RETURN UNRESOLVED\_RELATION¶

IF hypotheses express equivalent conditions¶

under a supported semantic mapping: RETURN EQUIVALENT¶

IF causal\_model establishes a permitted¶

cause-and-consequence relationship: RETURN CAUSE\_AND\_CONSEQUENCE¶

IF established exclusivity rules prove¶

simultaneous satisfaction impossible: RETURN MUTUALLY\_EXCLUSIVE¶

IF joint conditions can be satisfied¶

under the defined model:¶

IF available observations cannot distinguish¶

the hypotheses: RETURN OBSERVATIONALLY\_INDISTINGUISHABLE¶

RETURN COEXISTING¶

RETURN UNRESOLVED\_RELATION¶

A coexistence conclusion means that both explanations are permitted by the model, not that both occurred.¶

2.B.10.7. Select Discriminating Observations¶

FUNCTION PLAN\_DISCRIMINATING\_TESTS(¶

unresolved\_hypotheses, diagnostic\_test\_catalog, network\_state, cost\_and\_risk\_policy ):¶

candidate\_tests = []¶

FOR EACH test IN diagnostic\_test\_catalog:¶

expected\_outcomes =¶

PREDICT\_TEST\_OUTCOMES(¶

test, unresolved\_hypotheses, network\_state )¶

IF expected\_outcomes insufficient¶

to discriminate relevant hypotheses: CONTINUE¶

feasibility = CHECK\_TEST\_FEASIBILITY(¶

test, network\_state )¶

IF feasibility not established:¶

CONTINUE operational\_effect = CLASSIFY\_TEST\_EFFECT(¶

test )¶

IF operational\_effect is state-changing¶

AND required separate permission¶

is not established:¶

MARK\_TEST\_UNAVAILABLE\_FOR\_EXECUTION(test) CONTINUE score = EVALUATE\_TEST\_UTILITY(¶

test, expected\_outcomes, cost\_and\_risk\_policy )¶

ADD {¶

test, score, expected\_outcomes, operational\_effect } TO candidate\_tests¶

IF candidate\_tests empty:¶

RETURN UNRESOLVED(¶

"NO\_SUPPORTED\_DISCRIMINATING\_TEST" )¶

RETURN SELECT\_BY\_DEFINED\_POLICY(candidate\_tests)¶

The planning stage does not itself authorize execution of a state-changing diagnostic operation.¶

2.B.10.8. Reconcile the Complete Hypothesis Set¶

FUNCTION RECONCILE\_DIAGNOSES(¶

incident, agent\_reports, evidence\_repository, model\_registry, processing\_budget ):¶

hypotheses = NORMALIZE\_ALL\_REPORTS(¶

incident, agent\_reports )¶

evaluations = []¶

FOR EACH hypothesis IN hypotheses:¶

IF processing\_budget exhausted:¶

RETURN UNRESOLVED(¶

"DIAGNOSTIC\_EVALUATION\_INCOMPLETE" )¶

lineage = RECONSTRUCT\_EVIDENCE\_LINEAGE(¶

hypothesis, evidence\_repository )¶

status = EVALUATE\_HYPOTHESIS(¶

hypothesis, evidence\_repository, incident.network\_context )¶

ADD {¶

hypothesis, lineage, status } TO evaluations¶

relationships = []¶

FOR EACH relevant pair of hypotheses:¶

relation = COMPARE\_HYPOTHESES(¶

pair.first, pair.second, incident.network\_context, model\_registry )¶

ADD relation TO relationships unresolved = FIND\_UNRESOLVED\_HYPOTHESES(¶

evaluations, relationships )¶

tests = PLAN\_DISCRIMINATING\_TESTS(¶

unresolved, incident.available\_test\_catalog, incident.network\_state, incident.test\_policy )¶

RETURN BUILD\_VERSIONED\_RECONCILIATION\_RESULT(¶

evaluations, relationships, tests )¶

2.B.10.9. Dependency-Based Revision¶

FUNCTION REVISE\_DIAGNOSTIC\_EVALUATION(¶

changed\_evidence\_or\_state, dependency\_index, reconciliation\_repository ):¶

affected\_hypotheses =¶

dependency\_index.FIND\_HYPOTHESES(¶

changed\_evidence\_or\_state )¶

IF dependency coverage incomplete:¶

affected\_hypotheses =¶

FIND\_BROADER\_REEVALUATION\_SCOPE()¶

FOR EACH hypothesis IN affected\_hypotheses:¶

revised = EVALUATE\_HYPOTHESIS(¶

hypothesis, CURRENT\_APPLICABLE\_EVIDENCE(), CURRENT\_RELEVANT\_NETWORK\_CONTEXT() )¶

STORE\_EVALUATION\_REVISION(¶

hypothesis, revised )¶

affected\_plans =¶

FIND\_REPAIR\_PLANS\_DEPENDING\_ON(¶

affected\_hypotheses )¶

MARK\_FOR\_APPLICABILITY\_REEVALUATION(¶

affected\_plans )¶

RETURN¶

This does not automatically cancel, execute, or reverse any prior remediation.¶

2.B.11. Worked Technical Examples¶

2.B.11.1. Example 1 - Different Causal Levels Are Not Necessarily Contradictory¶

A packet network experiences increased packet loss.¶

Agent A reports:¶

The affected interface is congested.¶

Agent B reports:¶

A routing policy change caused traffic redistribution.¶

Agent C reports:¶

Packet loss increased after route convergence.¶

Suppose the source history establishes:¶

Time Observation¶

10:00:00 Routing configuration change 10:00:04 Selected route changes 10:00:06 Traffic moves onto alternate path 10:00:08 Alternate-link utilization increases 10:00:10 Packet loss increases¶

The reports are potentially compatible.¶

Agent B's diagnosis may concern an upstream cause.¶

Agent A's diagnosis may concern an intermediate congestion condition.¶

Agent C's report may concern an observed effect.¶

The event order is consistent with a causal chain but does not alone establish that the configuration change caused every subsequent condition.¶

The system records the potential relationships and seeks the required supporting evidence.¶

It does not force the three reports into a single mutually exclusive vote.¶

2.B.11.2. Example 2 - Apparent Contradiction Caused by Different State Meanings¶

Agent A states:¶

Interface R is up.¶

Agent B states:¶

The routing adjacency over interface R is down.¶

A superficial text comparison may classify these as contradictory.¶

However, Agent A may refer to the physical or operational interface state.¶

Agent B may refer to the routing protocol adjacency state.¶

Both conditions can coexist.¶

The proposition normalizer identifies the respective technical state dimensions.¶

The processor does not declare contradiction merely because the reports contain opposite words.¶

2.B.11.3. Example 3 - Genuine Contradiction Under a Common Context¶

Agent A claims:¶

The operational state of resource instance R was up throughout interval I.¶

Agent B claims:¶

The operational state of the same resource instance R was down throughout interval I.¶

The resource identity, state definition, and interval have been established as identical.¶

Assume the operational-state model does not permit both states simultaneously.¶

If the supporting source evidence establishes one state under the applicable coverage and state semantics, the opposing proposition may be contradicted.¶

The contradiction witness identifies the common resource, interval, state definition, evidence, and exclusivity rule.¶

If the source reports are themselves contradictory and their correctness cannot be established, the system may instead retain unresolved state rather than declaring one agent correct arbitrarily.¶

2.B.11.4. Example 4 - Three Agents Relying on One Source Observation¶

Three agents report congestion.¶

Agent A directly reads utilization record U-501.¶

Agent B reads a dashboard derived from U-501.¶

Agent C reads Agent B's summary.¶

The evidence graph is:¶

U-501¶

| +--> Agent A diagnosis | +--> Dashboard summary¶

| +--> Agent B diagnosis¶

| +--> Agent C diagnosis¶

The system identifies one underlying observation lineage.¶

It does not treat the reports as three independent utilization measurements.¶

Additional models or transformations may contribute analysis, but the evidence ancestry remains explicit.¶

2.B.11.5. Example 5 - Numerical Demonstration of Duplicate Evidence¶

Consider an illustrative binary hypothesis (H) that a particular fault is present.¶

Assume:¶

[ P(H)=0.2. ]¶

A positive diagnostic test has:¶

[ P(E\_1\\mid H)=0.8 ]¶

and:¶

[ P(E\_1\\mid \\neg H)=0.1. ]¶

After one positive test:¶

\\frac{0.16}{0.24}¶

0.6667. ]¶

Agent B then republishes the same result (E\_1) as a derived summary.¶

That summary is not a second independent test.¶

The posterior based upon the available physical evidence remains approximately 0.6667 under the stated model.¶

If a genuinely separate test (E\_2) has:¶

[ P(E\_2\\mid H)=0.75 ]¶

and:¶

[ P(E\_2\\mid\\neg H)=0.15, ]¶

and the two tests are conditionally independent under both hypotheses, then:¶

\\frac{0.12}{0.132} \\approx0.9091. ]¶

The increase is justified only under the specified independence and likelihood assumptions.¶

The numerical values are illustrative, not measured diagnostic accuracy.¶

2.B.11.6. Example 6 - Negative Evidence with Incomplete Reporting¶

A fault hypothesis predicts that a device will issue an alarm.¶

No such alarm appears in the monitoring repository.¶

However, the source subscription was disconnected during the relevant interval.¶

The processor cannot treat the missing alarm as conclusive contradiction.¶

If a source later supplies a complete applicable history and the detection model establishes that the alarm would necessarily have been generated and reported, the absence may then become relevant negative evidence.¶

The inference depends upon those source and detection assumptions.¶

2.B.11.7. Example 7 - Two Simultaneous Causes¶

An optical interface experiences a signal impairment.¶

At the same time, traffic exceeds a relevant processing capacity.¶

Both may contribute to packet loss.¶

Agent A diagnoses optical impairment.¶

Agent B diagnoses congestion.¶

An exclusive single-root-cause model would reject one explanation.¶

A multi-fault diagnostic model may permit both.¶