# LEO Anomaly Library Runtime Evidence DOCUMENT STATUS: EVALUATOR-FACING RUNTIME EVIDENCE DOCUMENT TYPE: BOUNDED PUBLIC EVALUATION EVIDENCE PACKAGE STRATEGIC TRACK: LEO PUBLIC VISIBILITY & PUBLIC EVALUATION READINESS PUBLICATION REPOSITORY: BBS-Open-System SOURCE ARCHITECTURE: CANONICAL LEO ANOMALY LIBRARY RUNTIME BASELINE: ANOMALY LIBRARY RUNTIME v0.1 RUNTIME STATUS: READ_ONLY_RUNTIME_COMPLETE BASELINE STATUS: FROZEN REVIEWED BASELINE EVIDENCE SCOPE: BOUNDED RUNTIME IMPLEMENTATION, TEST, GOVERNANCE, PROVENANCE, AND HUMAN-REVIEW EVIDENCE HUMAN REVIEW: REQUIRED AUTONOMOUS LEARNING: NOT AUTHORIZED AUTONOMOUS ENFORCEMENT: NOT AUTHORIZED PRODUCTION MUTATION: NOT AUTHORIZED FRAUD VERDICT AUTHORITY: NOT AUTHORIZED LEGAL VERDICT AUTHORITY: NOT AUTHORIZED PRODUCTION DEPLOYMENT CLAIM: NOT MADE COMPLETE-SYSTEM PRODUCTION READINESS CLAIM: NOT MADE --- # 1. Purpose This document provides evaluator-facing runtime evidence for the current reviewed LEO Anomaly Library Runtime baseline. Its purpose is to connect the documented LEO Anomaly Library architecture to inspectable implementation, test, evidence-lineage, provenance, governance, and human-review behavior without extending the claims beyond the reviewed runtime boundary. The document is intended to help an external evaluator distinguish between: - architectural intent; - implemented runtime behavior; - reviewed runtime behavior; - test-backed runtime evidence; - frozen baseline status; - human-reviewed institutional knowledge; - public evaluation evidence; - and capabilities that remain explicitly outside the authorized runtime boundary. This distinction is essential to the LEO methodology. The existence of a runtime implementation does not by itself establish production authorization. The successful execution of runtime tests does not by itself establish institutional decision authority. The generation of an anomaly-related review package does not constitute an anomaly verdict. A runtime validation result does not constitute a fraud finding, legal finding, enforcement action, or autonomous institutional decision. The Anomaly Library Runtime documented here is therefore presented as a bounded, read-only, evidence-preserving, provenance-preserving, human-review-support runtime. --- # 2. Evaluator Context LEO is being developed as a human-controlled institutional integrity, provenance, evidence review, process interpretation, and anomaly analysis system. Within that architecture, the Anomaly Library is not intended to operate as an autonomous fraud detector or autonomous institutional decision engine. Its role is narrower and more controlled. The Anomaly Library provides a structured architectural and runtime layer for working with reviewed anomaly knowledge while preserving the evidence, provenance, process context, classification context, and human-review requirements associated with that knowledge. The current runtime baseline supports the validation and review preparation of reviewed anomaly artifacts. It does not independently convert observations into final institutional knowledge. It does not independently decide whether misconduct, fraud, illegality, or institutional failure has occurred. It does not autonomously modify institutional systems in response to an anomaly. Instead, the runtime operates inside a governance model in which evidence is preserved, provenance is preserved, review boundaries are explicit, and institutionally significant interpretation remains subject to human review. For evaluation purposes, this document should therefore be read as evidence of a specific implemented and reviewed runtime capability, not as evidence that all future Anomaly Library capabilities have been implemented. --- # 3. Why the Anomaly Library Exists Institutional anomalies cannot be interpreted safely as isolated deviations. The significance of an apparent anomaly depends on context. That context may include: - the underlying evidence; - the provenance of that evidence; - the process in which the observation occurred; - the Process Mode associated with that process; - the anomaly category; - previously reviewed classifications; - relationships to other reviewed artifacts; - the history of human review; - and the institutional meaning assigned through controlled review. A deviation inside a highly deterministic process may have a different interpretive significance from superficially similar variation inside a stochastic or mixed process. For that reason, the LEO architecture does not treat anomaly knowledge as a context-free collection of suspicious events. The Anomaly Library is designed to preserve the context necessary for a human reviewer to understand what has been observed, how the observation was classified, what evidence supports it, where that evidence came from, and which interpretations have actually been reviewed. The runtime documented here implements a controlled portion of that model. Its primary runtime function is validation and review preparation. Its primary governance function is preservation of the boundary between machine-executed validation and human-reviewed institutional interpretation. --- # 4. Architectural Position The Anomaly Library follows the Process Mode Layer in the current LEO architectural dependency model. At a high level: ```text SOURCE EVIDENCE -> EVIDENCE-DERIVED CHARACTERISTICS -> SIGNAL ELIGIBILITY -> DETERMINISTIC / STOCHASTIC SIGNAL COUNTS -> PROCESS MODE PROPOSAL -> HUMAN REVIEW -> REVIEWED PROCESS CONTEXT -> ANOMALY INTERPRETATION / REVIEW CONTEXT -> REVIEWED ANOMALY KNOWLEDGE ``` The Anomaly Library must therefore not be interpreted as replacing Process Mode analysis. Process Mode provides relevant process context for anomaly interpretation. The Anomaly Library provides structured handling of reviewed anomaly knowledge within that context. This dependency is important because an observation that appears exceptional under one process model may be expected variation under another. The architecture consequently preserves a distinction between: source evidence; evidence-derived interpretation; process characterization; anomaly-related interpretation; reviewed classification; and institutional knowledge accepted through human review. The runtime must not collapse these stages into a single autonomous decision. --- # 5. Relationship to the Process Mode Layer The Anomaly Library Runtime includes validation of Process Mode assignments as part of its reviewed artifact validation boundary. This does not mean that the Anomaly Library independently establishes the Process Mode of an institutional process. The Process Mode Layer has its own architectural and runtime evidence boundary. The current canonical Process Mode states are: DETERMINISTIC_PROCESS; STOCHASTIC_PROCESS; MIXED_PROCESS; UNKNOWN_REQUIRES_REVIEW. The current canonical Process Mode interpretation pipeline is: SOURCE EVIDENCE -> EVIDENCE-DERIVED CHARACTERISTICS -> SIGNAL ELIGIBILITY -> DETERMINISTIC / STOCHASTIC SIGNAL COUNTS -> PROCESS MODE PROPOSAL -> HUMAN REVIEW The Anomaly Library consumes process context only within its authorized architectural relationship to that layer. It must preserve the distinction between: a Process Mode proposal; a human-reviewed Process Mode classification; anomaly evidence; anomaly classification; and reviewed anomaly knowledge. No automatic anomaly conclusion should be inferred solely from the presence of a deterministic, stochastic, mixed, or unknown Process Mode classification. Likewise, Process Mode must not be treated as a proxy for institutional correctness, misconduct, fraud, legality, or compliance. The Process Mode relationship exists to improve interpretive context, not to create autonomous institutional conclusions. --- # 6. Knowledge Boundary A central LEO architectural principle is: DOCUMENTS ARE EVIDENCE. HUMAN-CONFIRMED CLASSIFICATIONS ARE KNOWLEDGE. The Anomaly Library must preserve this distinction. A document entering the evidence environment does not automatically become institutional knowledge. A runtime-readable anomaly artifact does not automatically become trusted institutional knowledge merely because it conforms to a schema. A successful validation result establishes that the artifact satisfies the applicable runtime validation requirements. It does not establish that every substantive interpretation represented by the artifact is institutionally correct. Human review remains necessary for that transition. The runtime therefore supports reviewed knowledge without autonomously manufacturing reviewed knowledge. This distinction is also fundamental to the long-term memory model of LEO. Institutional memory must be traceable to evidence and review history rather than being accumulated through opaque autonomous inference. --- # 7. Reviewed Knowledge and Runtime Validation The Anomaly Library Runtime works with reviewed anomaly library artifacts. Its validation responsibilities include the structural and governance properties necessary to determine whether those artifacts can safely enter the runtime review flow. The reviewed runtime boundary includes: artifact loading; schema validation; reference validation; evidence-lineage validation; provenance validation; anomaly-category validation; Process Mode assignment validation; reviewed-classification validation; human-review requirement validation; runtime orchestration; runtime pipeline execution; Human Review Package generation; evidence preservation; provenance preservation; source-reference preservation; and production of read-only validation outputs. These functions establish an inspectable runtime path between reviewed artifacts and human review support. They do not authorize autonomous institutional action. --- # 8. Evidence Lineage Requirement Evidence lineage is a mandatory property of the runtime baseline. The reviewed runtime preserves: evidence identifiers; source references; artifact identity; lineage relationships; provenance; review metadata; reference relationships; and validation history. The runtime is not authorized to rewrite source evidence in order to make it fit an anomaly interpretation. It is not authorized to replace provenance with generated provenance. It is not authorized to detach reviewed classifications from the evidence and review context through which they were established. This means that an evaluator should be able to distinguish between: SOURCE -> EVIDENCE -> REVIEWED ARTIFACT -> RUNTIME VALIDATION -> HUMAN REVIEW PACKAGE -> HUMAN REVIEW rather than encountering a single opaque anomaly result with no inspectable lineage. This preservation of lineage is part of the integrity boundary of the Anomaly Library Runtime. --- # 9. Provenance Requirement Provenance preservation is separate from, but closely related to, evidence lineage. Evidence lineage describes the traceable relationship among evidence, artifacts, classifications, validation, and review. Provenance preserves information about origin, identity, source relationships, and review context. The reviewed runtime baseline preserves provenance rather than rewriting it. The evaluator-facing significance of this requirement is substantial. An anomaly-related result without provenance may be impossible to audit reliably. A reviewed classification detached from its source and review history may appear more authoritative than the underlying evidence permits. The LEO runtime therefore treats provenance as part of the evidence-control model rather than as optional metadata. --- # 10. Human Review Boundary The Anomaly Library Runtime is a human-review support layer. Human review is not an optional final presentation step added after an otherwise autonomous decision. It is part of the architectural authorization boundary. Runtime outputs are not institutional decisions. Runtime outputs are not enforcement actions. Runtime outputs are not fraud findings. Runtime outputs are not legal findings. Runtime validation does not authorize production mutation. Runtime validation does not authorize autonomous learning. Runtime validation does not authorize autonomous enforcement. Any institutionally significant use of the runtime output remains subject to human review. The runtime may organize, validate, preserve, and present evidence and reviewed artifact relationships. It may prepare structured material for a reviewer. It may expose inconsistencies or validation failures within its approved scope. It may not convert those operations into an autonomous institutional verdict. --- # 11. Baseline Identity The evidence presented in this package refers to the reviewed Anomaly Library Runtime baseline designated: ANOMALY LIBRARY RUNTIME v0.1 The reviewed status of that baseline is: READ_ONLY_RUNTIME_COMPLETE The implementation status recorded by the runtime completion review is: FOUNDATION_COMPLETE The baseline was subsequently frozen as the official reviewed Anomaly Library Runtime reference point for the applicable development stage. The freeze does not mean that the entire Anomaly Library architecture is complete. It does not mean that no future runtime development is expected. It means that the reviewed runtime behavior covered by the freeze is treated as a controlled reference baseline against which subsequent Anomaly Library Runtime evolution can be compared. This distinction protects both architectural continuity and evaluator interpretation. --- # 12. What the Frozen Baseline Establishes Within its reviewed scope, the frozen baseline establishes an implemented runtime capable of: REVIEWED ARTIFACT INPUT -> STRUCTURAL AND REFERENCE VALIDATION -> EVIDENCE-LINEAGE VALIDATION -> PROVENANCE VALIDATION -> ANOMALY-CATEGORY VALIDATION -> PROCESS MODE ASSIGNMENT VALIDATION -> REVIEWED-CLASSIFICATION VALIDATION -> HUMAN-REVIEW REQUIREMENT VALIDATION -> RUNTIME ORCHESTRATION / PIPELINE -> HUMAN REVIEW PACKAGE The baseline is designed to preserve rather than overwrite the reviewed evidence context during this flow. The runtime is deterministic in the engineering sense that the reviewed validation behavior is intended to be reproducible for the same applicable inputs and runtime conditions. That engineering characteristic must not be confused with the Process Mode classification DETERMINISTIC_PROCESS. The former describes a property of runtime execution. The latter describes a classification within the LEO Process Mode architecture. They are separate concepts. --- # 13. What the Frozen Baseline Does Not Establish The frozen baseline does not establish: production deployment; production authorization; autonomous anomaly detection authority; autonomous anomaly learning; autonomous institutional knowledge creation; autonomous institutional decision-making; autonomous enforcement; fraud determination; legal determination; complete institutional-process coverage; complete anomaly-domain coverage; complete LEO system readiness; or complete-system production readiness. The baseline must not be interpreted as evidence that LEO autonomously decides whether an institution, organization, person, transaction, document, or process is fraudulent, unlawful, compliant, non-compliant, trustworthy, or untrustworthy. Those claims are outside the reviewed runtime boundary. --- # 14. Public Evaluation Claim Boundary For public evaluation, the appropriate claim is: The LEO Anomaly Library Runtime v0.1 is a reviewed and frozen read-only runtime baseline for validating reviewed anomaly artifacts, preserving evidence lineage and provenance, maintaining Process Mode and classification context, and generating structured support for mandatory human review. The following stronger claim is not authorized: LEO autonomously detects and decides institutional anomalies. The following claim is also not authorized: LEO automatically determines fraud or legal violations. The following claim is also not established by this evidence package: The Anomaly Library Runtime is production-ready as a complete institutional system. The evaluator should therefore assess the baseline according to the bounded capabilities actually demonstrated and reviewed. --- # 15. Evidence Classes Used in This Package The remainder of this evidence package distinguishes among several evidence classes. 15.1 Architectural Evidence Architectural evidence describes the intended role, boundaries, relationships, and governance model of the Anomaly Library. It does not by itself prove runtime implementation. 15.2 Implementation Evidence Implementation evidence identifies concrete runtime components and executable behavior corresponding to the reviewed architecture. It demonstrates implementation existence but does not by itself establish that all behavior has been reviewed or validated. 15.3 Test Evidence Test evidence records executable validation of the reviewed runtime behavior within a defined test scope. The current reviewed Anomaly Library Runtime test baseline records: 61 passed This number refers to the applicable Anomaly Library Runtime test scope. It must not be presented as the current global LEO test count. 15.4 Review Evidence Review evidence records the outcome of structured architectural, governance, evidence, provenance, human-review, and runtime-safety review. The reviewed baseline records PASS outcomes for the applicable review categories. 15.5 Freeze Evidence Freeze evidence establishes the controlled runtime reference point against which future changes can be assessed. Freeze status does not imply production deployment or prohibition of future development. 15.6 Evaluator-Facing Evidence This document is evaluator-facing evidence. It organizes selected canonical evidence into a bounded public explanation. It does not replace the canonical source architecture. It does not create new runtime authority. It does not silently broaden the claims made by the canonical reviews. --- # 16. Evaluation Principle The Anomaly Library Runtime should be evaluated according to a simple principle: CLAIM ONLY WHAT CAN BE CONNECTED TO ARCHITECTURE + IMPLEMENTATION + TEST OR REVIEW EVIDENCE + GOVERNANCE BOUNDARIES + HUMAN REVIEW Where one of these elements is absent, the corresponding public claim should be narrowed rather than inferred. This evidence package follows that principle. --- # 17. Runtime Architecture The reviewed Anomaly Library Runtime implements a bounded execution layer between reviewed anomaly artifacts and structured human review. Its architectural purpose is not to replace the Anomaly Library knowledge model. Its purpose is to provide executable validation and orchestration for the reviewed artifacts governed by that model. At a high level, the runtime path can be represented as: ```text REVIEWED ANOMALY ARTIFACTS -> ARTIFACT LOADING -> SCHEMA VALIDATION -> REFERENCE VALIDATION -> EVIDENCE-LINEAGE VALIDATION -> PROVENANCE VALIDATION -> ANOMALY-CATEGORY VALIDATION -> PROCESS MODE ASSIGNMENT VALIDATION -> REVIEWED-CLASSIFICATION VALIDATION -> HUMAN-REVIEW REQUIREMENT VALIDATION -> RUNTIME ORCHESTRATION -> HUMAN REVIEW PACKAGE -> HUMAN REVIEW ``` Each stage exists inside the reviewed read-only runtime boundary. The stages should not be interpreted as a sequence that autonomously produces an institutional anomaly verdict. The runtime validates whether reviewed artifacts and their relationships satisfy the applicable structural, provenance, lineage, classification, and human-review requirements. The institutionally significant interpretation remains outside autonomous runtime authority. --- # 18. Runtime Component Model The frozen runtime baseline separates responsibilities into explicit runtime components rather than concentrating all validation and interpretation inside a single opaque operation. The reviewed component model includes functions for: artifact loading; schema validation; anomaly-category validation; reviewed-classification validation; Process Mode assignment validation; evidence-lineage validation; provenance validation; human-review requirement validation; reference validation; runtime orchestration; pipeline execution; and Human Review Package generation. This separation is relevant to evaluation because it makes the runtime boundary more inspectable. An evaluator can distinguish between: INPUT HANDLING VALIDATION RELATIONSHIP CHECKING GOVERNANCE CHECKING ORCHESTRATION REVIEW-PACKAGE GENERATION rather than treating all runtime behavior as an undifferentiated anomaly analysis operation. --- # 19. Artifact Loading Artifact loading is the entry point through which reviewed anomaly artifacts become available to the runtime validation flow. The loader is not authorized to reinterpret an artifact merely because it can read it. Loading establishes runtime access to the artifact. It does not establish substantive correctness. It does not establish institutional acceptance. It does not establish that an anomaly classification is true. The runtime therefore preserves the distinction: ARTIFACT CAN BE LOADED != ARTIFACT IS VALID != CLASSIFICATION IS HUMAN-APPROVED != INSTITUTIONAL DECISION This boundary prevents basic machine readability from being confused with reviewed institutional knowledge. --- # 20. Schema Validation Schema validation checks whether reviewed anomaly artifacts conform to the expected structural requirements of the applicable runtime model. This may include validation of required fields, expected data structures, and other schema-governed properties represented by the reviewed runtime implementation. A successful schema validation means that the artifact satisfies the applicable structural contract. It does not mean that the substantive anomaly interpretation is correct. It does not mean that the supporting evidence is sufficient for an institutional decision. It does not mean that human review can be bypassed. Accordingly: SCHEMA_VALID -> STRUCTURALLY ACCEPTABLE FOR THE APPLICABLE RUNTIME FLOW must not be transformed into: SCHEMA_VALID -> ANOMALY CONFIRMED The second interpretation is outside the reviewed runtime authority. --- # 21. Reference Validation Reviewed anomaly knowledge may depend on relationships among multiple artifacts. Those relationships must remain inspectable. Reference validation exists to determine whether applicable references used by the reviewed artifacts satisfy the runtime reference requirements. The runtime must not silently invent a missing referenced artifact. It must not silently redirect a broken reference to a different artifact. It must not replace an unavailable source relationship with an inferred relationship and then present that inference as preserved provenance. Reference validation therefore supports both structural integrity and institutional auditability. The intended relationship is: REVIEWED ARTIFACT -> EXPLICIT REFERENCE -> RESOLVABLE / VALIDATABLE RELATIONSHIP rather than: REVIEWED ARTIFACT -> MISSING OR AMBIGUOUS REFERENCE -> AUTONOMOUSLY INVENTED SUBSTITUTE The latter behavior is not part of the frozen runtime baseline. --- # 22. Evidence-Lineage Validation Evidence-lineage validation is a distinct runtime responsibility. Its purpose is to preserve the traceable relationship between reviewed anomaly artifacts and the evidence context on which they depend. This is important because anomaly-related interpretation can become unsafe if the interpretive result is detached from the evidence that produced it. The runtime therefore validates applicable lineage relationships rather than treating lineage as optional descriptive metadata. The evaluator should understand the lineage model as preserving an inspectable path such as: SOURCE EVIDENCE -> EVIDENCE REFERENCE -> REVIEWED ARTIFACT -> REVIEWED CLASSIFICATION CONTEXT -> RUNTIME VALIDATION -> HUMAN REVIEW PACKAGE The exact structure of individual artifacts may vary within their applicable schema and review model, but the governance principle remains constant: anomaly knowledge must not become detached from its evidentiary lineage. --- # 23. Provenance Validation Provenance validation addresses the origin and traceability context associated with reviewed anomaly artifacts and their evidence relationships. The runtime treats provenance as a validation concern. It does not treat provenance as cosmetic metadata. This is necessary because two apparently identical statements can have different evidentiary significance when their origins, review histories, or source relationships differ. Within the frozen runtime boundary, provenance is preserved and validated. The runtime is not authorized to manufacture replacement provenance. It is not authorized to rewrite provenance in order to strengthen an anomaly claim. It is not authorized to erase provenance that weakens or complicates an interpretation. The runtime therefore supports the following integrity principle: PRESERVE ORIGIN PRESERVE SOURCE RELATIONSHIPS PRESERVE REVIEW CONTEXT VALIDATE APPLICABLE PROVENANCE REQUIREMENTS DO NOT REWRITE HISTORY This behavior is fundamental to the evidentiary role of the Anomaly Library. --- # 24. Anomaly-Category Validation The runtime includes validation of anomaly-category information represented in reviewed artifacts. An anomaly category provides structured interpretive context. It does not independently establish institutional culpability, fraud, illegality, or enforcement significance. Category validation therefore determines whether the applicable category representation satisfies the reviewed runtime requirements. It does not autonomously decide that the categorized phenomenon has occurred as a final institutional fact. The distinction is: CATEGORY REPRESENTATION VALID versus: CATEGORY SUBSTANTIVELY CONFIRMED BY FINAL HUMAN REVIEW These are not equivalent states. The runtime operates on the former within its authorized scope. Institutionally significant acceptance remains subject to the latter. --- # 25. Process Mode Assignment Validation The Anomaly Library Runtime validates applicable Process Mode assignments associated with reviewed anomaly artifacts. This capability exists because anomaly interpretation depends in part on the character of the process in which the relevant observation occurs. The runtime does not use a Process Mode assignment as an automatic anomaly verdict. For example: DETERMINISTIC_PROCESS does not mean: ANY DEVIATION = FRAUD Likewise: STOCHASTIC_PROCESS does not mean: ANY VARIATION = ACCEPTABLE and: MIXED_PROCESS does not authorize the runtime to decide autonomously which component of a specific observation should dominate institutional interpretation. UNKNOWN_REQUIRES_REVIEW explicitly preserves uncertainty requiring review. The runtime therefore validates Process Mode context without transforming that context into autonomous institutional judgment. --- # 26. Transitional Process Context Process character may not always be adequately represented by treating institutional behavior as permanently fixed at one conceptual extreme. A process may contain deterministic and stochastic characteristics at the same time. That case is represented at the current Process Mode classification level by: MIXED_PROCESS However, the existence of a mixed classification should not be interpreted as proof that the current architecture already implements a complete temporal model of transitions between deterministic and stochastic process states. Questions such as: DETERMINISTIC -> INCREASING DISCRETION OR VARIABILITY -> MIXED -> PREDOMINANTLY STOCHASTIC or the reverse direction are relevant to future evaluator-facing development, but they require explicit evidence, state-transition semantics, temporal context, and human-review rules before they can be presented as implemented runtime capability. Accordingly, this Anomaly Library Runtime evidence package uses the current reviewed Process Mode classifications as contextual inputs where applicable. It does not claim that a complete Process Mode transition-state model is already implemented. This distinction prevents a current capability from being overstated while preserving an important architectural direction for subsequent reviewed development. --- # 27. Reviewed-Classification Validation The runtime includes validation of reviewed-classification information. The word reviewed is significant. LEO distinguishes between machine-readable interpretation and human-confirmed knowledge. The runtime must therefore preserve the review status associated with a classification. A classification cannot become human-reviewed merely because it successfully passes structural validation. The relationship is instead: CLASSIFICATION DATA + REVIEW STATUS + EVIDENCE CONTEXT + PROVENANCE -> VALIDATABLE REVIEWED ARTIFACT The runtime may validate these properties within the reviewed contract. It does not independently grant human-review status to an unreviewed classification. --- # 28. Human-Review Requirement Validation Human-review requirement validation is a first-class runtime responsibility. This is not merely a user-interface concern. The requirement is part of the governance architecture. Where an artifact or runtime flow requires human review, the runtime must preserve that requirement. It must not silently convert: HUMAN_REVIEW_REQUIRED into: AUTO_APPROVED because other validations passed. This is one of the central safeguards of the runtime. Structural validity, reference validity, lineage validity, provenance validity, and classification validity do not eliminate the human-review boundary. The runtime can establish that an artifact is ready for the applicable review flow. It cannot replace the reviewer. --- # 29. Runtime Orchestration The reviewed baseline includes runtime orchestration that coordinates the applicable validation components. The orchestration layer provides a controlled execution path through the runtime responsibilities. Conceptually: INPUT ARTIFACT SET -> LOAD -> VALIDATE -> CHECK RELATIONSHIPS -> CHECK GOVERNANCE REQUIREMENTS -> ASSEMBLE RESULTS -> PREPARE HUMAN REVIEW SUPPORT Orchestration does not create additional authority beyond the authority of the individual validated operations. The existence of an orchestration layer therefore must not be interpreted as the existence of an autonomous decision engine. Its role is coordination. Its role is reproducibility. Its role is consistent execution of the reviewed runtime path. Its role is not autonomous institutional judgment. --- # 30. Runtime API Surface The reviewed implementation exposes a concrete runtime API surface. The canonical runtime includes the primary runtime class: AnomalyLibraryRuntime with reviewed execution behavior represented through: run_reviewed_artifacts(...) The runtime pipeline includes: AnomalyLibraryPipeline with execution paths represented through: run(...) and: run_paths(...) These interfaces are relevant because they demonstrate that the Anomaly Library Runtime is represented by executable software components rather than architecture documentation alone. However, the existence of callable runtime interfaces does not broaden their authorization. The same governance constraints apply regardless of whether the runtime is invoked directly, through a pipeline, through tests, or through a future approved integration layer. --- # 31. Pipeline Execution Model The pipeline provides a higher-level execution path across the reviewed runtime functions. Its evaluator-facing significance is that the individual validators are not merely disconnected implementation fragments. They participate in a controlled runtime flow. The pipeline should be understood as: CONTROLLED EXECUTION + VALIDATION COORDINATION + RESULT AGGREGATION + REVIEW PREPARATION and not as: AUTONOMOUS ANOMALY JUDGMENT The pipeline remains subject to: read-only operation; evidence preservation; provenance preservation; reference preservation; reviewed-classification boundaries; Process Mode context boundaries; human-review requirements; no autonomous enforcement; no autonomous learning; no production mutation; no fraud verdict authority; and no legal verdict authority. --- # 32. Human Review Package Generation A significant runtime output is the Human Review Package. The Human Review Package exists to make relevant validated material available for structured human evaluation. Its role is not to conceal the underlying evidence behind a machine-generated conclusion. Its role is to preserve and organize the information necessary for review. Depending on the applicable reviewed artifact context, this may include validated relationships concerning: artifact identity; evidence references; lineage; provenance; anomaly categories; Process Mode assignments; reviewed classifications; reference relationships; validation results; and human-review requirements. The package is therefore an interface between runtime validation and human institutional judgment. It is not a replacement for that judgment. --- # 33. Read-Only Output Model The frozen runtime baseline is explicitly read-only. This has several consequences. The runtime may produce validation outputs and Human Review Packages within its reviewed scope. It may not use those outputs as authority to mutate production records. It may not rewrite the source evidence. It may not rewrite reviewed anomaly artifacts to eliminate validation problems. It may not alter institutional records in order to make them consistent with a proposed anomaly interpretation. It may not autonomously correct a process merely because an anomaly-related validation or review condition has been identified. The output model can therefore be represented as: READ -> VALIDATE -> PRESERVE -> REPORT -> PREPARE FOR HUMAN REVIEW not: READ -> DECIDE -> MUTATE -> ENFORCE This distinction is one of the principal runtime safety properties of the frozen baseline. --- # 34. Runtime Failure Behavior A safe anomaly runtime must preserve failure and uncertainty rather than silently converting them into stronger conclusions. Where applicable runtime validation fails, the appropriate result is a validation failure or review-relevant condition. The runtime must not compensate for missing or invalid evidence by inventing support. It must not convert a failed provenance check into assumed provenance. It must not convert a failed reference check into an inferred valid reference. It must not convert an unresolved review requirement into autonomous approval. It must not convert insufficient process context into a confident anomaly conclusion. The safe pattern is: VALIDATION FAILURE -> PRESERVED FAILURE STATE -> INSPECTABLE REVIEW CONTEXT -> HUMAN REVIEW WHERE APPLICABLE This behavior supports institutional integrity because uncertainty and inconsistency remain visible rather than being hidden by automated completion. --- # 35. Determinism of Runtime Execution The reviewed runtime is intended to provide controlled and reproducible validation behavior for the same applicable inputs and runtime conditions. This engineering determinism is useful for evaluation. It allows test-backed runtime behavior to be inspected and repeated. However, as stated earlier, engineering determinism must remain distinct from the Process Mode state: DETERMINISTIC_PROCESS The runtime can execute validation deterministically while processing artifacts whose institutional process context is: DETERMINISTIC_PROCESS STOCHASTIC_PROCESS MIXED_PROCESS UNKNOWN_REQUIRES_REVIEW There is no contradiction between those facts because they describe different layers of the system. --- # 36. Separation of Validation and Interpretation The runtime architecture deliberately separates validation from final institutional interpretation. Validation asks questions such as: Is the artifact structurally acceptable? Are required references valid? Is evidence lineage preserved? Is provenance represented as required? Is the anomaly category representation valid? Is the Process Mode assignment represented according to the applicable contract? Is the reviewed classification represented according to its contract? Is the human-review requirement preserved? Institutional interpretation asks different questions, such as: What does the reviewed evidence mean in the institutional context? Is the classification justified? Does the anomaly require further investigation? Is corrective institutional action appropriate? Does the evidence support any legal or compliance conclusion? The frozen runtime supports the first class of questions. It does not autonomously resolve the second class. --- # 37. Separation of Detection, Validation, and Verdict For evaluator clarity, three concepts must remain separate: DETECTION VALIDATION VERDICT The current public evidence package is principally evidence of a reviewed validation and human-review-support runtime. It should not be interpreted as evidence of unrestricted autonomous anomaly detection. It must not be interpreted as evidence of autonomous verdict authority. Even where anomaly-related information is present in a reviewed artifact, the runtime's role is governed by the reviewed artifact and validation contracts. The runtime does not acquire broader authority merely because the subject matter concerns anomalies. --- # 38. Separation of Runtime and Production Systems The frozen baseline is a runtime engineering and evaluation baseline. It is not evidence that LEO has been authorized to mutate a production institutional system. Any future production integration would require its own explicit architecture, authorization, governance, security, validation, deployment, rollback, monitoring, and human-control decisions. Those decisions are outside this evidence package. Accordingly: RUNTIME COMPLETE FOR REVIEWED SCOPE does not mean: PRODUCTION DEPLOYMENT AUTHORIZED and: BASELINE FROZEN does not mean: PRODUCTION SYSTEM FROZEN The evaluator should preserve these distinctions when assessing maturity. --- # 39. Runtime Architecture Assessment Within the bounded reviewed scope, the Anomaly Library Runtime demonstrates a coherent implementation path from reviewed anomaly artifacts through validation, orchestration, and Human Review Package generation. The architecture is materially aligned with the LEO governance principles because the runtime: separates artifact loading from substantive acceptance; separates schema validity from anomaly confirmation; validates explicit references rather than inventing replacements; preserves evidence lineage; preserves provenance; validates anomaly-category representation; preserves Process Mode context; preserves reviewed-classification status; validates mandatory human-review requirements; coordinates runtime execution without creating autonomous authority; produces review-support outputs rather than enforcement actions; and remains inside a read-only runtime boundary. The implementation therefore provides an evaluator with inspectable evidence that the Anomaly Library methodology is represented by executable runtime behavior within a defined scope. It does not establish complete Anomaly Library implementation. It does not establish complete LEO implementation. It does not establish production authorization. --- # 40. Part 2 Assessment For the purposes of this evaluator-facing package: RUNTIME_ARCHITECTURE = IMPLEMENTED_WITHIN_REVIEWED_SCOPE RUNTIME_COMPONENT_SEPARATION = PRESENT ARTIFACT_LOADING = PRESENT SCHEMA_VALIDATION = PRESENT REFERENCE_VALIDATION = PRESENT EVIDENCE_LINEAGE_VALIDATION = PRESENT PROVENANCE_VALIDATION = PRESENT ANOMALY_CATEGORY_VALIDATION = PRESENT PROCESS_MODE_ASSIGNMENT_VALIDATION = PRESENT REVIEWED_CLASSIFICATION_VALIDATION = PRESENT HUMAN_REVIEW_REQUIREMENT_VALIDATION = PRESENT RUNTIME_ORCHESTRATION = PRESENT PIPELINE_EXECUTION = PRESENT HUMAN_REVIEW_PACKAGE_GENERATION = PRESENT READ_ONLY_RUNTIME_BOUNDARY = PRESERVED AUTONOMOUS_INSTITUTIONAL_DECISION_AUTHORITY = ABSENT PRODUCTION_MUTATION_AUTHORITY = ABSENT The next part examines the evidence, provenance, reviewed-knowledge, Process Mode, and anomaly-interpretation relationships in greater detail. --- # 41. Evidence-Control Architecture The Anomaly Library Runtime operates inside the broader LEO evidence-control architecture. Anomaly-related information is not treated as self-validating merely because it is represented in a structured artifact or processed by executable runtime code. The runtime preserves separation among: ```text SOURCE EVIDENCE -> EVIDENCE REFERENCES -> EVIDENCE LINEAGE -> PROVENANCE -> PROCESS CONTEXT -> ANOMALY-RELATED INTERPRETATION -> REVIEWED CLASSIFICATION -> HUMAN REVIEW -> REVIEWED ANOMALY KNOWLEDGE ``` These stages represent different evidentiary and governance states. The runtime must not collapse them into a single machine-generated conclusion. This separation is particularly important for institutional anomaly analysis because the meaning of an observation can change materially depending on its source, provenance, process context, classification history, and human-review status. --- # 42. Source Evidence Boundary Source evidence remains distinct from anomaly interpretation. The presence of evidence does not by itself establish that an anomaly exists. Likewise, the absence of expected evidence does not automatically establish misconduct, fraud, illegality, or institutional failure. Source evidence may include records, documents, structured artifacts, reviewed references, or other material accepted by the applicable LEO evidence model. The runtime must preserve the identity and applicable relationships of such evidence. It must not rewrite source evidence to make it conform to a preferred anomaly interpretation. It must not silently discard contradictory evidence. It must not manufacture missing evidence. The safe evidence relationship is: SOURCE EVIDENCE -> PRESERVED EVIDENCE IDENTITY -> TRACEABLE REFERENCE -> REVIEW CONTEXT not: SOURCE EVIDENCE -> AUTONOMOUS REINTERPRETATION -> REWRITTEN EVIDENCE --- # 43. Evidence Is Not a Verdict Evidence and verdict are separate concepts. An evidence artifact can support, weaken, complicate, or leave unresolved an anomaly-related interpretation. The runtime must therefore preserve evidentiary states without converting them automatically into final conclusions. Conceptually: EVIDENCE AVAILABLE != ANOMALY CONFIRMED and: EVIDENCE INCOMPLETE != ANOMALY DISPROVED and: EVIDENCE CONFLICTING != AUTOMATIC FRAUD FINDING The evaluator should interpret runtime evidence as material prepared for controlled review rather than as autonomous adjudication. --- # 44. Evidence-Lineage Chain The Anomaly Library Runtime preserves the lineage necessary to connect reviewed anomaly knowledge back to its evidentiary basis. A simplified lineage chain is: SOURCE -> SOURCE EVIDENCE -> EVIDENCE REFERENCE -> REVIEWED ANOMALY ARTIFACT -> CLASSIFICATION CONTEXT -> RUNTIME VALIDATION -> HUMAN REVIEW PACKAGE -> HUMAN REVIEW The exact artifact representation may differ according to the applicable schema, but the integrity requirement remains the same. A reviewer must not be presented with an anomaly-related conclusion that has been detached from the evidence relationships necessary to inspect it. Lineage therefore serves both technical and institutional purposes. Technically, it enables validation of relationships among artifacts. Institutionally, it enables review of how a conclusion or classification came to exist. --- # 45. Lineage Preservation Versus Lineage Reconstruction Preserving known lineage is different from autonomously reconstructing missing lineage. The frozen runtime baseline supports validation and preservation of applicable lineage relationships. It must not silently invent missing historical relationships. Where lineage is absent, broken, inconsistent, or insufficient under the applicable contract, that condition should remain visible. The correct pattern is: LINEAGE PRESENT -> VALIDATE -> PRESERVE or: LINEAGE PROBLEM -> PRESERVE PROBLEM STATE -> SURFACE FOR REVIEW not: LINEAGE PROBLEM -> GENERATE PLAUSIBLE HISTORY -> PRESENT AS FACT This boundary protects the provenance and institutional memory model of LEO. --- # 46. Provenance as an Integrity Control Provenance provides information necessary to understand the origin and history of evidence and reviewed artifacts. Within anomaly analysis, provenance is particularly important because a statement without reliable origin information may be materially weaker than the same statement supported by a traceable source and review history. The runtime therefore preserves provenance as part of the integrity model. Applicable provenance information must remain connected to the artifact or relationship it describes. The runtime is not authorized to replace uncertain provenance with confident generated provenance. It is not authorized to erase inconvenient provenance. It is not authorized to alter provenance in order to strengthen an anomaly classification. --- # 47. Provenance and Confidence Provenance may affect how a human reviewer assesses evidence, but provenance must not be reduced to a simplistic automatic truth score. A traceable source may improve auditability. It does not automatically make every substantive claim from that source true. Likewise, incomplete provenance may create an evidentiary limitation without automatically proving that the underlying information is false. The runtime therefore preserves provenance for review rather than using provenance alone as an autonomous institutional verdict mechanism. This distinction prevents: GOOD PROVENANCE -> AUTOMATICALLY TRUE and: INCOMPLETE PROVENANCE -> AUTOMATICALLY FALSE from becoming implicit runtime rules. --- # 48. Reviewed Artifact Boundary A reviewed anomaly artifact occupies a different state from raw source evidence. It represents evidence and interpretation that have entered an explicit review model. However, the word reviewed must remain tied to actual review status. The runtime must not manufacture reviewed status. The runtime must not infer that an artifact is reviewed merely because it is stored in a reviewed-artifact directory or conforms to a schema. Review status must remain an explicit property of the applicable knowledge and governance model. Accordingly: STRUCTURALLY VALID ARTIFACT and: HUMAN-REVIEWED ARTIFACT are distinct states. --- # 49. Reviewed Classification Boundary A reviewed classification represents a classification that has passed through the applicable human-review process. This is more than a technical label. It is part of the institutional knowledge boundary. The runtime can validate whether reviewed-classification information satisfies the applicable artifact contract. It cannot independently confer reviewed status. This preserves the sequence: EVIDENCE -> PROPOSED OR RECORDED INTERPRETATION -> HUMAN REVIEW -> REVIEWED CLASSIFICATION rather than: EVIDENCE -> RUNTIME CLASSIFICATION -> AUTOMATICALLY REVIEWED KNOWLEDGE The second sequence is not authorized. --- # 50. Reviewed Anomaly Knowledge Reviewed anomaly knowledge is knowledge whose evidentiary, provenance, classification, and review context has been preserved through the applicable LEO governance process. It should not be understood as immutable truth. Institutional understanding can change when: new evidence appears; provenance is corrected; a prior classification is challenged; process context changes; an earlier review is found to be incomplete; or a later human review reaches a different justified conclusion. For this reason, reviewed anomaly knowledge must remain auditable and correctable. The objective is not to create an irreversible anomaly label. The objective is to create traceable institutional memory. --- # 51. Institutional Memory Model The Anomaly Library contributes to the longer-term LEO institutional memory model. That memory should grow through reviewed and traceable knowledge rather than through uncontrolled accumulation of model-generated conclusions. The intended principle is: DOCUMENTS ARE EVIDENCE HUMAN-CONFIRMED CLASSIFICATIONS ARE KNOWLEDGE REVIEW HISTORY IS PART OF KNOWLEDGE PROVENANCE CORRECTIONS MUST REMAIN TRACEABLE This model is designed to reduce the risk that institutional memory becomes opaque, detached from evidence, or impossible to audit. The archive and review history therefore remain relevant to the meaning of current knowledge. --- # 52. Correction Without Historical Erasure A reviewed classification may later require correction. Correction should not require destruction of the historical record. A mature reviewed-knowledge system should be able to represent: ORIGINAL EVIDENCE -> ORIGINAL REVIEW -> ORIGINAL CLASSIFICATION -> NEW EVIDENCE OR REVIEW -> CORRECTION -> CURRENT REVIEWED STATE while preserving the fact that the earlier state existed. This supports institutional accountability. It also allows an evaluator or reviewer to determine why knowledge changed. The Anomaly Library architecture therefore favors reversible and auditable review history over silent replacement. The frozen runtime evidence presented here does not claim implementation of every future correction-history capability. It establishes the governance direction within which reviewed runtime behavior must remain compatible. --- # 53. Process Context as Anomaly Context An anomaly cannot be interpreted safely without considering the process in which it appears. Process Mode provides one component of that context. The current canonical states are: DETERMINISTIC_PROCESS STOCHASTIC_PROCESS MIXED_PROCESS UNKNOWN_REQUIRES_REVIEW These states characterize process behavior for the purposes of the applicable LEO Process Mode model. They do not themselves establish anomaly severity. They do not themselves establish misconduct. They do not themselves establish legal or compliance significance. Their purpose in the Anomaly Library context is to improve interpretation. --- # 54. Deterministic Process Context A DETERMINISTIC_PROCESS is a process whose reviewed Process Mode context is dominated by the applicable deterministic characteristics and signals under the Process Mode architecture. Within such a context, deviations from stable rules, procedural controls, or expected repeatability may be relevant to anomaly review. However, a deviation must not automatically be interpreted as misconduct. Possible explanations may include, depending on the evidence: legitimate exception handling; incomplete records; process redesign; authorized override; data-quality problems; implementation defects; misunderstood process scope; or other contextual factors. The Anomaly Library therefore preserves the process classification as context without turning deterministic deviation into an automatic verdict. --- # 55. Stochastic Process Context A STOCHASTIC_PROCESS contains material behavioral variability, human discretion, response uncertainty, or other applicable stochastic characteristics under the reviewed Process Mode model. Variation inside such a process may be expected. Therefore: VARIATION must not automatically become: ANOMALY merely because observed outcomes differ. The evaluator should understand this as one reason Process Mode context is architecturally important. An anomaly model that ignores expected stochastic variation risks producing false significance from normal process behavior. At the same time, stochastic context does not make every observation normal or acceptable. Evidence and human review remain necessary. --- # 56. Mixed Process Context A MIXED_PROCESS contains both deterministic and stochastic signal context under the current Process Mode model. This state is especially important for anomaly interpretation because the same institutional workflow may contain: rule-bound stages; discretionary stages; repeatable controls; variable human responses; deterministic eligibility conditions; stochastic behavioral outcomes; or other combinations of controlled and variable behavior. An anomaly assessment that treats the entire process as purely deterministic may overstate expected variation. An assessment that treats the entire process as purely stochastic may understate deviations from mandatory controls. The MIXED_PROCESS state therefore preserves the need for more careful contextual interpretation. --- # 57. Unknown Process Context UNKNOWN_REQUIRES_REVIEW preserves the state in which the available reviewed Process Mode evidence does not justify a deterministic, stochastic, or mixed proposal under the applicable model. This is a meaningful state. It is not a runtime failure that should automatically be hidden. It is not permission to guess. Within anomaly interpretation, unknown process context should increase the need for review rather than create artificial certainty. The safe relationship is: PROCESS MODE UNKNOWN -> PRESERVE UNCERTAINTY -> REQUIRE REVIEW not: PROCESS MODE UNKNOWN -> SELECT MOST CONVENIENT MODE -> CONTINUE AS CERTAIN --- # 58. Process Mode Is Not a Risk Score The four Process Mode states must not be interpreted as a risk ranking. There is no authorized ordering such as: DETERMINISTIC_PROCESS = LOW RISK MIXED_PROCESS = MEDIUM RISK STOCHASTIC_PROCESS = HIGH RISK or the reverse. Process Mode describes process character. Risk, anomaly significance, compliance relevance, and institutional response are separate questions requiring their own evidence and review. This separation prevents the Process Mode layer from becoming an implicit institutional scoring system. --- # 59. Process Mode Is Not a Legality Classification Process Mode also has no inherent legal meaning. A deterministic process may be lawful or unlawful depending on its substantive rules and institutional context. A stochastic process may be appropriate or inappropriate depending on its purpose, governance, and evidence. A mixed process may be intentionally designed that way. An unknown process classification may simply indicate insufficient reviewed evidence. Therefore: PROCESS MODE != LEGAL STATUS and: PROCESS MODE != COMPLIANCE VERDICT The Anomaly Library Runtime preserves that separation. --- # 60. Transitional States Between Determinism and Stochasticity A significant future evaluation question concerns transitions between deterministic and stochastic process behavior. The current four-state Process Mode model can represent a process whose reviewed evidence supports deterministic signals, stochastic signals, both, or neither. It does not by itself establish a temporal transition model. A temporal transition model would address questions such as: DETERMINISTIC_PROCESS -> DETERMINISTIC PROCESS WITH GROWING DISCRETION -> MIXED_PROCESS -> STOCHASTIC-DOMINANT PROCESS or: STOCHASTIC_PROCESS -> INTRODUCTION OF FORMAL CONTROLS -> MIXED_PROCESS -> DETERMINISTIC-DOMINANT PROCESS These transitions may be institutionally significant. For example, increasing discretionary behavior inside a previously stable rule-bound process may deserve review. Likewise, increasing standardization inside a historically discretionary process may change what constitutes expected variation. However, this evidence package does not claim that such temporal transition states are implemented as a current runtime feature. --- # 61. Requirements for a Future Transition-State Model A defensible transition-state model would require more than simply comparing two Process Mode labels. At minimum, future reviewed architecture would need to address: temporal evidence; comparable observation windows; characteristic-state history; signal history; reviewed Process Mode history; changes in procedural controls; changes in human discretion; changes in behavioral variability; changes in outcome predictability; evidence sufficiency; provenance continuity; confidence interpretation; transition thresholds or transition semantics; human confirmation; correction history; and uncertainty. Without these controls, apparent movement between deterministic and stochastic behavior could reflect: actual process change; changed evidence availability; changed observation scope; data-quality differences; changed reviewers; changed classification criteria; or incomplete provenance. A future implementation must distinguish among these possibilities. --- # 62. Transition Evidence Is Not Transition Verdict Even if future runtime components detect evidence consistent with changing process character, the safe architectural sequence should remain: TEMPORAL EVIDENCE -> CHARACTERISTIC CHANGE EVIDENCE -> SIGNAL CHANGE EVIDENCE -> PROPOSED PROCESS-STATE CHANGE -> HUMAN REVIEW -> REVIEWED TRANSITION INTERPRETATION not: SIGNAL COUNT CHANGED -> PROCESS TRANSITION DECLARED -> ANOMALY DECLARED This distinction is necessary because changes in measured signals may have multiple causes. The current runtime evidence package therefore preserves transitional process behavior as an explicit future architectural concern without presenting it as completed functionality. --- # 63. Why Transitional Context Matters to the Anomaly Library Transition-state analysis is relevant to the Anomaly Library because anomaly meaning can depend on whether process behavior itself is changing. Consider a process that historically operates under stable procedural controls. If reviewed evidence over time begins to show increasing human discretion, behavioral variability, or response uncertainty, a reviewer may need to ask whether: the process was intentionally redesigned; controls weakened; an exceptional operating condition exists; evidence coverage changed; or an institutional anomaly is developing. The Anomaly Library could eventually preserve reviewed knowledge about such changes. But the runtime must not assume that every transition is anomalous. Some transitions are legitimate. Some are planned. Some reflect institutional adaptation. Some reflect incomplete observation. Therefore the transition itself is evidence for review, not an automatic negative conclusion. --- # 64. Evidence-Derived Characteristics and Anomaly Interpretation The current Process Mode architecture distinguishes source evidence from evidence-derived characteristics. This distinction should also be preserved when process context is used for anomaly interpretation. The canonical Process Mode signal characteristics include deterministic-side characteristics such as: rule_stability procedural_control outcome_predictability repeatability and stochastic-side characteristics such as: human_discretion behavioral_variability response_uncertainty auditability remains a non-signal characteristic in the current reviewed Process Mode mapping. These characteristics are not themselves anomaly verdicts. They are evidence-derived process characteristics used within the applicable Process Mode interpretation model. --- # 65. Signal Eligibility Boundary Not every characteristic state may contribute a positive Process Mode signal. Within the current reviewed Process Mode model, only signal-eligible characteristics in the applicable positive supported state should contribute positive signals. Non-positive or unresolved states remain distinct. The current model preserves distinctions among states including: SUPPORTED NOT_SUPPORTED NOT_OBSERVED UNKNOWN CONFLICTING These states must not be collapsed. In particular: NOT_SUPPORTED does not automatically mean that the opposite characteristic is supported. Likewise: NOT_OBSERVED does not mean false. UNKNOWN does not mean negative. CONFLICTING does not authorize selection of whichever interpretation is more convenient. This boundary is relevant to anomaly interpretation because weak Process Mode evidence must not be converted into artificially strong anomaly context. --- # 66. No Automatic Opposite Signal The Process Mode evidence model does not treat failure to support one side as automatic support for the other side. Conceptually: DETERMINISTIC CHARACTERISTIC NOT_SUPPORTED != STOCHASTIC SIGNAL and: STOCHASTIC CHARACTERISTIC NOT_SUPPORTED != DETERMINISTIC SIGNAL This prevents binary inversion from creating evidence that was never observed. The same principle is important to anomaly reasoning. Absence of evidence supporting one interpretation does not automatically create evidence supporting its opposite. --- # 67. Signal Counts as Process Evidence The Process Mode classifier consumes deterministic and stochastic signal counts within its reviewed classification model. The current reviewed classifier behavior distinguishes: d > 0, s = 0 -> DETERMINISTIC_PROCESS d > 0, s > 0 -> MIXED_PROCESS d = 0, s > 0 -> STOCHASTIC_PROCESS d = 0, s = 0 -> UNKNOWN_REQUIRES_REVIEW These counts are process-classification inputs. They are not anomaly counts. They are not fraud scores. They are not risk scores. They are not legal scores. The Anomaly Library must preserve this semantic boundary whenever Process Mode context is referenced. --- # 68. Process Mode Confidence and Anomaly Confidence Confidence associated with Process Mode interpretation must not automatically become confidence in an anomaly interpretation. These are different claims. For example, a reviewer may have strong evidence that a process is deterministic while having weak evidence that a particular deviation is institutionally significant. Likewise, a process may be confidently classified as stochastic while a specific observation still warrants detailed review. Therefore: HIGH PROCESS MODE CONFIDENCE != HIGH ANOMALY CONFIDENCE and: LOW PROCESS MODE CONFIDENCE != NO ANOMALY A future anomaly-confidence model, if implemented, would require its own reviewed evidence contract. --- # 69. Anomaly Categories Are Not Severity Scores Anomaly categories provide classification structure. They must not be interpreted automatically as severity levels unless a separate reviewed model explicitly establishes such semantics. A category may describe the nature of an observed or reviewed condition. Severity may depend on additional evidence, institutional context, recurrence, scope, affected controls, consequences, and human judgment. Accordingly: ANOMALY CATEGORY != AUTOMATIC SEVERITY and: ANOMALY CATEGORY != AUTOMATIC ENFORCEMENT PRIORITY The frozen runtime does not create such authority. --- # 70. Reviewed Anomalies and Recurrence One future value of an institutional Anomaly Library is the ability to compare new review contexts with prior human-reviewed anomaly knowledge. Such comparison could help reviewers understand: whether a condition has occurred before; how it was previously interpreted; what evidence supported the prior interpretation; whether corrective action was recorded; whether the condition later recurred; and whether institutional understanding changed. However, similarity to a prior reviewed anomaly must not automatically create the same classification. Context may differ. Evidence may differ. Process Mode may differ. Institutional rules may have changed. The earlier review may later have been corrected. Historical anomaly knowledge therefore supports review; it does not replace review. --- # 71. Reviewed Anomalies and Prevention Reviewed anomaly knowledge may eventually support institutional prevention. For example, reviewed history may reveal that particular control failures, documentation gaps, process transitions, or evidence-quality problems recur. This can inform human decisions about: process redesign; additional controls; documentation improvements; review priorities; training; monitoring; or future investigation. The frozen runtime baseline does not autonomously implement such corrective actions. It preserves the knowledge and review context necessary for humans to make those decisions. This is consistent with the LEO principle: NO AUTOMATIC CORRECTION NO AUTONOMOUS ENFORCEMENT --- # 72. Reviewed Knowledge Does Not Become Immutable Truth The term reviewed knowledge does not mean that an institutional conclusion can never be challenged. Human review can be wrong. Evidence can be incomplete. New evidence can appear. Provenance can be corrected. Institutional rules can change. Interpretive frameworks can improve. For that reason, the Anomaly Library must support a governance model in which reviewed knowledge remains: traceable; attributable; challengeable; correctable; historically inspectable; and connected to its evidence. This is more defensible than treating a machine-readable classification as permanent truth. --- # 73. Conflicting Evidence Conflicting evidence must remain visible. The runtime must not silently resolve substantive evidentiary conflicts by discarding one side. A conflict may indicate: different source perspectives; different time periods; inconsistent records; changed process conditions; data-quality problems; incomplete provenance; or genuine institutional disagreement. The safe handling pattern is: CONFLICTING EVIDENCE -> PRESERVE CONFLICT -> VALIDATE AVAILABLE RELATIONSHIPS -> SURFACE FOR REVIEW not: CONFLICTING EVIDENCE -> AUTOMATICALLY SELECT ONE SOURCE -> HIDE CONFLICT This is particularly important where anomaly-related conclusions could affect institutional or individual interests. --- # 74. Missing Evidence Missing evidence is also a review condition rather than an automatic verdict. The absence of an expected record may be relevant. But multiple explanations may exist. The runtime must therefore avoid: EXPECTED EVIDENCE MISSING -> FRAUD as an autonomous rule. The appropriate response within the reviewed boundary is to preserve the missing-evidence condition and make it inspectable. Human reviewers can then consider the institutional context. --- # 75. Unknown States Unknown states are legitimate states in the LEO evidence model. They prevent insufficient evidence from being transformed into artificial certainty. This principle applies across: Process Mode classification; evidence-derived characteristics; provenance; anomaly interpretation; and review status. An UNKNOWN state should therefore be preserved when the applicable evidence does not support a stronger state. Unknown does not mean failure of the system. In many institutional contexts, correctly preserving uncertainty is safer than producing a confident but unsupported answer. --- # 76. Human Review as Knowledge Conversion Boundary Human review is the principal boundary between machine-supported evidence processing and institutionally accepted reviewed knowledge. The runtime can: LOAD VALIDATE RELATE PRESERVE ORGANIZE REPORT but the transition to institutionally significant reviewed knowledge requires human review under the applicable governance model. This is why Human Review Package generation is a central runtime capability. The package is not simply a report. It is the controlled handoff between runtime validation and human judgment. --- # 77. No Autonomous Learning From Anomalies The frozen runtime is not authorized to learn autonomously from anomaly artifacts. This prohibition is architectural, not merely temporary implementation absence. A new artifact must not automatically alter institutional anomaly knowledge simply because it resembles a previously reviewed case. A new runtime observation must not silently change classification rules. A model-generated interpretation must not silently become institutional memory. The controlled knowledge-growth model is instead: NEW EVIDENCE -> RUNTIME VALIDATION -> HUMAN REVIEW -> HUMAN-CONFIRMED CLASSIFICATION -> TRACEABLE REVIEWED KNOWLEDGE Any future learning mechanism must remain compatible with this human-controlled boundary unless a separately reviewed architecture explicitly changes it. --- # 78. No Autonomous Enforcement From Anomalies The presence of an anomaly-related condition does not authorize enforcement. The runtime is not authorized to: block a transaction; suspend a person; reject an application; alter an institutional record; impose a sanction; accuse a party of misconduct; or trigger irreversible institutional action autonomously. Such actions require separate institutional authority and governance. The Anomaly Library Runtime supplies evidence and review support. It does not become an enforcement authority. --- # 79. No Fraud Verdict An anomaly is not synonymous with fraud. Fraud is a materially stronger concept involving legal, factual, and institutional considerations outside the authority of this runtime baseline. An observed inconsistency may result from: error; missing information; process variation; legitimate exception; misunderstanding; system defect; procedural weakness; conflicting records; or intentional misconduct. The runtime cannot autonomously determine which explanation is legally or institutionally correct. Therefore: ANOMALY != FRAUD is a mandatory public-evaluation boundary. --- # 80. No Legal Verdict The runtime also has no legal adjudication authority. It may preserve evidence relevant to legal or compliance review. It may validate artifacts used by human reviewers. It may expose inconsistencies requiring further examination. It may not autonomously determine: criminal liability; civil liability; regulatory breach; contractual liability; legal guilt; legal innocence; or final compliance status. Any legal interpretation requires appropriate human authority and applicable legal process. --- # 81. Evidence Before Escalation The architecture favors evidence preservation before institutional escalation. Where an anomaly-related condition is identified within the reviewed runtime scope, the first concern is not automatic action. The first concern is whether the evidence and its relationships can be inspected reliably. The sequence should therefore remain conceptually: OBSERVATION -> EVIDENCE -> LINEAGE -> PROVENANCE -> PROCESS CONTEXT -> VALIDATION -> HUMAN REVIEW -> AUTHORIZED HUMAN DECISION This reduces the risk of acting on opaque or unsupported machine interpretation. --- # 82. Evaluator Interpretation of Reviewed Anomaly Knowledge For public evaluation, reviewed anomaly knowledge should be understood as: traceable, evidence-connected, provenance-preserving anomaly-related institutional knowledge whose reviewed status depends on explicit human confirmation rather than autonomous runtime inference. This definition intentionally avoids describing the Anomaly Library as a database of proven wrongdoing. It is not such a database. It is a controlled knowledge layer for reviewed anomaly context. --- # 83. Evidence and Knowledge Assessment Within the bounded public evaluation scope, the evidence-control model supports the following assessment: SOURCE_EVIDENCE_SEPARATION = PRESERVED EVIDENCE_LINEAGE = REQUIRED PROVENANCE = REQUIRED REVIEWED_STATUS = EXPLICIT STRUCTURAL_VALIDITY_IS_NOT_SUBSTANTIVE_TRUTH = PRESERVED PROCESS_MODE_CONTEXT = PRESERVED PROCESS_MODE_IS_NOT_RISK_SCORE = PRESERVED PROCESS_MODE_IS_NOT_LEGAL_STATUS = PRESERVED TRANSITION_STATE_MODEL = NOT_CLAIMED_AS_IMPLEMENTED NON_POSITIVE_CHARACTERISTIC_STATES = PRESERVED AUTOMATIC_OPPOSITE_SIGNAL = PROHIBITED PROCESS_SIGNAL_COUNTS_ARE_NOT_ANOMALY_SCORES = PRESERVED REVIEWED_ANOMALY_KNOWLEDGE = HUMAN_CONTROLLED CONFLICTING_EVIDENCE = REVIEW_RELEVANT MISSING_EVIDENCE = REVIEW_RELEVANT UNKNOWN_STATE = PRESERVED AUTONOMOUS_ANOMALY_LEARNING = NOT AUTHORIZED AUTONOMOUS_ENFORCEMENT = NOT AUTHORIZED FRAUD_VERDICT_AUTHORITY = ABSENT LEGAL_VERDICT_AUTHORITY = ABSENT --- # 84. Part 3 Assessment Part 3 establishes the evidence and knowledge boundary necessary for safe interpretation of the Anomaly Library Runtime. The principal evaluator-facing conclusion is: ANOMALY LIBRARY != AUTONOMOUS ANOMALY VERDICT ENGINE The reviewed architecture instead follows: EVIDENCE + LINEAGE + PROVENANCE + PROCESS CONTEXT + REVIEWED CLASSIFICATION CONTEXT + RUNTIME VALIDATION + HUMAN REVIEW -> TRACEABLE REVIEWED ANOMALY KNOWLEDGE The Process Mode relationship is contextual rather than determinative. The current four-state Process Mode model is preserved. The possibility of transitions between deterministic and stochastic process behavior is explicitly recognized as evaluator-significant future work, but a complete temporal transition-state model is not claimed as implemented by the current frozen runtime baseline. This preserves both current evidence accuracy and the architectural direction needed for subsequent reviewed development. --- # 85. Human Governance Architecture The Anomaly Library Runtime operates under explicit human governance. Human governance is not an optional layer added after runtime interpretation. It is part of the architecture that determines what the runtime is permitted to do and what institutional meaning may be assigned to its outputs. The governing sequence is: ```text EVIDENCE -> RUNTIME PROCESSING -> VALIDATION -> REVIEW MATERIAL -> HUMAN REVIEW -> AUTHORIZED HUMAN DECISION ``` The runtime does not replace the final human decision boundary. This remains true even where runtime behavior is deterministic, reproducible, and fully test-backed within its defined implementation scope. --- # 86. Human Review Required Human review is required before anomaly-related runtime output can become an institutionally accepted reviewed conclusion. This requirement protects against several classes of error: incomplete evidence; incorrect provenance; misunderstood process context; schema-valid but substantively incorrect artifacts; inappropriate analogy with historical cases; unresolved conflicting evidence; missing contextual information; and overinterpretation of runtime-generated structure. Accordingly: RUNTIME OUTPUT != FINAL INSTITUTIONAL DECISION and: RUNTIME VALIDATION PASS != HUMAN REVIEW PASS The evaluator should treat this distinction as a central safety property of the current LEO method. --- # 87. Human Review Is Not a Cosmetic Approval Step Human review must not be interpreted as a ceremonial confirmation of a machine-generated conclusion. A meaningful reviewer must be able to: inspect the relevant evidence; inspect provenance; inspect evidence lineage; understand process context; identify uncertainty; inspect conflicting evidence; challenge runtime interpretation; reject a proposed interpretation; request additional evidence; correct classification; and preserve the resulting review history. The architecture therefore requires human review to remain substantively capable of changing the outcome. A system in which the reviewer can only approve a predetermined machine result would not satisfy this governance objective. --- # 88. Review Authority Review authority belongs to authorized humans or institutions operating under the applicable governance framework. The runtime itself has no institutional review authority. It may support the reviewer by producing structured material. It may validate whether required artifact relationships exist. It may expose missing or conflicting information. It may organize prior reviewed anomaly knowledge. It may not confer authority on itself. Therefore: RUNTIME CAPABILITY != INSTITUTIONAL AUTHORITY This distinction remains applicable regardless of technical accuracy. --- # 89. Human Review Package The Human Review Package is the primary controlled interface between runtime processing and human decision-making. Its purpose is to make the relevant material inspectable rather than to predetermine the review result. A review package should preserve, where applicable: artifact identity; anomaly-related classification context; evidence references; provenance; evidence lineage; Process Mode context; validation results; unresolved conditions; relevant reviewed knowledge references; and explicit governance boundaries. The package should allow a reviewer to understand why the material has been presented for review. It should not conceal uncertainty in order to create a cleaner narrative. --- # 90. Review Package Is Not a Verdict Package The existence of a Human Review Package does not mean that the runtime has already reached a substantive institutional conclusion. The safe semantic model is: RUNTIME -> PREPARES REVIEWABLE MATERIAL -> HUMAN REVIEWS not: RUNTIME -> DECIDES -> HUMAN SIGNS This distinction is important for external evaluation because the technical ability to produce structured anomaly information should not be mistaken for delegated institutional decision authority. --- # 91. Reviewer Access to Evidence A reviewer must be able to connect anomaly-related information to the evidence necessary to assess it. The architecture therefore favors inspectable evidence references over opaque machine assertions. Where a review package refers to a source artifact, the relationship should remain traceable under the applicable evidence and provenance model. Where evidence is unavailable, the package should preserve that limitation. Where evidence conflicts, the conflict should remain visible. The runtime must not generate synthetic evidentiary certainty merely to make a review package appear complete. --- # 92. Reviewer Access to Process Context The reviewer should also have access to the applicable Process Mode context where it materially affects anomaly interpretation. This includes the distinction among: DETERMINISTIC_PROCESS STOCHASTIC_PROCESS MIXED_PROCESS UNKNOWN_REQUIRES_REVIEW The reviewer must not be expected to interpret process deviations without knowing whether the relevant process was classified as rule-dominant, variability-dominant, mixed, or insufficiently established. However, Process Mode remains contextual evidence. The reviewer retains authority to challenge the applicability or sufficiency of that context. --- # 93. Reviewer Access to Uncertainty Uncertainty must be visible to the human reviewer. This includes uncertainty resulting from: missing evidence; incomplete provenance; conflicting evidence; unknown characteristic states; insufficient Process Mode evidence; incomplete historical comparison; or unresolved anomaly classification context. A review interface that hides uncertainty would undermine the human-control model. The architecture therefore treats uncertainty as review-relevant information, not as a presentation defect to be removed automatically. --- # 94. Reviewer Access to Conflicting Evidence Where conflicting evidence exists, the reviewer should be able to inspect the conflict rather than receiving only a runtime-selected interpretation. The runtime must not silently optimize for internal consistency by removing evidence that does not fit a preferred anomaly narrative. A safe review pattern is: SOURCE A \ -> CONFLICT PRESERVED -> HUMAN REVIEW / SOURCE B The human reviewer may then determine whether the conflict can be resolved, requires additional evidence, or must remain unresolved. --- # 95. Reviewer Rejection Authority Human review must include the ability to reject runtime-supported interpretation. A reviewer may determine that: the evidence is insufficient; the anomaly classification is unsupported; the Process Mode context is inappropriate; the evidence lineage is incomplete; provenance is inadequate; the historical comparison is misleading; additional investigation is necessary; or no anomaly-related conclusion should be accepted. The runtime must not convert reviewer rejection into an error condition that is automatically overridden. Rejection is a valid governance outcome. --- # 96. Reviewer Correction Authority Human review must also support correction. A reviewer may identify: incorrect metadata; incorrect classification; incorrect process context; incorrect evidence linkage; incomplete provenance; missing evidence; or an earlier reviewed interpretation that requires revision. Correction must remain attributable and traceable under the broader LEO institutional memory model. The objective is not merely to produce a corrected current value. The objective is to preserve how and why the reviewed state changed. --- # 97. Reviewer Deferral Authority A reviewer may also defer a decision. Deferral is appropriate where evidence is insufficient or where additional institutional authority is required. The architecture must therefore allow a meaningful state equivalent to: INSUFFICIENT BASIS FOR CURRENT DECISION -> PRESERVE MATERIAL -> REQUEST OR AWAIT ADDITIONAL REVIEW The runtime must not force a binary conclusion merely because processing has completed successfully. Technical completion and substantive review completion are different states. --- # 98. Reviewer Escalation Authority A human reviewer may determine that material requires escalation to another authorized human, institutional function, legal authority, compliance function, or specialist. The runtime may support documentation of that review state where explicitly implemented. It does not independently decide institutional escalation consequences. The distinction is: RUNTIME SUPPORTS REVIEW -> HUMAN IDENTIFIES NEED FOR ESCALATION -> AUTHORIZED GOVERNANCE PROCESS rather than: RUNTIME DETECTS CONDITION -> AUTOMATIC SANCTION OR ENFORCEMENT --- # 99. Separation of Technical Validation and Institutional Judgment Technical validation answers questions about artifact and runtime contract conformance. Institutional judgment answers materially different questions. For example, technical validation may establish that: a required field exists; an identifier conforms to a schema; a referenced artifact can be resolved; a Process Mode value is from the allowed set; or a review package can be generated reproducibly. Technical validation does not establish that: an individual acted improperly; a process should be sanctioned; a payment should be blocked; a legal violation occurred; or an institution must take a particular action. The evaluator should preserve this separation when interpreting runtime test evidence. --- # 100. Structural Validity Is Not Substantive Correctness A structurally valid anomaly artifact may still contain a substantively incorrect interpretation. This is a general limitation of schema validation. Conceptually: SCHEMA VALID != FACTUALLY CORRECT and: REFERENCE RESOLVES != REFERENCE PROVES CLAIM and: PROCESS MODE VALUE VALID != PROCESS MODE CLASSIFICATION SUBSTANTIVELY CORRECT Human review remains necessary because runtime structural guarantees and institutional substantive correctness are different properties. --- # 101. Deterministic Runtime Does Not Mean Deterministic Institution Parts of the runtime may behave deterministically. Given the same valid input and controlled implementation baseline, they may produce the same validation or transformation result. This does not imply that the institution itself is deterministic. Nor does it imply that every human-reviewed anomaly question has a single machine-computable answer. The distinction is: DETERMINISTIC SOFTWARE OPERATION != DETERMINISTIC INSTITUTIONAL REALITY This is especially important where the runtime operates on stochastic or mixed human processes. --- # 102. Runtime Reproducibility Reproducibility is an important engineering property of the public evaluation baseline. Where the runtime performs a defined validation or transformation, evaluators should be able to inspect the relevant implementation and test evidence within the published scope. Reproducibility supports: engineering review; defect identification; evidence comparison; auditability; and controlled evaluation. It does not create substantive institutional authority. A reproducible wrong assumption remains wrong. A reproducible incomplete model remains incomplete. Therefore reproducibility must be evaluated together with scope and governance boundaries. --- # 103. Read-Only Governance Principle The current evaluation baseline must be interpreted under a read-only governance principle unless a separately authorized operation explicitly provides otherwise. The Anomaly Library Runtime evidence package does not authorize production mutation. It does not authorize modification of source institutional evidence. It does not authorize modification of external systems. It does not authorize enforcement actions. The public evaluator-facing baseline is evidence of implemented behavior within a controlled scope, not evidence of unrestricted operational authority. --- # 104. No Production Mutation Production mutation is not authorized by this evidence package. The runtime evidence must not be interpreted as permission to: alter production records; modify institutional decisions; rewrite source evidence; change payment status; modify case status; alter user permissions; or perform equivalent operational actions. Any future production mutation capability would require separate architecture, authorization, controls, testing, review, and governance. It cannot be inferred from the existence of anomaly-processing code. --- # 105. No Autonomous Enforcement Autonomous enforcement remains outside the authorized runtime boundary. This includes automatic actions against: individuals; organizations; applications; transactions; grants; procurement processes; approvals; or institutional records. The runtime may provide evidence for review. It may not convert that evidence into autonomous coercive action. This is a mandatory LEO governance constraint. --- # 106. No Autonomous Sanction An anomaly-related classification must not automatically trigger a sanction. Sanctions may have legal, financial, professional, contractual, or institutional consequences. Such consequences require appropriate authority and due process outside the runtime's current scope. Therefore: ANOMALY-RELATED OUTPUT != SANCTION AUTHORIZATION The public evaluation package must not imply otherwise. --- # 107. No Autonomous Accusation The runtime is not authorized to accuse a person or organization of fraud, corruption, misconduct, criminal behavior, or legal violation. It may identify evidence requiring review. It may preserve anomaly-related classifications. It may show inconsistencies. It may organize reviewed institutional knowledge. But language representing guilt or culpability requires human and, where applicable, legal authority. This protects both institutional integrity and affected parties. --- # 108. No Autonomous Exoneration The same boundary applies in the opposite direction. The absence of a detected anomaly must not be interpreted as autonomous proof that: no misconduct occurred; no fraud occurred; no legal violation occurred; the process was fully compliant; or the evidence was complete. Therefore: NO RUNTIME ANOMALY != PROOF OF NO WRONGDOING The runtime is an evidence-review support mechanism, not an omniscient institutional adjudicator. --- # 109. No Autonomous Legal Interpretation Legal meaning must not be generated as an authoritative runtime conclusion. The runtime may preserve information relevant to legal review. It may help organize evidence. It may expose relationships. It may support authorized human reviewers. It may not determine the final interpretation of legislation, regulation, contractual obligations, or legal liability. Where legal significance is material, appropriate legal review remains necessary. --- # 110. No Autonomous Compliance Certification The runtime also does not provide autonomous compliance certification. Evidence that a runtime component follows a documented governance boundary is not equivalent to certification that an organization, system, process, or institution complies with all applicable regulation. Accordingly: GOVERNANCE-AWARE DESIGN != REGULATORY CERTIFICATION and: CONTROLLED RUNTIME EVIDENCE != COMPLETE COMPLIANCE ASSESSMENT Any stronger claim would require separate evidence and appropriate authority. --- # 111. No Autonomous Policy Creation The Anomaly Library Runtime does not create institutional policy. Historical reviewed cases may reveal patterns. Runtime evidence may reveal recurring conditions. Such information may support human policy development. It does not authorize the runtime to convert patterns into binding rules. Policy creation remains a human governance function. --- # 112. No Autonomous Rule Expansion The runtime must not silently expand anomaly classification rules based on newly observed cases. A reviewed anomaly example does not automatically become a new universal rule. A repeated pattern does not automatically become a mandatory institutional control. Any rule expansion requires explicit reviewed architecture and governance. This preserves the distinction between: LEARNING ABOUT REVIEWED HISTORY and: AUTONOMOUSLY CREATING INSTITUTIONAL RULES --- # 113. No Silent Knowledge Mutation Reviewed anomaly knowledge must not change silently. Any accepted correction, reclassification, or knowledge update should remain traceable under the applicable review and provenance model. Silent mutation would damage institutional memory because a future reviewer could no longer determine: what was previously believed; what changed; why it changed; who reviewed the change; and which evidence supported it. The architecture therefore favors explicit state transitions over hidden replacement. --- # 114. No Evidence Destruction The runtime must not destroy source evidence because it conflicts with a reviewed anomaly interpretation. Likewise, superseded reviewed knowledge should not require destruction of the historical review record. Retention policy itself may be governed by applicable institutional and legal requirements. Within the LEO architectural principle, however, correction and review must not be implemented by silently erasing provenance-relevant history. --- # 115. No Provenance Rewriting Provenance must not be rewritten to make an artifact appear more authoritative than the available record supports. Examples of prohibited conceptual behavior include: UNKNOWN SOURCE -> GENERATED SOURCE ATTRIBUTION or: UNREVIEWED ARTIFACT -> GENERATED REVIEW HISTORY or: BROKEN LINEAGE -> INVENTED LINEAGE Missing or uncertain provenance should remain visible for review. --- # 116. No Automatic Resolution of Conflicts Conflicting evidence may require human judgment. The runtime may validate that a conflict exists. It may organize the relevant evidence. It may expose differences. It must not automatically resolve substantive conflicts unless a specifically reviewed deterministic rule legitimately governs the narrow technical condition being processed. Even then, technical resolution of a deterministic field conflict must not be misrepresented as resolution of the broader institutional dispute. --- # 117. No Automatic Severity Escalation The runtime must not infer that recurrence automatically increases severity. Repeated conditions may be important, but recurrence can have different meanings. It may indicate: repeated error; repeated legitimate exception; unchanged data-quality problems; systemic process weakness; recurring misconduct; or incomplete historical understanding. The runtime may surface recurrence. Humans determine its institutional significance. --- # 118. No Automatic Risk Ranking The current Anomaly Library Runtime evidence does not establish an autonomous risk-ranking system. Anomaly categories, Process Mode, recurrence, evidence completeness, and review history may all be relevant to future risk analysis. But combining them into a risk score would constitute a separate model with separate assumptions and governance consequences. No such complete autonomous risk authority should be inferred from the current baseline. --- # 119. No Automatic Individual Profiling The Anomaly Library is not presented as an autonomous profiling mechanism for individuals. The architectural object of review is evidence and institutional process context. Any future use involving individual-level profiling would require separate legal, ethical, evidentiary, and governance review. The current public evidence package does not authorize such use. --- # 120. No Automatic Institutional Profiling The same caution applies to institutions. Anomaly history must not automatically become an institutional reputation score. Different institutions may operate under different rules, evidence quality, process structures, legal frameworks, and review practices. Historical anomaly counts without context could therefore be misleading. The current runtime does not claim authority to generate such institutional ratings. --- # 121. Human Accountability Human control does not mean that humans may act without accountability. A robust human-review model should preserve enough review history to understand how consequential decisions were reached. Where supported by the applicable implementation and governance model, this includes attribution of review actions and preservation of relevant review context. The objective is a controlled relationship: MACHINE SUPPORT + HUMAN AUTHORITY + TRACEABLE REVIEW rather than either machine autonomy or untraceable human discretion. --- # 122. Human Review and Automation Bias Human review alone does not eliminate risk. Reviewers may over-trust structured machine output, especially where the runtime appears technically precise. The architecture therefore avoids presenting runtime output as a final verdict. Explicit uncertainty, provenance, evidence lineage, and governance markers help reduce the risk that reviewers interpret machine-generated structure as institutional truth. This remains an important evaluation consideration even where runtime tests pass completely. --- # 123. Human Review and Confirmation Bias Reviewers may also interpret evidence in ways that reinforce prior expectations. Preserving conflicting evidence and historical corrections helps reduce this risk. The Anomaly Library should therefore not become a repository containing only evidence that supports previously accepted classifications. Institutional memory is more useful when it preserves the evidence and review history necessary to challenge earlier understanding. --- # 124. Human Review and Institutional Bias Institutional decisions may themselves contain systematic bias. For that reason, reviewed knowledge should remain attributable and correctable. Human confirmation is necessary for institutional authority, but human confirmation is not equivalent to infallibility. The architecture therefore combines human authority with: evidence preservation; provenance; review history; correction capability; and auditability. This is a stronger control model than either autonomous machine judgment or unrecorded human judgment alone. --- # 125. Review Reversibility Where institutional and legal constraints permit, reviewed anomaly classifications should be capable of correction without destroying historical traceability. Reversibility supports: correction of mistakes; response to new evidence; review of changed process context; and institutional learning. Reversibility does not mean arbitrary alteration. Changes must remain governed and attributable. --- # 126. Auditability of Review The review process should be auditable to the extent defined by the applicable runtime and governance model. Auditability means that a later authorized reviewer can understand the relevant relationship among: EVIDENCE -> RUNTIME OUTPUT -> HUMAN REVIEW -> REVIEWED STATE It does not require that every internal deliberation be publicly exposed. Public evaluation and institutional audit are different disclosure contexts. The public package should expose enough evidence to evaluate the method without claiming unrestricted access to every institutional artifact. --- # 127. Public Evaluation Boundary The evaluator-facing package is intentionally bounded. It demonstrates selected architecture, runtime behavior, test evidence, governance constraints, and review relationships. It does not claim to expose: every internal LEO artifact; every historical review; every institutional record; every future architecture; or every possible runtime integration. This boundary is necessary both for accurate evaluation and for controlled publication. --- # 128. Public Evidence Versus Production Evidence Public evidence and production evidence are different artifact classes. A public evaluation artifact may demonstrate that a method or runtime behavior exists. It does not establish that the same artifact is a production record. Likewise, public test fixtures and reviewed examples must not automatically be treated as live institutional cases. The evaluator should therefore preserve the distinction: PUBLIC EVALUATION EVIDENCE != PRODUCTION INSTITUTIONAL DATA --- # 129. Demonstration Versus Authorization A demonstrated capability is not automatically an authorized capability. For example, a runtime may demonstrate that it can: validate an anomaly artifact; resolve a reference; construct a review package; or compare controlled knowledge structures. That demonstration does not automatically authorize its use in production. Authorization requires separate governance. --- # 130. Implementation Versus Deployment Implementation and deployment are also different states. A capability may be: ARCHITECTURALLY DEFINED -> IMPLEMENTED -> TESTED -> FROZEN without being: PRODUCTION DEPLOYED The public evaluation package must not collapse these maturity states. This is particularly important for Commission-facing review because engineering maturity should be presented accurately rather than maximized through ambiguous language. --- # 131. Deployment Versus Institutional Authority Even deployment would not automatically create institutional authority. A deployed system may still operate only as advisory or review-support infrastructure. Institutional authority depends on governance, law, organizational mandate, and human decision structures. Therefore: DEPLOYED != AUTHORIZED TO ENFORCE This distinction remains part of the LEO governance model. --- # 132. Safety Through Explicit Non-Goals The runtime safety model is strengthened by explicit non-goals. The current Anomaly Library Runtime is not intended to be: an autonomous fraud detector issuing verdicts; an autonomous legal decision system; an autonomous sanction engine; an autonomous compliance certification authority; an autonomous institutional policy generator; an autonomous production mutation engine; or an autonomous replacement for human review. These are architectural boundaries rather than marketing qualifications. --- # 133. Failure Should Prefer Review Over Unsupported Certainty Where the runtime cannot establish the conditions necessary for a safe interpretation, failure behavior should preserve the need for review. Conceptually: INSUFFICIENT EVIDENCE -> REVIEW UNKNOWN PROCESS CONTEXT -> REVIEW CONFLICTING PROVENANCE -> REVIEW INVALID REVIEW ARTIFACT -> REJECT OR REVIEW The runtime should not repair substantive uncertainty by inventing certainty. --- # 134. Fail-Closed Versus Fail-Review Not every anomaly-related uncertainty should be described as a traditional technical fail-closed condition. In many institutional review contexts, the correct behavior is better described as: FAIL TO AUTOMATICALLY CONCLUDE -> PRESERVE STATE -> REQUIRE HUMAN REVIEW This can be understood as a fail-review governance pattern. The term does not imply that every runtime error is handled identically. It describes the broader principle that insufficient evidentiary basis should prevent autonomous substantive conclusion. --- # 135. Safety of Unknown States UNKNOWN_REQUIRES_REVIEW is one concrete expression of this principle within Process Mode. Unknown states protect against forced classification. The same design philosophy applies to anomaly knowledge: UNKNOWN -> PRESERVE UNKNOWN rather than: UNKNOWN -> INFER MOST LIKELY INSTITUTIONAL CONCLUSION This is particularly important where consequences could affect rights, funding, reputation, access, or institutional standing. --- # 136. Safety of Mixed Process Interpretation MIXED_PROCESS also serves a safety function. It prevents the system from forcing a process containing both deterministic and stochastic characteristics into an oversimplified binary model. For anomaly analysis, this matters because deterministic deviations and expected stochastic variation may coexist in the same process. Human review can then examine which part of the process is relevant to the observed condition. --- # 137. Safety of Future Transitional-State Analysis Future transitional-state analysis must preserve the same governance principles. If LEO later evaluates movement between deterministic and stochastic process behavior, the runtime should not autonomously conclude that such movement is negative. A transition may represent: legitimate institutional adaptation; increased formalization; emergency procedure; deliberate delegation; process degradation; evidence-quality change; or another condition. Transition evidence therefore requires interpretation and human review. This future direction must remain compatible with: TRANSITION DETECTION != TRANSITION VERDICT --- # 138. Safety of Historical Comparison Historical reviewed anomalies can support current review. They can also mislead if context is ignored. A prior anomaly may have occurred: under different rules; under a different Process Mode; with different evidence; in a different institutional setting; before a process redesign; or before correction of earlier knowledge. The runtime must therefore avoid treating historical similarity as automatic substantive equivalence. --- # 139. Safety of Pattern Recognition Pattern recognition may help identify cases deserving human attention. But a pattern is not self-interpreting. Repeated structure may reflect: a genuine recurring anomaly; a normal recurring process feature; a recurring data-quality problem; a recurring documentation convention; or an artifact of the analysis method. Any future pattern-recognition capability must preserve evidence, provenance, process context, and human review. --- # 140. Safety of Confidence Values Confidence values can create a false impression of mathematical certainty. Where confidence is used, the evaluator must understand what proposition the confidence applies to. Process Mode confidence, evidence completeness, anomaly classification confidence, and legal confidence are not interchangeable. The runtime must not allow a confidence value from one layer to silently authorize conclusions in another layer. --- # 141. Safety of Quantification Counts and metrics must preserve their semantics. For example: DETERMINISTIC_SIGNAL_COUNT is not: COMPLIANCE_SCORE and: STOCHASTIC_SIGNAL_COUNT is not: FRAUD_RISK_SCORE Likewise, anomaly counts alone are not institutional risk scores. Quantification can support review only when its meaning remains explicit. --- # 142. Safety of Runtime Tests Passing tests demonstrate behavior covered by those tests. They do not prove complete institutional safety. Test evidence should therefore be interpreted as: TESTED BEHAVIOR WITHIN DEFINED SCOPE not: ALL POSSIBLE INSTITUTIONAL USE IS SAFE This evidence package uses test-backed runtime behavior as engineering evidence while preserving broader governance limitations. --- # 143. Safety of Architectural Documentation Architecture documentation defines intended contracts and boundaries. Documentation alone does not prove implementation. For this reason, the evaluator-facing package distinguishes architectural claims from runtime evidence. Where implementation exists, the strongest public claim should connect: ARCHITECTURE + CODE + TESTS + REVIEW EVIDENCE Where that chain is incomplete, the claim should be narrowed accordingly. --- # 144. Safety of Runtime Evidence Runtime evidence also has limits. Executable behavior can prove that a component performs a defined operation. It cannot by itself prove that the operation is institutionally appropriate for every context. The evaluation therefore requires both engineering evidence and governance interpretation. This dual requirement is central to LEO. --- # 145. Human Governance Assessment Within the bounded public evaluation scope, the governance model supports the following assessment: HUMAN_REVIEW_REQUIRED = TRUE RUNTIME_HAS_FINAL_REVIEW_AUTHORITY = FALSE REVIEWER_REJECTION_SUPPORTED_AS_GOVERNANCE_PRINCIPLE = TRUE REVIEWER_CORRECTION_SUPPORTED_AS_GOVERNANCE_PRINCIPLE = TRUE REVIEWER_DEFERRAL_IS_VALID = TRUE STRUCTURAL_VALIDITY_EQUALS_SUBSTANTIVE_TRUTH = FALSE PRODUCTION_MUTATION_AUTHORIZED = FALSE AUTONOMOUS_ENFORCEMENT_AUTHORIZED = FALSE AUTONOMOUS_SANCTION_AUTHORIZED = FALSE AUTONOMOUS_ACCUSATION_AUTHORIZED = FALSE AUTONOMOUS_EXONERATION_AUTHORIZED = FALSE FRAUD_VERDICT_AUTHORITY = FALSE LEGAL_VERDICT_AUTHORITY = FALSE AUTONOMOUS_COMPLIANCE_CERTIFICATION = FALSE AUTONOMOUS_POLICY_CREATION = FALSE AUTONOMOUS_RULE_EXPANSION = FALSE SILENT_KNOWLEDGE_MUTATION = PROHIBITED PROVENANCE_REWRITING = PROHIBITED UNSUPPORTED_CERTAINTY = PROHIBITED PUBLIC_EVALUATION_EQUALS_PRODUCTION_AUTHORIZATION = FALSE The runtime does not replace the final human decision boundary. This remains true even where runtime behavior is deterministic, reproducible, and fully test-backed within its defined implementation scope. --- # 146. Part 4 Assessment Part 4 establishes that the Anomaly Library Runtime is governed as human-controlled institutional review infrastructure. The principal relationship is: RUNTIME -> EVIDENCE ORGANIZATION -> VALIDATION -> REVIEW PACKAGE -> HUMAN REVIEW -> AUTHORIZED HUMAN DECISION not: RUNTIME -> AUTONOMOUS INSTITUTIONAL VERDICT -> AUTONOMOUS ENFORCEMENT The governance model explicitly separates: technical validation from substantive judgment; implementation from deployment; deployment from institutional authority; anomaly classification from fraud or legal verdict; confidence from authority; pattern recognition from institutional conclusion; and public evidence from production authorization. Human review remains substantive, challengeable, correctable, and institutionally authoritative within the applicable governance framework. The runtime remains subordinate to that boundary. --- # 147. Runtime Evidence Model The evaluator-facing Anomaly Library evidence package must distinguish implementation evidence from architectural description. The current frozen baseline is supported by: - implemented runtime components; - executable test modules; - runtime completion review; - governance review; - evidence-integrity review; - provenance review; - human-review compliance review; - runtime-safety review; - and an approved baseline freeze decision. The evidence chain can therefore be represented as: ```text ARCHITECTURAL CONTRACT -> IMPLEMENTED RUNTIME -> EXECUTABLE TESTS -> COMPLETION REVIEW -> BASELINE FREEZE -> BOUNDED PUBLIC EVALUATION EVIDENCE ``` This chain is stronger than architectural documentation alone because the reviewed claims can be connected to executable implementation and recorded test behavior. It remains bounded because neither successful implementation nor successful tests authorize production deployment or autonomous institutional authority. --- # 148. Reviewed Runtime Component Evidence The reviewed runtime baseline contains concrete implementation components for the following responsibilities: artifact_loader.py schema_validator.py reference_validator.py evidence_lineage_validator.py provenance_validator.py category_validator.py process_mode_validator.py classification_validator.py human_review_validator.py anomaly_library_runtime.py anomaly_library_pipeline.py human_review_package_builder.py The runtime package also includes its package initialization boundary. These files establish an inspectable implementation surface corresponding to the runtime architecture described earlier in this document. The presence of a component file alone is not treated as proof of complete behavior. Implementation evidence becomes materially stronger when considered together with the applicable tests and reviewed completion evidence. --- # 149. Runtime Test Surface The reviewed Anomaly Library Runtime test surface includes dedicated tests for: test_anomaly_library_pipeline.py test_anomaly_library_runtime.py test_artifact_loader.py test_category_validator.py test_classification_validator.py test_evidence_lineage_validator.py test_human_review_package_builder.py test_human_review_validator.py test_process_mode_validator.py test_provenance_validator.py test_reference_validator.py test_schema_validator.py This structure provides evaluator-visible evidence that testing was separated across individual runtime responsibilities rather than being represented only by one undifferentiated end-to-end test. The test structure supports inspection of component behavior, integration behavior, and governance-relevant validation behavior. --- # 150. Recorded Test Command The reviewed Runtime Completion Review records the following test command: python -m pytest leo/runtime/anomaly_library/runtime/tests The recorded result is: 61 passed The reviewed completion record further states that all applicable runtime tests completed successfully and no runtime failures were observed within that test execution. This evidence should be interpreted narrowly. It establishes the recorded test result for the reviewed Anomaly Library Runtime test scope. It does not establish a current global LEO test count. It does not establish that every future Anomaly Library capability has been tested. It does not establish production readiness. --- # 151. Test Result Scope The public statement: 61 passed must remain bound to: ANOMALY LIBRARY RUNTIME REVIEWED TEST BASELINE It must not be represented as: 61 TESTS PROVE COMPLETE LEO SAFETY or: 61 TESTS PROVE COMPLETE INSTITUTIONAL CORRECTNESS or: 61 TESTS PROVE PRODUCTION READINESS The test count is engineering evidence of reviewed runtime behavior within the defined test boundary. --- # 152. Component Test Evidence The test package demonstrates that individual runtime responsibilities have dedicated executable test surfaces. These include: artifact loading; schema validation; reference validation; evidence-lineage validation; provenance validation; category validation; Process Mode validation; reviewed-classification validation; human-review validation; runtime orchestration; pipeline execution; and Human Review Package construction. The evaluator should treat this as evidence of deliberate decomposition of runtime responsibilities. This reduces the risk that a single high-level test hides failures in individual governance-critical components. --- # 153. Integration Test Evidence Component tests are necessary but not sufficient for evaluating runtime integration. The reviewed test surface therefore also includes: test_anomaly_library_runtime.py test_anomaly_library_pipeline.py These provide evidence that the runtime components participate in executable higher-level flows. The integration evidence supports the claim that the runtime architecture is represented by an actual orchestration and pipeline implementation. It does not imply autonomous institutional decision capability. --- # 154. Evidence-Lineage Test Relevance The dedicated evidence-lineage validator test surface is particularly important for public evaluation. A runtime that processes anomaly-related information without maintaining traceable evidence relationships would undermine one of the central integrity requirements of LEO. The dedicated test surface demonstrates that evidence-lineage behavior is an explicit engineering concern rather than an undocumented expectation. The evaluator-facing claim remains: EVIDENCE LINEAGE IS AN IMPLEMENTED AND TESTED VALIDATION CONCERN within the reviewed runtime baseline. --- # 155. Provenance Test Relevance The dedicated provenance validator test surface similarly demonstrates that provenance is represented in executable validation behavior. This supports the broader architectural claim that provenance is not treated as optional explanatory metadata. The runtime includes explicit implementation and testing for provenance validation. The public claim must remain limited to the reviewed behavior. It must not be expanded into a claim that all possible provenance problems in all institutional data environments can be resolved automatically. --- # 156. Human-Review Test Relevance The runtime includes a dedicated human-review validator and corresponding test surface. This is significant because the human-review requirement is implemented as a runtime concern rather than merely stated in documentation. The evaluator can therefore distinguish between: HUMAN REVIEW AS MARKETING LANGUAGE and: HUMAN REVIEW AS AN EXPLICIT RUNTIME VALIDATION CONCERN The reviewed baseline supports the second interpretation. --- # 157. Human Review Package Test Relevance The Human Review Package Builder also has a dedicated test surface. This provides evidence that the handoff between runtime validation and human review is represented by executable software behavior. The package builder is therefore not only an architectural concept. However, testing the builder does not establish that every possible human review workflow has been implemented. The evidence applies to the reviewed package-generation scope. --- # 158. Process Mode Validator Test Relevance The dedicated Process Mode validator test surface establishes that Process Mode context is an explicit runtime validation concern inside the Anomaly Library baseline. This supports the architectural dependency: PROCESS MODE CONTEXT -> ANOMALY LIBRARY VALIDATION CONTEXT It does not establish that the Anomaly Library independently performs the full Process Mode classification pipeline. That capability remains governed by the separate Process Mode Layer. --- # 159. Classification Validator Test Relevance The reviewed-classification validator and its tests support the distinction between: CLASSIFICATION DATA and: VALID REVIEWED CLASSIFICATION REPRESENTATION This is important because anomaly-related knowledge must preserve review status and classification context. The runtime does not acquire authority to create human-reviewed classifications simply because it can validate their representation. --- # 160. Reference Validator Test Relevance Reference validation tests support the requirement that relationships among reviewed artifacts remain explicit and inspectable. This helps prevent a runtime implementation from silently accepting broken or unresolvable relationships as valid evidence chains. The tests provide engineering evidence for reference-integrity behavior within the reviewed scope. --- # 161. Schema Validator Test Relevance The schema validator test surface provides evidence that runtime structural contracts are executable and testable. This supports reproducibility. It does not convert structural validity into substantive correctness. The distinction established earlier remains active: SCHEMA_VALID != ANOMALY_CONFIRMED --- # 162. Runtime Completion Review The Runtime Completion Review is the principal formal review artifact for the implemented baseline before freeze. It evaluates: implementation completeness; runtime behavior; evidence integrity; provenance preservation; human-review requirements; architectural compliance; and runtime safety. The reviewed runtime status is recorded as: READ_ONLY_RUNTIME_COMPLETE This establishes a defined maturity state for the reviewed implementation. --- # 163. Runtime Characteristics Review The Completion Review records the reviewed runtime characteristics as: deterministic reproducible auditable review-oriented read-only provenance-preserving evidence-preserving governance-compliant These characteristics describe reviewed engineering behavior. The word deterministic in this context refers to runtime execution behavior. It must not be confused with the Process Mode state: DETERMINISTIC_PROCESS The two concepts belong to different semantic layers. --- # 164. Evidence Integrity Review The reviewed Evidence Integrity section confirms preservation of: evidence identifiers; source references; lineage; provenance; and validation history. It also records that no evidence mutation occurs during runtime execution. This is a critical evaluator-facing property. The runtime therefore supports anomaly review without requiring the original evidence to be rewritten into a machine-preferred representation. --- # 165. Provenance Review The reviewed Provenance section records preservation of: source identity; artifact identity; review metadata; and references. It also records that no provenance rewriting occurs. This supports the public claim that runtime interpretation remains traceable to its origin context. --- # 166. Governance Review The Runtime Completion Review confirms the following governance boundaries: Human Review Required Read-only Runtime No Autonomous Enforcement No Autonomous Learning No Production Mutation No Fraud Verdicts No Legal Verdicts Evidence Lineage Preserved Provenance Preserved Institutional Traceability Preserved These boundaries are part of the reviewed runtime behavior and governance assessment. They should remain visible in every evaluator-facing representation of the Anomaly Library Runtime. --- # 167. Runtime Safety Review The Runtime Completion Review separately evaluates runtime safety. The reviewed baseline does not include authority to perform production or institutionally coercive actions. Safety is therefore established primarily through: read-only boundaries; preservation of evidence; preservation of provenance; explicit validation; structured review preparation; and prohibition of autonomous institutional decisions. This is a bounded safety model. It does not claim that every conceivable future integration risk has already been assessed. --- # 168. Architectural Compliance Review The reviewed completion evidence records alignment with: the Process Mode Layer; the Evidence Review Model; the Provenance Model; the Human Review Model; and Runtime Governance Principles. The review records no architectural inconsistency within the reviewed runtime scope. This matters because evaluator-facing evidence should demonstrate not merely that code exists, but that the code remains aligned with the governing LEO architecture. --- # 169. Formal Review Outcomes The reviewed completion evidence records PASS outcomes for: Architecture Review Governance Review Evidence Review Provenance Review Human Review Compliance Runtime Safety The runtime status after review remains: READ_ONLY_RUNTIME_COMPLETE The implementation status is recorded as: FOUNDATION_COMPLETE These review outcomes provide the formal basis for the subsequent freeze decision. --- # 170. Baseline Freeze Decision Following completion review, the reviewed runtime was formally frozen as a controlled runtime reference baseline. The Freeze Decision records: Status: APPROVED_DECISION and: FREEZE_RUNTIME_BASELINE The frozen runtime status remains: READ_ONLY_RUNTIME_COMPLETE The freeze establishes a controlled engineering reference point. It does not authorize production deployment. It does not authorize public release automatically. It does not authorize autonomous anomaly learning or enforcement. --- # 171. Freeze Boundary The approved frozen runtime boundary permits the baseline to: load reviewed anomaly artifacts; validate schema; validate references; validate evidence lineage; validate provenance; validate anomaly categories; validate Process Mode assignments; validate reviewed classifications; validate human-review requirements; execute runtime orchestration; execute the runtime pipeline; generate Human Review Packages; preserve evidence lineage; preserve provenance; preserve source references; produce read-only validation outputs; and support human reviewers. This list defines the strongest positive evaluator-facing capability boundary for the frozen runtime baseline. --- # 172. Freeze Prohibitions The same Freeze Decision explicitly prohibits the baseline from: mutating production systems; modifying institutional records; altering reviewed artifacts during runtime execution; rewriting evidence; rewriting provenance; creating autonomous anomaly knowledge; learning autonomously from runtime execution; classifying new institutional facts as final knowledge; enforcing outcomes; issuing fraud verdicts; issuing legal verdicts; bypassing human review; or replacing institutional decision-making. These prohibitions are equally important to the public evidence package as the positive capability list. --- # 173. Frozen Baseline Governance Markers The Freeze Decision records the governance state: HUMAN_REVIEW_REQUIRED: YES AUTONOMOUS_LEARNING: NO AUTONOMOUS_ENFORCEMENT: NO PRODUCTION_MUTATION: NO FRAUD_VERDICTS: NO LEGAL_VERDICTS: NO EVIDENCE_LINEAGE_REQUIRED: YES PROVENANCE_REQUIRED: YES These values provide a compact evaluator-facing governance summary. They do not require inference from broader descriptive text. --- # 174. Human Review Relationship After Freeze The frozen runtime baseline remains a human-review support layer only. Its outputs are not: institutional decisions; enforcement actions; fraud findings; or legal findings. Runtime outputs must remain subject to human review before institutionally significant use. The freeze therefore stabilizes the runtime implementation without weakening the human-control boundary. --- # 175. Public Evaluation Relationship After Freeze The Freeze Decision explicitly connects the baseline to: LEO Public Visibility & Public Evaluation Readiness The frozen baseline is treated as a stable internal reference point before public-facing explanation, packaging, and evaluation work. Any public package derived from that baseline must preserve: read-only runtime; human review required; no autonomous enforcement; no fraud verdicts; no legal verdicts; evidence-lineage preservation; and provenance preservation. This current evaluator-facing document follows that requirement. --- # 176. Freeze Does Not Authorize Release The approved runtime freeze and public publication authorization are different decisions. The Freeze Decision itself does not introduce: production deployment; autonomous anomaly detection; autonomous anomaly learning; institutional enforcement; fraud determination; legal determination; public release authorization; new runtime capabilities; new anomaly categories; new reviewed examples; or mutation of archived artifacts. The current publication process therefore requires separate human review and repository authorization. --- # 177. Failure Mode: Invalid Artifact Structure An artifact may fail the applicable schema requirements. In that case, the runtime should preserve the invalid state rather than silently repair the artifact and treat the repaired version as reviewed source material. The evaluator-facing expectation is: INVALID STRUCTURE -> VALIDATION FAILURE -> INSPECTABLE RESULT -> REVIEW OR CORRECTION OUTSIDE AUTONOMOUS RUNTIME AUTHORITY This preserves the integrity of the original artifact and review process. --- # 178. Failure Mode: Broken Reference An artifact may contain a missing, invalid, or unresolvable reference. The runtime must not silently substitute a different reference. The failure should remain visible. Conceptually: BROKEN REFERENCE -> REFERENCE VALIDATION FAILURE -> REVIEW-RELEVANT CONDITION The runtime must not fabricate the missing relationship. --- # 179. Failure Mode: Evidence-Lineage Failure A reviewed anomaly artifact may fail evidence-lineage validation. This can indicate that the runtime cannot establish the required relationship between the artifact and its supporting evidence under the applicable contract. The safe behavior is to preserve the failure state. The runtime must not manufacture evidence lineage. The runtime must not treat incomplete lineage as if it were complete. --- # 180. Failure Mode: Provenance Failure Provenance may be incomplete, invalid, inconsistent, or otherwise insufficient under the applicable reviewed contract. The runtime should surface that condition. It must not invent provenance in order to satisfy validation. A provenance problem therefore remains an explicit review limitation. --- # 181. Failure Mode: Invalid Anomaly Category An anomaly-category representation may fail its applicable validation rules. Such a failure indicates a problem with the runtime representation. It does not independently determine whether an anomaly exists. The appropriate response is validation failure and review or correction of the artifact under human governance. --- # 182. Failure Mode: Invalid Process Mode Assignment A reviewed artifact may contain a Process Mode assignment that fails the applicable validation rules. The Anomaly Library Runtime must not silently replace that assignment with a different Process Mode. The proper behavior is: INVALID PROCESS MODE REPRESENTATION -> VALIDATION FAILURE -> REVIEW The full Process Mode determination remains governed by its own reviewed architecture. --- # 183. Failure Mode: Reviewed Classification Problem A reviewed classification may fail applicable runtime validation. This could involve structural, reference, review-status, or other contract conditions. The runtime must not automatically create a replacement reviewed classification. Such replacement would improperly convert runtime validation into autonomous knowledge creation. --- # 184. Failure Mode: Human Review Requirement Missing Where the applicable artifact requires human review but the necessary review state or requirement is absent, the runtime must not silently waive the requirement. The safe result is a validation failure or review-relevant condition. This protects against a pathway in which structurally valid artifacts bypass human governance. --- # 185. Failure Mode: Conflicting Evidence Conflicting evidence may remain technically valid while creating substantive review uncertainty. The runtime must preserve that distinction. It may validate the structure and provenance of conflicting sources. It must not necessarily determine which substantive source is correct. Conflict therefore becomes a Human Review Package concern rather than an automatic runtime verdict. --- # 186. Failure Mode: Missing Evidence Missing evidence can be relevant without proving a substantive anomaly. The runtime should preserve missing-evidence conditions where represented by the applicable artifact model. The evaluator should not interpret such conditions as autonomous fraud findings. --- # 187. Failure Mode: Unknown Process Context Where Process Mode context is unresolved, the runtime must preserve uncertainty rather than manufacture process certainty. UNKNOWN_REQUIRES_REVIEW is a valid Process Mode state. It supports the governance pattern: INSUFFICIENT PROCESS EVIDENCE -> NO FORCED CLASSIFICATION -> HUMAN REVIEW --- # 188. Failure Mode: Pipeline Failure A pipeline execution may fail because one or more reviewed runtime conditions are not satisfied. The system must not convert pipeline completion failure into a substantive institutional conclusion. A pipeline failure is an engineering or validation state. It is not itself evidence of institutional misconduct. --- # 189. Failure Mode: Review Package Cannot Be Safely Built If required evidence, provenance, references, or review metadata are insufficient to construct the applicable Human Review Package safely, the runtime should not fabricate the missing material. The failure must remain visible. A partial or failed review package can itself become information for an authorized reviewer. --- # 190. Failure Mode: Unexpected Runtime Exception Unexpected runtime exceptions are implementation failures. They must not be interpreted as anomaly confirmations. They should be handled as engineering defects or runtime-failure evidence within the applicable development and review process. The evaluator should preserve the distinction: SOFTWARE FAILURE != INSTITUTIONAL ANOMALY --- # 191. Failure Mode: Unsupported Future Capability The evaluator may encounter architectural documents describing capabilities outside the frozen baseline. The presence of such documentation does not mean the capability is implemented in this runtime version. Where no reviewed implementation and test evidence exists for a capability, the correct public status is: NOT ESTABLISHED BY THIS BASELINE This is especially relevant to future: temporal Process Mode transition analysis; broader autonomous pattern recognition; production integration; risk scoring; and distributed edge deployment. --- # 192. Failure Mode: Misinterpretation by Evaluator An evaluation artifact can itself be misunderstood. One risk is interpreting the term Anomaly Library as a list of proven fraud cases. That interpretation is incorrect. Another risk is interpreting 61 passed as proof of complete system safety. That interpretation is also incorrect. A further risk is interpreting FROZEN as production release status. That is incorrect. This evidence package therefore treats terminology clarification as part of the public-evaluation safety boundary. --- # 193. Evaluator Verification Path An evaluator should be able to assess the current Anomaly Library evidence through a controlled sequence. Recommended sequence: 1. Read the bounded public evidence package. 2. Confirm the stated runtime boundary. 3. Inspect the listed runtime component surface where published. 4. Inspect the applicable test evidence where published. 5. Compare runtime claims against test-backed behavior. 6. Confirm evidence-lineage and provenance requirements. 7. Confirm Process Mode dependency and limits. 8. Confirm human-review requirements. 9. Confirm explicit non-authorized capabilities. 10. Distinguish the frozen runtime baseline from future architecture. The purpose of this sequence is not to require acceptance of LEO's claims. The purpose is to make those claims inspectable. --- # 194. Evaluator Questions the Package Should Answer A competent evaluator should be able to answer the following questions after reviewing the evidence package: What does the Anomaly Library Runtime actually do? Which capabilities are implemented? Which runtime components exist? What test scope was recorded? What was the test result? Which formal review was completed? Was the runtime baseline frozen? What does "frozen" mean? Does the runtime mutate production systems? Does it issue fraud verdicts? Does it issue legal verdicts? Does it learn autonomously? Does it enforce outcomes? Is human review required? How is evidence lineage preserved? How is provenance handled? How does Process Mode relate to anomaly interpretation? What future capabilities are explicitly not claimed? If these questions cannot be answered from the public evidence route, the publication package would remain incomplete. --- # 195. What the Evaluator Should Not Need to Assume The evaluator should not need to assume that: architectural diagrams correspond to code; runtime code is covered by tests; evidence lineage is merely aspirational; provenance is merely descriptive; human review is optional; a frozen baseline means production deployment; anomaly means fraud; Process Mode means risk score; or missing public implementation evidence should be inferred from internal design documents. The public evaluation package should make the applicable evidence explicit. --- # 196. Evidence Triangulation The strongest evaluator-facing claim is supported by multiple evidence classes. For example: CLAIM: The runtime preserves evidence lineage. SUPPORT: Architecture + evidence_lineage_validator.py + dedicated test surface + Runtime Completion Review + Freeze Decision Similarly: CLAIM: Human review is required. SUPPORT: Governance architecture + human_review_validator.py + Human Review Package Builder + dedicated tests + Completion Review + Freeze Decision This multi-source relationship reduces reliance on any single document. --- # 197. Evidence Triangulation for Read-Only Behavior The read-only boundary is supported by: runtime architecture; completion-review findings; explicit governance review; freeze decision; explicit production-mutation prohibition; and final frozen-baseline statement. The public package should therefore treat read-only status as a central reviewed property of the baseline. --- # 198. Evidence Triangulation for No Autonomous Enforcement No autonomous enforcement is supported by explicit review and freeze boundaries. This is not inferred from absence of enforcement code alone. The architecture deliberately prohibits enforcement authority. That distinction matters because: NO ENFORCEMENT CODE FOUND is weaker evidence than: ENFORCEMENT IS EXPLICITLY OUTSIDE THE REVIEWED AUTHORITY BOUNDARY The current baseline supports the stronger governance statement. --- # 199. Evidence Triangulation for No Autonomous Learning The freeze boundary explicitly prohibits autonomous anomaly learning and autonomous creation of anomaly knowledge. This is consistent with the broader LEO knowledge principle: DOCUMENTS ARE EVIDENCE HUMAN-CONFIRMED CLASSIFICATIONS ARE KNOWLEDGE The public evaluation package should preserve this relationship. --- # 200. Evidence Triangulation for Process Mode Dependency The runtime includes explicit Process Mode validation. The Runtime Completion Review records alignment with the Process Mode Layer. The broader Anomaly Library architecture treats Process Mode as contextual input to anomaly interpretation. Together these evidence classes support the bounded statement: ANOMALY LIBRARY RUNTIME PRESERVES AND VALIDATES APPLICABLE PROCESS MODE CONTEXT They do not support the stronger statement: ANOMALY LIBRARY AUTONOMOUSLY DETERMINES PROCESS MODE --- # 201. Evidence Triangulation for Human Review Package Generation Human Review Package generation is supported by: an implemented package builder; a dedicated test surface; runtime-completion review evidence; and freeze-boundary authorization. This makes it an appropriate public capability claim. The package remains review support, not final institutional judgment. --- # 202. Evaluator Reproducibility Boundary Reproducibility should be demonstrated within the published and reviewable scope. The current public package should not require the evaluator to reproduce private institutional data or internal confidential cases. Instead, the evaluation route should use: published implementation evidence where authorized; public documentation; bounded test evidence; reviewed public examples where appropriate; and preserved governance statements. The goal is reproducibility of the method and reviewed runtime claims, not disclosure of every internal artifact. --- # 203. Public Test Evidence Boundary If runtime tests themselves are later published or reproduced in a dedicated evaluation package, they must be assessed for: rights and licensing; absence of private institutional data; absence of secrets; absence of internal-only identifiers where material; consistency with current architecture; and suitability for public evaluation. This document does not automatically authorize publication of the full canonical test suite. It establishes the test evidence that may guide a controlled publication decision. --- # 204. Public Runtime Code Boundary Similarly, the existence of canonical runtime code does not automatically mean that every runtime file should be copied into the public repository. Public code selection must remain controlled. The evaluator may be given sufficient evidence through: selected source code; selected tests; documentation; reproducible fixtures; or bounded demonstrations. The publication decision should maximize evaluator inspectability without discarding repository governance and rights controls. --- # 205. Historical Evidence Boundary Historical anomaly artifacts can be useful context. They can also represent older architecture, terminology, or governance models. Historical evidence must therefore be labeled as historical when applicable. It must not silently replace the current frozen runtime baseline. The evaluator should be able to distinguish: HISTORICAL EVIDENCE from: CURRENT REVIEWED BASELINE --- # 206. Baseline Evolution Boundary The frozen v0.1 runtime is a reference point, not the permanent endpoint of the Anomaly Library. Future development may introduce additional reviewed capabilities. When that happens, evaluators should be able to compare: FROZEN BASELINE v0.1 -> AUTHORIZED CHANGE -> IMPLEMENTATION -> TEST -> REVIEW -> NEW CONTROLLED BASELINE This preserves architectural continuity and makes evolution auditable. --- # 207. Transition-State Future Evidence Requirement The separately identified evaluator interest in transitions between deterministic and stochastic process behavior will require its own evidence chain. A future transition-state public claim should not be made until supported by: TRANSITION ARCHITECTURE + TEMPORAL STATE MODEL + IMPLEMENTATION + TESTS + PROVENANCE RULES + HUMAN REVIEW MODEL + FORMAL REVIEW The current Anomaly Library evidence package intentionally does not substitute for that future work. --- # 208. Knowledge Layer Relationship The Anomaly Library also sits before broader Reviewed / Institutional Knowledge integration in the developing LEO architecture. A reviewed anomaly may contribute to institutional knowledge only under the applicable human-governed Knowledge Layer model. The Anomaly Library Runtime does not independently authorize that final knowledge transition. The relationship can be summarized: REVIEWED ANOMALY CONTEXT -> HUMAN REVIEW -> CONTROLLED KNOWLEDGE INTEGRATION not: ANOMALY RUNTIME OUTPUT -> AUTOMATIC INSTITUTIONAL KNOWLEDGE --- # 209. Knowledge Layer Evidence Is Separate The Knowledge Layer has its own implementation, test, review, and freeze evidence. This Anomaly Library package must not claim those capabilities as part of the Anomaly Library Runtime. The public evaluator route should eventually allow the evaluator to understand the dependency chain without collapsing separate runtime baselines. This supports modular evaluation. --- # 210. Modular Evaluation Principle LEO should be evaluated as a connected architecture composed of separately reviewable layers. For the current method, an evaluator can conceptually distinguish: PROCESS MODE -> ANOMALY LIBRARY -> REVIEWED / INSTITUTIONAL KNOWLEDGE -> HUMAN GOVERNANCE Each layer has its own authority boundary. Each layer requires its own evidence. Completion of one layer does not automatically prove completion of the next. --- # 211. Runtime Evidence Strengths The current Anomaly Library Runtime evidence has several material strengths: explicit frozen baseline; explicit read-only boundary; concrete runtime implementation; separated validators; runtime orchestration; pipeline execution; Human Review Package generation; dedicated test modules; recorded 61 passed result; completion review; evidence-integrity review; provenance review; governance review; human-review compliance review; runtime-safety review; Process Mode alignment; and explicit non-authorized capabilities. These strengths make the baseline suitable for bounded evaluator-facing presentation. --- # 212. Runtime Evidence Limitations The evidence also has important limitations. It does not establish: complete anomaly-domain coverage; production-scale performance; production deployment; production security posture; complete third-party integration behavior; autonomous anomaly detection; temporal Process Mode transitions; autonomous institutional knowledge creation; legal decision support completeness; regulatory compliance certification; or complete-system LEO production readiness. These limitations should remain explicit. --- # 213. Implementation Maturity Assessment Within its defined scope: RUNTIME IMPLEMENTATION = COMPLETE FOR FROZEN v0.1 SCOPE RUNTIME TEST EXECUTION = RECORDED AS PASS RUNTIME COMPLETION REVIEW = COMPLETE BASELINE FREEZE = APPROVED READ_ONLY BOUNDARY = PRESERVED PUBLIC EVALUATION EVIDENCE = AVAILABLE THROUGH THIS PACKAGE PRODUCTION READINESS = NOT ESTABLISHED This is a strong but bounded maturity position. --- # 214. Evidence Quality Assessment The available evidence is materially stronger than a conceptual proposal because multiple evidence forms agree on the same runtime boundary. The architecture, implementation, tests, completion review, and freeze decision all support the same central model: READ-ONLY EVIDENCE-PRESERVING PROVENANCE-PRESERVING PROCESS-CONTEXT-AWARE HUMAN-REVIEW-REQUIRED NON-AUTONOMOUS No reviewed evidence identified in this package authorizes autonomous institutional enforcement or final fraud/legal verdicts. --- # 215. Evaluator Verification Outcome Model The evaluator does not need to produce a binary judgment that LEO as a whole is either complete or incomplete. A more useful evaluation outcome can distinguish: ARCHITECTURE PRESENT IMPLEMENTATION PRESENT TEST EVIDENCE PRESENT FORMAL REVIEW PRESENT FROZEN BASELINE PRESENT PUBLIC INSPECTABILITY PRESENT PRODUCTION AUTHORIZATION ABSENT AUTONOMOUS AUTHORITY ABSENT FUTURE CAPABILITIES DEFERRED This maturity model reflects the actual development state more accurately. --- # 216. Runtime Evidence Assessment For the bounded frozen baseline, the evaluator-facing evidence supports: ANOMALY_LIBRARY_RUNTIME_IMPLEMENTATION = PRESENT ANOMALY_LIBRARY_RUNTIME_TEST_SURFACE = PRESENT RECORDED_TEST_RESULT = 61 PASSED RUNTIME_COMPLETION_REVIEW = PASS ARCHITECTURE_REVIEW = PASS GOVERNANCE_REVIEW = PASS EVIDENCE_REVIEW = PASS PROVENANCE_REVIEW = PASS HUMAN_REVIEW_COMPLIANCE = PASS RUNTIME_SAFETY_REVIEW = PASS RUNTIME_STATUS = READ_ONLY_RUNTIME_COMPLETE BASELINE_FREEZE = APPROVED BASELINE_VERSION = v0.1 --- # 217. Governance Evidence Assessment The reviewed evidence supports: HUMAN_REVIEW_REQUIRED = TRUE EVIDENCE_LINEAGE_REQUIRED = TRUE PROVENANCE_REQUIRED = TRUE AUTONOMOUS_LEARNING = FALSE AUTONOMOUS_ENFORCEMENT = FALSE PRODUCTION_MUTATION = FALSE FRAUD_VERDICT_AUTHORITY = FALSE LEGAL_VERDICT_AUTHORITY = FALSE These governance markers should remain visible in the final public evaluation route. --- # 218. Failure-Handling Assessment The safe failure model requires: INVALID STRUCTURE -> NO AUTOMATIC REPAIR AS REVIEWED FACT BROKEN REFERENCE -> NO INVENTED REFERENCE LINEAGE FAILURE -> NO INVENTED LINEAGE PROVENANCE FAILURE -> NO INVENTED PROVENANCE UNKNOWN PROCESS CONTEXT -> NO FORCED PROCESS MODE REVIEW REQUIREMENT FAILURE -> NO AUTOMATIC APPROVAL CONFLICTING EVIDENCE -> NO SILENT CONFLICT REMOVAL RUNTIME FAILURE -> NO INSTITUTIONAL VERDICT These boundaries define how the runtime should fail without manufacturing institutional certainty. --- # 219. Evaluator Evidence Decision Based on the reviewed architecture, implementation surface, recorded test evidence, formal runtime review, and approved baseline freeze, the Anomaly Library Runtime is suitable for bounded public evaluation as an implemented human-review-support runtime. The suitable evaluator claim is: THE ANOMALY LIBRARY RUNTIME v0.1 IS AN IMPLEMENTED, TESTED, REVIEWED, AND FROZEN READ-ONLY RUNTIME BASELINE FOR VALIDATING REVIEWED ANOMALY ARTIFACTS, PRESERVING EVIDENCE LINEAGE AND PROVENANCE, VALIDATING PROCESS AND REVIEW CONTEXT, AND GENERATING HUMAN REVIEW SUPPORT. The evidence does not support a stronger autonomous institutional authority claim. --- # 220. Part 5 Assessment Part 5 establishes that the Anomaly Library Runtime baseline is supported by a traceable engineering evidence chain rather than architectural description alone. The reviewed evidence includes: IMPLEMENTATION + COMPONENT TESTS + PIPELINE TESTS + 61-PASSED TEST BASELINE + RUNTIME COMPLETION REVIEW + ARCHITECTURE REVIEW + GOVERNANCE REVIEW + EVIDENCE REVIEW + PROVENANCE REVIEW + HUMAN REVIEW COMPLIANCE + RUNTIME SAFETY REVIEW + APPROVED BASELINE FREEZE The resulting evaluator-facing maturity state is: IMPLEMENTED TESTED REVIEWED FROZEN READ-ONLY HUMAN-CONTROLLED EVIDENCE-PRESERVING PROVENANCE-PRESERVING NOT PRODUCTION-AUTHORIZED NOT AUTONOMOUSLY ENFORCING NOT A FRAUD OR LEGAL VERDICT ENGINE The remaining part of this package will provide the evaluator walkthrough, explicit limitations, architectural risk assessment, implementation-readiness assessment, formal evidence decision, continuity statement, and final Commission-facing status. --- # 221. Evaluator Walkthrough This final section provides a controlled evaluator-facing walkthrough for the Anomaly Library Runtime evidence package. The purpose is to make the reviewed runtime baseline inspectable without requiring the evaluator to infer implementation maturity from architectural documentation alone. The recommended evaluation path is: ```text UNDERSTAND THE PURPOSE -> IDENTIFY THE RUNTIME BOUNDARY -> INSPECT IMPLEMENTATION EVIDENCE -> INSPECT TEST EVIDENCE -> INSPECT GOVERNANCE BOUNDARIES -> INSPECT EVIDENCE LINEAGE AND PROVENANCE -> INSPECT PROCESS MODE DEPENDENCY -> INSPECT HUMAN REVIEW HANDOFF -> INSPECT FAILURE BEHAVIOR -> CONFIRM NON-AUTHORIZED CAPABILITIES -> ASSESS CURRENT MATURITY ``` This sequence is intended to support independent evaluation. It is not intended to prescribe a required evaluator conclusion. --- # 222. Step 1 — Identify the Problem Boundary The evaluator should first identify the institutional problem addressed by the Anomaly Library Layer. Institutional anomaly analysis can fail when: evidence is detached from its origin; anomaly labels are treated as conclusions; process context is ignored; uncertainty is collapsed; historical observations become decontextualized; machine-generated outputs are treated as reviewed knowledge; or automated systems acquire authority beyond their evidence. The Anomaly Library Runtime addresses a narrower engineering problem: HOW CAN REVIEWED ANOMALY-RELATED ARTIFACTS BE VALIDATED, PRESERVED, CONNECTED TO PROCESS CONTEXT, AND PREPARED FOR HUMAN REVIEW WITHOUT TURNING THE RUNTIME INTO AN AUTONOMOUS VERDICT ENGINE? That is the correct evaluation boundary. --- # 223. Step 2 — Identify the Runtime Role The evaluator should next confirm what role the runtime actually performs. The reviewed baseline supports: artifact loading; structural validation; reference validation; evidence-lineage validation; provenance validation; anomaly-category validation; Process Mode validation; reviewed-classification validation; human-review validation; runtime orchestration; pipeline execution; and Human Review Package generation. The runtime therefore functions as: A CONTROLLED VALIDATION AND REVIEW-PREPARATION LAYER It does not function as: AN AUTONOMOUS INSTITUTIONAL DECISION AUTHORITY --- # 224. Step 3 — Confirm the Evidence Boundary The evaluator should confirm that the runtime preserves a distinction among: SOURCE EVIDENCE ANOMALY-RELATED ARTIFACTS VALIDATION RESULTS REVIEWED CLASSIFICATIONS HUMAN REVIEW OUTPUTS INSTITUTIONAL KNOWLEDGE These are not interchangeable states. A source document does not become institutional knowledge merely because it enters the runtime. A validation result does not become a confirmed anomaly merely because the runtime successfully produced it. A machine-readable anomaly artifact does not become a fraud determination. The boundary between these states is fundamental to the LEO method. --- # 225. Step 4 — Confirm Evidence Lineage The evaluator should inspect whether the reviewed runtime treats evidence lineage as a first-class requirement. The intended chain is: SOURCE -> SOURCE REFERENCE -> EVIDENCE ARTIFACT -> REVIEWED RELATIONSHIP -> ANOMALY-RELATED ARTIFACT -> VALIDATION -> HUMAN REVIEW PACKAGE The runtime must preserve the ability to trace review-relevant claims back toward their evidence context. Where the required lineage cannot be established, the runtime must not invent it. --- # 226. Step 5 — Confirm Provenance Preservation The evaluator should separately inspect provenance behavior. Provenance answers questions such as: Where did this artifact originate? Which source does it reference? Which reviewed artifact does it depend on? Which review context applies? Has the runtime rewritten the origin record? Can the relationship be inspected later? The reviewed runtime baseline preserves provenance rather than treating it as disposable metadata. This supports later review, correction, comparison, and audit. --- # 227. Step 6 — Confirm Process Mode Dependency The evaluator should verify that anomaly interpretation is not isolated from process character. The broader LEO dependency remains: SOURCE EVIDENCE -> EVIDENCE-DERIVED CHARACTERISTICS -> SIGNAL ELIGIBILITY -> DETERMINISTIC / STOCHASTIC SIGNAL COUNTS -> PROCESS MODE PROPOSAL -> HUMAN REVIEW -> REVIEWED PROCESS CONTEXT -> ANOMALY INTERPRETATION CONTEXT The Anomaly Library consumes applicable reviewed Process Mode context. It does not acquire authority to bypass the Process Mode Layer. --- # 228. Step 7 — Confirm the Four Process Mode States The evaluator should recognize the current canonical Process Mode states: DETERMINISTIC_PROCESS STOCHASTIC_PROCESS MIXED_PROCESS UNKNOWN_REQUIRES_REVIEW Anomaly interpretation must not assume that the same deviation has the same meaning across all four states. This is one of the central methodological distinctions in LEO. --- # 229. Step 8 — Confirm Signal Eligibility Boundary Where Process Mode evidence is relevant, the evaluator should preserve the canonical signal rule: ONLY SIGNAL-ELIGIBLE SUPPORTED CHARACTERISTICS MAY CONTRIBUTE POSITIVE PROCESS MODE SIGNALS The following states remain distinct: NOT_SUPPORTED NOT_OBSERVED UNKNOWN CONFLICTING They must not automatically create positive signals. They must not automatically create opposite signals. This protects Process Mode context from false binary inference. --- # 230. Step 9 — Confirm Anomaly Interpretation Boundary The evaluator should verify that the runtime does not implement: DEVIATION = ANOMALY = FRAUD The correct interpretation is closer to: OBSERVATION + PROCESS CONTEXT + EVIDENCE + PROVENANCE + REVIEW HISTORY + UNCERTAINTY -> REVIEWABLE ANOMALY CONTEXT A final institutional interpretation remains human-controlled. --- # 231. Step 10 — Confirm Human Review The evaluator should confirm that human review is not an optional final decoration. It is a governance boundary. The runtime can: validate; organize; preserve; compare; identify validation failures; and prepare review material. The authorized human reviewer remains responsible for institutionally significant interpretation. --- # 232. Step 11 — Confirm Runtime Test Evidence The evaluator should confirm the recorded Anomaly Library Runtime test baseline: python -m pytest leo/runtime/anomaly_library/runtime/tests with the recorded result: 61 passed This result belongs to the reviewed runtime scope. It must not be generalized into a current global LEO test count. It must not be interpreted as regulatory certification. It must not be interpreted as complete-system production validation. --- # 233. Step 12 — Confirm Formal Runtime Review The evaluator should confirm that the runtime was subjected to formal review before freeze. The reviewed outcomes include: Architecture Review = PASS Governance Review = PASS Evidence Review = PASS Provenance Review = PASS Human Review Compliance = PASS Runtime Safety = PASS The reviewed runtime status is: READ_ONLY_RUNTIME_COMPLETE This provides a formal engineering checkpoint. --- # 234. Step 13 — Confirm Baseline Freeze The evaluator should confirm that the reviewed runtime was frozen as a controlled reference baseline. The approved decision is: FREEZE_RUNTIME_BASELINE for: ANOMALY LIBRARY RUNTIME v0.1 A freeze means that a reviewed implementation state has been stabilized as a reference baseline. It does not mean that the entire LEO architecture is complete. --- # 235. Step 14 — Confirm Production Boundary The evaluator should explicitly verify: PRODUCTION MUTATION = NOT AUTHORIZED The runtime baseline is read-only. It is not authorized to modify institutional production records. It is not authorized to execute institutional corrective action. It is not authorized to enforce its own anomaly interpretation. --- # 236. Step 15 — Confirm Verdict Boundary The evaluator should explicitly verify: FRAUD VERDICT = NOT AUTHORIZED LEGAL VERDICT = NOT AUTHORIZED Anomaly analysis and fraud determination are different semantic and institutional activities. A suspicious pattern may require investigation. A validation failure may require correction. A conflicting evidence state may require escalation. None of those states automatically constitutes fraud or legal liability. --- # 237. Step 16 — Confirm Learning Boundary The evaluator should verify: AUTONOMOUS LEARNING = NOT AUTHORIZED LEO's reviewed-memory principle remains: DOCUMENTS ARE EVIDENCE. HUMAN-CONFIRMED CLASSIFICATIONS ARE KNOWLEDGE. The runtime does not convert every observed artifact or runtime output into institutional knowledge automatically. --- # 238. Step 17 — Confirm Enforcement Boundary The evaluator should verify: AUTONOMOUS ENFORCEMENT = NOT AUTHORIZED The runtime may support review. It may expose inconsistencies. It may preserve anomaly context. It may generate structured review material. It may not autonomously impose an institutional consequence. --- # 239. Step 18 — Confirm Failure Preservation The evaluator should inspect how the method treats uncertainty and failure. Safe behavior includes preserving: invalid structure; missing references; incomplete lineage; incomplete provenance; unresolved Process Mode; conflicting evidence; missing evidence; incomplete review state; and runtime failure. The system should not manufacture certainty merely to produce a complete output. --- # 240. Step 19 — Confirm Historical Separation The evaluator should distinguish the current frozen baseline from historical LEO demonstrations and older artifacts. Historical material may remain useful evidence of development continuity. However: HISTORICAL DEMONSTRATION != CURRENT RUNTIME BASELINE and: CURRENT RUNTIME BASELINE != FUTURE ARCHITECTURE This separation prevents chronology from being mistaken for capability equivalence. --- # 241. Step 20 — Confirm Future-Capability Separation The evaluator should identify capabilities deliberately outside the current baseline. These include, where applicable: temporal Process Mode transition modeling; richer longitudinal anomaly recurrence analysis; broader institutional knowledge integration; production deployment; production authorization; distributed Local Data Hub architecture; and future predictive methods subject to separate governance. The absence of these capabilities from the frozen baseline is not concealed. They remain future work requiring separate architecture, implementation, testing, and review. --- # 242. Commission-Relevant Methodological Value The evaluator-facing significance of the Anomaly Library is not that LEO can produce more anomaly labels. The methodological value is that anomaly-related evidence is placed inside a controlled chain of interpretation. That chain includes: EVIDENCE -> PROVENANCE -> PROCESS CHARACTER -> ANOMALY CONTEXT -> REVIEW HISTORY -> HUMAN INTERPRETATION This makes the method materially different from systems that output opaque risk scores or unreviewed anomaly flags. --- # 243. Why Process Character Matters A deviation cannot be interpreted reliably without understanding the process from which it arose. In a highly deterministic process, repeated deviation from a stable rule may carry substantial review significance. In a stochastic process, variability may be expected. In a mixed process, deterministic constraints and discretionary behavior may coexist. In an unresolved process, uncertainty must remain visible. Therefore: ANOMALY SIGNIFICANCE IS PROCESS-CONTEXT-DEPENDENT This principle is central to the relationship between Process Mode and the Anomaly Library. --- # 244. Why Provenance Matters An anomaly record without provenance can become detached from the evidence that gave it meaning. That creates risks including: unsupported institutional memory; false historical continuity; inability to correct earlier interpretation; inability to compare later evidence; and inability to determine whether two anomaly records refer to the same underlying event. The runtime's provenance-preservation requirement is therefore an integrity control rather than merely a documentation preference. --- # 245. Why Review History Matters Institutional interpretation can change. Evidence can be corrected. Earlier classifications can become obsolete. Additional evidence can resolve uncertainty. A trustworthy anomaly library must therefore preserve review history rather than silently overwrite earlier states. The desired institutional memory model is: ORIGINAL EVIDENCE + ORIGINAL REVIEW + LATER EVIDENCE + CORRECTION + REVIEW HISTORY not: LATEST OUTPUT REPLACES HISTORY --- # 246. Why Non-Autonomous Knowledge Matters A system that automatically converts its own anomaly outputs into knowledge can create self-reinforcing error. For example: MODEL FLAGS EVENT -> FLAG BECOMES KNOWLEDGE -> KNOWLEDGE BECOMES TRAINING CONTEXT -> FUTURE MODEL TREATS FLAG AS CONFIRMED FACT LEO's human-reviewed knowledge boundary is designed to interrupt this loop. The Anomaly Library Runtime therefore remains a review-support system rather than a self-authorizing knowledge engine. --- # 247. Why Unknown Must Remain a Valid State Institutional systems often create pressure to produce a categorical answer. That pressure can generate false certainty. LEO explicitly preserves unresolved states. For Process Mode: UNKNOWN_REQUIRES_REVIEW is a valid result. For anomaly interpretation, insufficient or conflicting evidence can similarly remain unresolved. The ability to preserve uncertainty is a positive integrity property. --- # 248. Why Conflicting Evidence Must Remain Visible Conflicting evidence should not be silently averaged, discarded, or converted into a single machine-selected truth. Conflict may itself be relevant. The system should preserve: SOURCE A SOURCE B THE RELATIONSHIP BETWEEN THEM THE CONFLICT THE PROVENANCE OF BOTH THE REVIEW HISTORY A human reviewer can then evaluate the conflict within institutional context. --- # 249. Why No Fraud Verdict Is an Architectural Requirement Fraud is not merely a statistical anomaly. A fraud determination can involve: intent; legal definitions; jurisdiction; evidentiary standards; procedural rights; institutional authority; and contextual interpretation. An anomaly-analysis runtime is not sufficient authority for that determination. Therefore: ANOMALY EVIDENCE != FRAUD VERDICT This is a deliberate architectural limit. --- # 250. Why No Legal Verdict Is an Architectural Requirement Legal conclusions similarly require authority and context outside the Anomaly Library Runtime. The runtime may preserve evidence relevant to legal review. It may help organize evidence. It may identify structural or provenance problems. It does not issue legal conclusions. This preserves separation between technical evidence review and legal authority. --- # 251. Why Read-Only Matters Read-only behavior reduces the risk that an analysis system changes the evidence it is evaluating. The reviewed runtime boundary therefore separates: OBSERVE LOAD VALIDATE ORGANIZE PREPARE FOR REVIEW from: ALTER ENFORCE CORRECT PRODUCTION DELETE REWRITE INSTITUTIONAL RECORDS This separation is especially important for institutional evidence systems. --- # 252. Why Runtime Freeze Matters A frozen baseline gives evaluators a stable object to inspect. Without a freeze, architectural claims, code behavior, and test evidence could refer to different development states. The freeze creates a reference point: DEFINED CODE STATE + DEFINED TEST STATE + DEFINED REVIEW STATE + DEFINED GOVERNANCE STATE Future development can then be compared against that reference. --- # 253. Why Public Evaluation Must Remain Bounded Public evaluation should expose enough evidence to permit meaningful scrutiny. It should not require disclosure of every internal artifact. It should not create new rights or confidentiality risks merely to demonstrate technical maturity. The publication objective is therefore: MAXIMIZE EVALUATOR INSPECTABILITY WHILE PRESERVING GOVERNANCE, PROVENANCE, RIGHTS, CONFIDENTIALITY, AND ARCHITECTURAL ACCURACY --- # 254. Current Public Evaluation Strength The current Anomaly Library evidence can demonstrate a coherent engineering story: ARCHITECTURE EXISTS IMPLEMENTATION EXISTS TESTS EXIST RECORDED TEST EXECUTION PASSED FORMAL REVIEW EXISTS BASELINE FREEZE EXISTS GOVERNANCE BOUNDARIES ARE EXPLICIT HUMAN REVIEW IS REQUIRED EVIDENCE LINEAGE IS REQUIRED PROVENANCE IS REQUIRED This is sufficient to justify serious bounded technical evaluation. --- # 255. Current Public Evaluation Limitation The current evidence package must not imply: COMPLETE PRODUCTION SYSTEM COMPLETE EU REGULATORY CERTIFICATION COMPLETE ANOMALY DOMAIN COVERAGE AUTONOMOUS INVESTIGATION SYSTEM AUTONOMOUS FRAUD DETECTION AUTHORITY AUTONOMOUS LEGAL DECISION SYSTEM FULL TEMPORAL PROCESS TRANSITION MODEL FULL DISTRIBUTED EDGE DEPLOYMENT Those claims are not established by this runtime baseline. --- # 256. Transition-State Evaluation Interest A separate evaluator-significant topic has been identified: TRANSITION STATES BETWEEN DETERMINISTIC AND STOCHASTIC PROCESS BEHAVIOR This topic is architecturally important because real institutional processes may change character over time. Examples may include: DETERMINISTIC -> MIXED MIXED -> STOCHASTIC STOCHASTIC -> MIXED MIXED -> DETERMINISTIC or unresolved transitions requiring review. --- # 257. Transition States Are Temporal Claims The current Process Mode model classifies process character within a reviewed evidence context. A transition claim adds a temporal dimension. Conceptually: PROCESS MODE AT t1 -> NEW EVIDENCE -> PROCESS MODE AT t2 -> REVIEWED TRANSITION INTERPRETATION This is not equivalent to merely comparing two labels. A valid transition model must preserve evidence and provenance for both states and for the relationship between them. --- # 258. Transition-State Provenance Requirement Any future transition-state model should preserve: evidence supporting the earlier state; evidence supporting the later state; timestamps or applicable temporal context; characteristic observations; signal eligibility; deterministic and stochastic signal counts; classification reasoning; review status; reviewer confirmation; and correction history. Without this lineage, a transition claim would be difficult to audit. --- # 259. Transition-State Human Review Requirement A future runtime must not autonomously conclude that a process has institutionally significant behavioral drift merely because two classifications differ. Possible explanations can include: genuine process change; different evidence coverage; missing evidence; changed documentation; changed institutional rules; different observation windows; conflicting evidence; or earlier review correction. Therefore: MODE DIFFERENCE != CONFIRMED PROCESS TRANSITION Human review remains required. --- # 260. Transition-State Anomaly Relationship Transition states may become highly relevant to anomaly interpretation. For example, behavior that was anomalous under a stable deterministic process may become expected after an authorized process change. Conversely, increasing variability inside a formerly stable deterministic process may become review-relevant. The future relationship may therefore resemble: PROCESS MODE HISTORY + TRANSITION EVIDENCE + ANOMALY HISTORY + PROVENANCE -> HUMAN REVIEW This remains future architectural work. --- # 261. Transition-State Non-Claim The current Anomaly Library Runtime evidence package does not claim that the frozen baseline implements a complete temporal Process Mode transition engine. The correct current status is: TRANSITION-STATE INTEREST = RECORDED ARCHITECTURAL RELEVANCE = HIGH CURRENT FULL IMPLEMENTATION = NOT ESTABLISHED BY THIS BASELINE SEPARATE REVIEW = REQUIRED BEFORE PUBLIC CAPABILITY CLAIM This preserves evaluator interest without overstating implementation. --- # 262. Future Transition-State Evaluation Package If transition-state functionality is later implemented, it should receive a separate evaluator-facing evidence package. That package should establish: TEMPORAL MODEL STATE IDENTITY TRANSITION IDENTITY EVIDENCE LINEAGE PROVENANCE SIGNAL HISTORY UNCERTAINTY HUMAN CONFIRMATION CORRECTION HISTORY TEST EVIDENCE RUNTIME BOUNDARY FAILURE MODES Only after those elements are reviewed should a strong public transition-state claim be made. --- # 263. Architectural Risks Assessment The current Anomaly Library architecture is coherent within the reviewed baseline, but several risks must remain visible. The principal architectural risks are: anomaly semantics may be misunderstood as verdict semantics; Process Mode context may be oversimplified by external evaluators; historical examples may be mistaken for current architecture; future transition-state requirements may be inferred as already implemented; evidence lineage may be weakened if future integrations bypass canonical references; provenance may become fragmented across external systems; review status may be lost during data transformation; runtime validation may be incorrectly treated as substantive truth; future scoring mechanisms could obscure evidence relationships; future automation could gradually erode the human-review boundary; institutional knowledge could become contaminated if unreviewed runtime outputs are admitted as knowledge; correction history could be lost if future systems overwrite prior review states; public documentation may become stale relative to runtime evolution; publication pressure may encourage broader claims than evidence supports; transition-state analysis may introduce temporal ambiguity if observation windows are not governed explicitly. These risks do not invalidate the current baseline. They define areas requiring continued architectural control. --- # 264. Risk: Semantic Overreach The highest public-interpretation risk is semantic overreach. Terms such as: anomaly classification validation knowledge deterministic stochastic can carry different meanings across engineering, statistics, governance, and legal domains. Public documentation must therefore continue to define these terms within the LEO architecture. No evaluator should be required to infer their intended meaning. --- # 265. Risk: Automation Drift A future implementation could technically preserve the current API while gradually adding automatic actions around it. This could create automation drift. Examples include: automatic escalation; automatic case creation; automatic sanctions; automatic record correction; automatic risk ranking used as final authority; or automatic admission of outputs into institutional knowledge. Any such capability would require separate architectural and governance review. It must not inherit authorization from the current baseline. --- # 266. Risk: Provenance Fragmentation As LEO integrates with additional systems, evidence and metadata may be stored across different technical boundaries. This can fragment provenance. A future architecture must ensure that cross-system integration does not reduce the ability to reconstruct: SOURCE -> TRANSFORMATION -> REVIEW -> CLASSIFICATION -> ANOMALY CONTEXT -> CORRECTION The current provenance discipline should therefore be treated as a continuing architectural requirement. --- # 267. Risk: Review-State Collapse A system can preserve evidence while still losing the distinction between: PROPOSED VALIDATED REVIEWED CONFIRMED CORRECTED SUPERSEDED Such collapse would damage institutional memory. Future Anomaly Library development must preserve review-state semantics and history explicitly. --- # 268. Risk: False Temporal Interpretation Transition-state work introduces a specific future risk. Two different Process Mode results at two different times do not automatically prove process evolution. The evidence basis may have changed. The observation window may have changed. The institutional rule may have changed. The earlier review may have been corrected. Therefore temporal inference must preserve both state evidence and transition evidence. --- # 269. Risk: Public Evidence Staleness A public evaluation repository can become stale while canonical LEO development continues. This risk is already relevant to Commission readiness. Public evaluator-facing artifacts must therefore identify their applicable baseline and avoid implying that historical public material automatically represents current canonical architecture. Future publication updates should continue to use controlled delta review. --- # 270. Risk: Test Count Misinterpretation A numerical test result is easy to communicate and easy to misuse. The recorded: 61 passed must remain attached to its specific runtime test scope. It must not become a marketing number detached from: the test path; the reviewed baseline; the execution context; and the applicable version. --- # 271. Risk: Production-Readiness Misinterpretation An implemented and tested runtime can still be unsuitable for production. Production readiness can require additional work including: deployment architecture; operational security; access control; observability; resilience; incident handling; data protection assessment; integration testing; performance testing; operational governance; and institutional authorization. The current baseline does not claim completion of those areas. --- # 272. Architectural Risk Decision The identified architectural risks are material but controlled within the current frozen scope because: runtime authority is narrow; production mutation is prohibited; autonomous enforcement is prohibited; autonomous learning is prohibited; evidence lineage is required; provenance is required; human review is required; formal runtime review has been completed; and the baseline has been frozen. Therefore: ARCHITECTURAL_RISK_STATUS = ACCEPTABLE_FOR_BOUNDED_PUBLIC_EVALUATION This is not a production-risk acceptance decision. --- # 273. Implementation Readiness Assessment The Anomaly Library Runtime has reached a materially implemented state within its frozen v0.1 boundary. The reviewed implementation includes: ARTIFACT LOADING SCHEMA VALIDATION REFERENCE VALIDATION EVIDENCE-LINEAGE VALIDATION PROVENANCE VALIDATION CATEGORY VALIDATION PROCESS MODE VALIDATION REVIEWED-CLASSIFICATION VALIDATION HUMAN-REVIEW VALIDATION RUNTIME ORCHESTRATION PIPELINE EXECUTION HUMAN REVIEW PACKAGE GENERATION The runtime is therefore beyond architecture-only maturity. --- # 274. Test Readiness Assessment The implementation is accompanied by a dedicated runtime test suite. The reviewed execution records: 61 passed No runtime failures were recorded in the reviewed test execution. This supports: TEST_READINESS_FOR_FROZEN_SCOPE = PASS It does not establish production validation. --- # 275. Governance Readiness Assessment The governance boundary is explicit and consistent with the reviewed runtime. The baseline requires: HUMAN REVIEW READ-ONLY EXECUTION EVIDENCE LINEAGE PROVENANCE NO AUTONOMOUS LEARNING NO AUTONOMOUS ENFORCEMENT NO PRODUCTION MUTATION NO FRAUD VERDICT NO LEGAL VERDICT Therefore: GOVERNANCE_READINESS_FOR_BOUNDED_EVALUATION = PASS --- # 276. Evidence Readiness Assessment Evidence-related implementation and review support: source references; evidence identifiers; evidence lineage; provenance; validation history; and review-oriented outputs. The runtime does not depend on silently rewriting source evidence. Therefore: EVIDENCE_READINESS_FOR_BOUNDED_EVALUATION = PASS --- # 277. Human Review Readiness Assessment Human review is represented at several levels: architecture; runtime validation; Human Review Package construction; test surface; completion review; and baseline freeze. This provides a coherent human-control chain. Therefore: HUMAN_REVIEW_READINESS_FOR_BOUNDED_EVALUATION = PASS --- # 278. Production Readiness Assessment The current evidence does not establish production readiness. Therefore: PRODUCTION_READINESS = NOT_ESTABLISHED This is not a failure of the frozen runtime baseline. Production deployment is outside the authorized scope of the reviewed baseline. A future production-readiness decision would require separate evidence and authorization. --- # 279. Autonomous Operation Readiness Assessment Autonomous institutional operation is neither established nor authorized. Therefore: AUTONOMOUS_OPERATION_READINESS = NOT_APPLICABLE_AND_NOT_AUTHORIZED This applies to: autonomous enforcement; autonomous institutional decisions; autonomous fraud findings; autonomous legal findings; and autonomous creation of reviewed institutional knowledge. --- # 280. Public Evaluation Readiness Assessment For bounded public technical evaluation, the evidence is sufficient to present the Anomaly Library Runtime as: IMPLEMENTED TEST-BACKED FORMALLY REVIEWED FROZEN READ-ONLY EVIDENCE-PRESERVING PROVENANCE-PRESERVING PROCESS-CONTEXT-AWARE HUMAN-REVIEW-REQUIRED Therefore: PUBLIC_EVALUATION_READINESS = PASS_FOR_BOUNDED_EVALUATION subject to controlled publication scope and repository rights review. --- # 281. Commission Evaluation Readiness Assessment For Commission-facing evaluation, the Anomaly Library Runtime provides evidence of a practical implementation of LEO's broader institutional-integrity method. It demonstrates that the method is not limited to conceptual statements about: provenance; evidence review; Process Mode; anomaly interpretation; and human control. Those principles are represented in executable runtime architecture and reviewed implementation. The correct maturity statement is: COMMISSION_EVALUATION_READINESS = SUITABLE_FOR_BOUNDED_TECHNICAL_AND_METHODOLOGICAL REVIEW It is not: PRODUCTION CERTIFICATION or: REGULATORY CERTIFICATION --- # 282. Formal Evidence Decision The reviewed evidence supports the following formal decision: ANOMALY_LIBRARY_RUNTIME_EVIDENCE_DECISION = PASS_FOR_BOUNDED_PUBLIC_EVALUATION Rationale: the runtime architecture is defined; the runtime implementation exists; dedicated runtime tests exist; the reviewed test execution records 61 passed; runtime completion review is complete; architecture review passed; governance review passed; evidence review passed; provenance review passed; human-review compliance passed; runtime-safety review passed; the runtime was frozen as a controlled v0.1 baseline; evidence lineage is required; provenance is required; human review is required; autonomous learning is prohibited; autonomous enforcement is prohibited; production mutation is prohibited; fraud verdicts are prohibited; legal verdicts are prohibited. The evidence is therefore sufficient for the bounded claim and insufficient for a broader production or autonomous-authority claim. --- # 283. Formal Non-Decision This review does not decide: PRODUCTION DEPLOYMENT PRODUCTION AUTHORIZATION REGULATORY CERTIFICATION LEGAL COMPLIANCE CERTIFICATION AUTONOMOUS ENFORCEMENT AUTHORIZATION AUTONOMOUS LEARNING AUTHORIZATION FRAUD DETERMINATION AUTHORITY LEGAL DETERMINATION AUTHORITY FULL TRANSITION-STATE IMPLEMENTATION COMPLETE LEO SYSTEM READINESS These remain outside this evidence decision. --- # 284. Architectural Continuity Statement The Anomaly Library Runtime evidence is consistent with the broader LEO architectural dependency: SOURCE EVIDENCE -> EVIDENCE-DERIVED CHARACTERISTICS -> SIGNAL ELIGIBILITY -> PROCESS MODE -> HUMAN REVIEW -> REVIEWED PROCESS CONTEXT -> ANOMALY INTERPRETATION -> HUMAN REVIEW -> REVIEWED / INSTITUTIONAL KNOWLEDGE The runtime does not collapse these layers into one automated decision process. It preserves the separation between evidence, validation, interpretation, review, and knowledge. --- # 285. Institutional Memory Continuity The Anomaly Library supports LEO's institutional-memory objective only when history remains inspectable. Accordingly, future evolution should preserve: ORIGINAL ARTIFACT SOURCE EVIDENCE PROVENANCE REVIEW STATE CLASSIFICATION CORRECTION SUPERSESSION HISTORICAL RELATIONSHIP Archive continuity is therefore part of the integrity architecture. Historical records should not be destroyed merely because a later review changes their interpretation. --- # 286. Correction Continuity Corrections should be represented as traceable events. The preferred model is: EARLIER REVIEWED STATE -> NEW EVIDENCE OR REVIEW -> CORRECTION -> NEW REVIEWED STATE while preserving the earlier state as historical evidence. This prevents correction from becoming silent historical rewriting. --- # 287. Provenance Continuity Every future extension of the Anomaly Library should preserve provenance across module boundaries. If anomaly evidence later contributes to Reviewed / Institutional Knowledge, the knowledge layer should remain able to identify the reviewed anomaly and underlying evidence lineage from which the knowledge claim arose. This prevents institutional memory from becoming detached from its evidentiary basis. --- # 288. Human Governance Continuity Human governance remains above the runtime layers. The architecture must continue to prevent technical capability from silently becoming institutional authority. The governing relationship remains: RUNTIME MAY SUPPORT HUMAN MAY REVIEW AUTHORIZED INSTITUTION MAY DECIDE subject to the applicable institutional and legal framework. --- # 289. Public Evaluation Continuity Future public-evaluation updates should preserve explicit separation among: CANONICAL LEO ROOT PUBLICATION WORKING REPOSITORY PUBLIC GITHUB REPOSITORY VERIFICATION CLONES HISTORICAL PUBLIC ARTIFACTS No artifact should be assumed equivalent across these repository classes without verification. This is necessary to prevent publication evidence from drifting away from the canonical architecture. --- # 290. Rights and Publication Continuity Technical readiness alone does not authorize publication. Public evaluation artifacts remain subject to: artifact-to-license mapping; third-party rights review; attribution review; provenance review; public-scope review; and explicit human publication authorization. The Anomaly Library evidence package must remain inside that controlled publication process. --- # 291. Current Baseline Statement The strongest supported current baseline statement is: LEO ANOMALY LIBRARY RUNTIME v0.1 IS A FROZEN, READ-ONLY, TEST-BACKED, HUMAN-REVIEW-REQUIRED RUNTIME BASELINE FOR VALIDATING REVIEWED ANOMALY ARTIFACTS, PRESERVING EVIDENCE LINEAGE AND PROVENANCE, VALIDATING APPLICABLE PROCESS AND REVIEW CONTEXT, AND GENERATING STRUCTURED HUMAN REVIEW SUPPORT. This statement is intentionally bounded. --- # 292. Current Non-Capability Statement The current baseline is not: A FRAUD DETECTION VERDICT ENGINE A LEGAL DECISION ENGINE AN AUTONOMOUS ENFORCEMENT SYSTEM AN AUTONOMOUS LEARNING SYSTEM A PRODUCTION-MUTATION SYSTEM A COMPLETE TEMPORAL PROCESS-TRANSITION ENGINE A COMPLETE INSTITUTIONAL KNOWLEDGE ENGINE A COMPLETE PRODUCTION DEPLOYMENT OF LEO These exclusions are part of the architecture, not disclaimers added after implementation. --- # 293. Evaluator-Facing Summary For an external evaluator, the Anomaly Library Runtime demonstrates a practical LEO design principle: MACHINE EXECUTION CAN ORGANIZE, VALIDATE, TRACE, AND PREPARE EVIDENCE WITHOUT ACQUIRING THE AUTHORITY TO TURN THAT EVIDENCE INTO AN UNREVIEWED INSTITUTIONAL VERDICT. This principle connects technical implementation to institutional governance. --- # 294. Methodological Summary The Anomaly Library should therefore be understood as a controlled institutional-memory and anomaly-review layer. Its core methodological relationship is: EVIDENCE -> PROVENANCE -> PROCESS CONTEXT -> ANOMALY CONTEXT -> REVIEW -> CORRECTION HISTORY -> CONTROLLED INSTITUTIONAL MEMORY The runtime implements a bounded portion of this larger architecture. --- # 295. Commission-Facing Significance For Commission evaluation, the significance of this runtime is that LEO can be examined as an engineering method rather than only as a conceptual governance proposal. The evidence demonstrates concrete mechanisms for: preserving evidence lineage; preserving provenance; validating structured anomaly artifacts; maintaining Process Mode context; preserving human review; separating validation from verdicts; and freezing reviewed runtime baselines. These are directly inspectable engineering properties. --- # 296. Commission-Facing Caution The same evidence should not be used to imply that the current public repository represents every canonical LEO capability. The public repository is a curated evaluation surface. Canonical development may contain newer or broader architecture. The evaluator-facing package should therefore identify what is: CURRENTLY PUBLISHED CURRENTLY IMPLEMENTED CURRENTLY REVIEWED CURRENTLY FROZEN HISTORICAL FUTURE / DEFERRED where those distinctions materially affect interpretation. --- # 297. Transition-State Follow-Up The identified interest in transitions between deterministic and stochastic process behavior should be preserved as a distinct next evaluator-significant architectural topic. The next work should not simply add transition terminology to the current runtime. It should first establish whether the canonical architecture contains sufficient reviewed evidence to define: state identity; temporal identity; transition identity; observation windows; evidence requirements; provenance requirements; transition uncertainty; human confirmation; correction history; and anomaly-library interaction. Only then should implementation or public capability claims be considered. --- # 298. Transition-State Safety Boundary Any future transition model must preserve: NO AUTOMATIC DRIFT VERDICT NO AUTOMATIC RISK VERDICT NO AUTOMATIC FRAUD VERDICT NO AUTOMATIC LEGAL VERDICT NO AUTOMATIC PROCESS CORRECTION NO PRODUCTION MUTATION HUMAN REVIEW REQUIRED This prevents temporal analysis from becoming an autonomous institutional surveillance or enforcement mechanism. --- # 299. Transition-State Evaluation Opportunity If implemented correctly, transition-state analysis could provide substantial evaluator value. It could demonstrate that LEO does not treat process character as permanently static. Instead, LEO could potentially preserve reviewed evidence of how institutional processes evolve. The future methodological chain could become: PROCESS STATE AT t1 -> NEW EVIDENCE -> PROCESS STATE AT t2 -> TRANSITION REVIEW -> ANOMALY CONTEXT UPDATE -> HUMAN REVIEW This remains a future opportunity, not a current runtime claim. --- # 300. Architectural Continuity Decision The current Anomaly Library Runtime evidence remains consistent with the foundational LEO principles: HUMAN REVIEW REQUIRED NO AUTONOMOUS ENFORCEMENT NO AUTONOMOUS LEARNING NO PRODUCTION MUTATION NO FRAUD VERDICTS NO LEGAL VERDICTS EVIDENCE LINEAGE PRESERVED PROVENANCE PRESERVED REVIEW HISTORY PRESERVED ARCHIVE CONTINUITY REQUIRED No architectural contradiction requiring correction has been identified by this evaluator-facing evidence synthesis within the reviewed scope. Therefore: ARCHITECTURAL_CONTINUITY = PASS for the bounded evidence package. --- # 301. Implementation Readiness Decision The reviewed implementation is sufficient for its frozen read-only runtime scope. Therefore: IMPLEMENTATION_READINESS = PASS_FOR_FROZEN_READ_ONLY_RUNTIME_SCOPE This decision does not extend to production deployment. --- # 302. Public Evaluation Decision The evidence package supports external technical and methodological inspection of the reviewed Anomaly Library Runtime baseline. Therefore: PUBLIC_EVALUATION_DECISION = PASS_FOR_BOUNDED_PUBLIC_EVALUATION subject to controlled publication and rights review. --- # 303. Production Authorization Decision No production authorization is established by this evidence package. Therefore: PRODUCTION_AUTHORIZATION = FALSE --- # 304. Autonomous Enforcement Decision No autonomous enforcement authority is established. Therefore: AUTONOMOUS_ENFORCEMENT = FALSE --- # 305. Autonomous Learning Decision No autonomous anomaly-learning authority is established. Therefore: AUTONOMOUS_LEARNING = FALSE --- # 306. Fraud Verdict Decision No fraud-verdict authority is established. Therefore: FRAUD_VERDICT_AUTHORITY = FALSE --- # 307. Legal Verdict Decision No legal-verdict authority is established. Therefore: LEGAL_VERDICT_AUTHORITY = FALSE --- # 308. Human Review Decision Human review remains mandatory for institutionally significant interpretation. Therefore: HUMAN_REVIEW_REQUIRED = TRUE --- # 309. Evidence Lineage Decision Evidence lineage remains mandatory. Therefore: EVIDENCE_LINEAGE_REQUIRED = TRUE --- # 310. Provenance Decision Provenance preservation remains mandatory. Therefore: PROVENANCE_REQUIRED = TRUE --- # 311. Transition-State Decision Transition-state analysis between deterministic and stochastic process behavior is recognized as evaluator-significant future work. The current decision is: TRANSITION_STATE_EVALUATOR_INTEREST = RECORDED TRANSITION_STATE_ARCHITECTURAL_RELEVANCE = HIGH TRANSITION_STATE_FULL_RUNTIME_IMPLEMENTATION = NOT_ESTABLISHED_BY_THIS_BASELINE TRANSITION_STATE_PUBLIC_CAPABILITY_CLAIM = NOT_AUTHORIZED_BY_THIS DOCUMENT SEPARATE_ARCHITECTURAL_REVIEW_REQUIRED = TRUE This preserves the topic without overstating present capability. --- # 312. Final Evidence Status The final evidence status for this package is: ANOMALY_LIBRARY_RUNTIME_EVIDENCE_STATUS = VERIFIED_FOR_BOUNDED_PUBLIC_EVALUATION EVIDENCE_RESULT = PASS_FOR_BOUNDED_PUBLIC_EVALUATION RUNTIME_BASELINE = ANOMALY_LIBRARY_RUNTIME_v0.1 RUNTIME_STATUS = READ_ONLY_RUNTIME_COMPLETE RUNTIME_FREEZE = APPROVED RECORDED_RUNTIME_TEST_RESULT = 61_PASSED HUMAN_REVIEW_REQUIRED = TRUE EVIDENCE_LINEAGE_REQUIRED = TRUE PROVENANCE_REQUIRED = TRUE PRODUCTION_AUTHORIZATION = FALSE AUTONOMOUS_LEARNING = FALSE AUTONOMOUS_ENFORCEMENT = FALSE FRAUD_VERDICT_AUTHORITY = FALSE LEGAL_VERDICT_AUTHORITY = FALSE TRANSITION_STATE_FULL_IMPLEMENTATION = NOT_ESTABLISHED_BY_THIS_BASELINE --- # 313. Final Review Decision The Anomaly Library Runtime v0.1 evidence package is accepted as a coherent evaluator-facing representation of the reviewed frozen runtime baseline. The package demonstrates that the Anomaly Library is not architecture-only. It connects: ARCHITECTURAL PURPOSE -> IMPLEMENTED RUNTIME -> DEDICATED VALIDATORS -> PIPELINE -> HUMAN REVIEW PACKAGE -> TEST EVIDENCE -> FORMAL REVIEW -> BASELINE FREEZE -> PUBLIC EVALUATION BOUNDARY The evidence supports bounded technical and methodological evaluation. It does not support production or autonomous institutional authority claims. --- # 314. Architectural Continuity Statement The final continuity assessment is: PROCESS MODE DEPENDENCY = PRESERVED ANOMALY LIBRARY BOUNDARY = PRESERVED EVIDENCE / KNOWLEDGE DISTINCTION = PRESERVED EVIDENCE LINEAGE = PRESERVED PROVENANCE = PRESERVED HUMAN REVIEW = PRESERVED READ_ONLY RUNTIME = PRESERVED NO AUTONOMOUS LEARNING = PRESERVED NO AUTONOMOUS ENFORCEMENT = PRESERVED NO FRAUD VERDICT = PRESERVED NO LEGAL VERDICT = PRESERVED ARCHIVE / HISTORY CONTINUITY = REQUIRED TRANSITION-STATE FUTURE WORK = EXPLICITLY SEPARATED No evaluator-facing simplification in this package is intended to override the canonical architecture. --- # 315. Next Authorized Phase Completion of this evidence package does not automatically authorize another implementation phase. Within the current Commission-readiness track, the next controlled activity should be: ANOMALY_LIBRARY_PUBLIC_EVALUATION_INTEGRATION_REVIEW The purpose of that review is to determine how this evidence package should be connected to the existing public evaluator route without overstating the current public runtime surface. The initial mode must be: READ_ONLY The review should determine: which public entry points should reference this package; whether the public repository contains sufficient inspectable Anomaly Library implementation evidence; whether additional bounded runtime/test evidence should be published; whether any publication rights issues apply; whether historical anomaly demonstrations need clearer separation; whether Process Mode and Anomaly Library evidence routes remain architecturally consistent; whether the transition-state topic should remain deferred or receive a separate architecture review after the current publication phase; and whether the resulting public route is suitable for Commission evaluation. No file mutation is authorized by this section itself. No GitHub publication is authorized by this section itself. Human approval remains required before the next mutation step. --- # 316. Final Controlled Status DOCUMENT = ANOMALY_LIBRARY_RUNTIME_EVIDENCE.md DOCUMENT_ROLE = COMMISSION_FACING_BOUNDED_RUNTIME_EVIDENCE_PACKAGE ANOMALY_LIBRARY_RUNTIME_EVIDENCE_STATUS = VERIFIED_FOR_BOUNDED_PUBLIC_EVALUATION EVIDENCE_RESULT = PASS_FOR_BOUNDED_PUBLIC_EVALUATION ARCHITECTURAL_RISK_STATUS = ACCEPTABLE_FOR_BOUNDED_PUBLIC_EVALUATION IMPLEMENTATION_READINESS = PASS_FOR_FROZEN_READ_ONLY_RUNTIME_SCOPE PUBLIC_EVALUATION_READINESS = PASS_FOR_BOUNDED_EVALUATION COMMISSION_EVALUATION_READINESS = SUITABLE_FOR_BOUNDED_TECHNICAL_AND_METHODOLOGICAL_REVIEW ARCHITECTURAL_CONTINUITY = PASS PRODUCTION_READINESS = NOT_ESTABLISHED PRODUCTION_AUTHORIZATION = FALSE AUTONOMOUS_LEARNING = FALSE AUTONOMOUS_ENFORCEMENT = FALSE FRAUD_VERDICT_AUTHORITY = FALSE LEGAL_VERDICT_AUTHORITY = FALSE HUMAN_REVIEW_REQUIRED = TRUE EVIDENCE_LINEAGE_REQUIRED = TRUE PROVENANCE_REQUIRED = TRUE TRANSITION_STATE_EVALUATOR_INTEREST = RECORDED TRANSITION_STATE_FULL_RUNTIME_IMPLEMENTATION = NOT_ESTABLISHED_BY_THIS_BASELINE NEXT_AUTHORIZED_PHASE = ANOMALY_LIBRARY_PUBLIC_EVALUATION_INTEGRATION_REVIEW NEXT_PHASE_INITIAL_MODE = READ_ONLY GITHUB_PUBLICATION = NOT_AUTHORIZED_BY_THIS DOCUMENT The Anomaly Library Runtime evidence package is complete for the current controlled public-evaluation scope.