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P217 — AIEP — Knowledge Distillation and Compression Engine

Applicant: Neil Grassby Classification: Patent Application — Confidential Priority: Claims priority from GB2519711.2 filed 20 November 2025 Architecture Layer: AIEP AGI Cognition Layer — Phase 2

Framework Context

[0001] This specification operates within an AIEP environment as defined in GB2519711.2 and GB2519798.9. The present specification defines the knowledge distillation and compression mechanism of the Phase-2 AIEP cognition architecture, enabling the system to extract high-value, durable knowledge from the evidence corpus and world state model in a compact, provenance-preserving form.

Field of the Invention

[0002] The present invention relates to knowledge distillation systems and evidence-bound knowledge compression architectures for artificial intelligence.

[0003] More particularly, the invention relates to a system that identifies high-utility, stable knowledge claims in the world state model and evidence corpus, represents those claims in a compact distilled form, and preserves the provenance chain linking distilled claims to their originating evidence artefacts.

Background

[0004] Accumulating evidence-bound world state over time produces expanding evidence corpora and increasingly large world state graphs. As corpora grow, reasoning latency increases and it becomes computationally costly to retrieve the most relevant knowledge for a given reasoning task.

[0005] Knowledge distillation in existing AI systems relies on parametric compression (embedding models) that breaks provenance chains. Evidence-bound architectures require distillation mechanisms that preserve provenance so that distilled claims remain auditable and can be invalidated when their evidence basis is contradicted.

Summary of the Invention

[0006] The invention provides a Knowledge Distillation and Compression Engine (KDCE) that: selects knowledge distillation candidates using the Knowledge Utility Scoring system (P232); compresses selected knowledge into distilled claim records; preserves provenance chains linking distilled claims to source evidence artefacts and CWSG nodes; evaluates distilled claims for staleness on each evidence admission cycle; and integrates with the Abstraction Extraction Engine (P231) to identify abstracted patterns eligible for distillation.

[0007] Distilled claim records are formally equivalent to knowledge summaries but retain full provenance metadata, enabling downstream reasoning systems to use distilled claims without losing the ability to trace claims back to evidence artefacts.

ASCII Architecture

Knowledge Utility Scorer (P232)
      |
      v  (high-utility candidates)
+----------------------------------------------+
| Knowledge Distillation Engine                |
|                                              |
|  Candidate selection                        |
|  Compression (structure-preserving)         |
|  Provenance chain preservation              |
|  Staleness evaluation binding               |
+-------------------+--------------------------+
                    |
                    v
+----------------------------------------------+
| Distilled Claim Store                        |
|  claim_id, content, provenance_chain,        |
|  utility_score, stale_flag                   |
+-------------------+--------------------------+
           |                    |
           v                    v
  Reasoning Retrieval       Staleness Check
  Interface                 (per CWSG update cycle)
  (P208, P200)              → invalidate stale claims

Definitions

[0008] Knowledge Distillation and Compression Engine (KDCE): The subsystem that compresses high-utility knowledge items into distilled claim records preserving provenance chains, and evaluates distilled claims for staleness on each evidence admission cycle.

[0009] Distilled Claim Record: A compressed knowledge summary record comprising: a claim identifier, the distilled claim content, a provenance chain linking the claim to source evidence artefacts and CWSG nodes, a utility score, a staleness flag, and compression timestamp.

[0010] Provenance Chain: An ordered list of source references linking a distilled claim back to the sequence of evidence artefacts, knowledge nodes, and CWSG entities from which it was derived.

[0011] Structure-Preserving Compression: A compression method that reduces the storage footprint of knowledge items while retaining their full provenance chain and logical content, permitting downstream reasoning systems to reconstruct the evidentiary basis of the claim.

[0012] Staleness Evaluation: The process of assessing whether a distilled claim’s provenance evidence artefacts remain valid on the current CWSG snapshot, based on the staleness evaluation rules of the Long-Term Memory Engine (P208).

Detailed Description

Candidate Selection. [0013] The KDCE subscribes to candidate reports from the Knowledge Utility Scoring Engine (P232). On each scoring cycle, high-utility knowledge items whose utility score exceeds the distillation threshold (policy-defined) are forwarded to the KDCE as distillation candidates. The KDCE also receives pattern candidates from the Abstraction Extraction Engine (P231), which identifies recurring structural patterns eligible for distilled representation across multiple source artefacts.

Structure-Preserving Compression. [0014] For each selected candidate, the KDCE applies a structure-preserving compression algorithm. The algorithm extracts the semantic content of the knowledge item and expresses it as a distilled claim statement while retaining: the full list of source evidence artefact identifiers; the CWSG entity identifiers to which the claim applies; and the logical inference chain connecting sources to the distilled statement. The compression ratio is bounded by a maximum information loss coefficient defined in the active governance policy; candidates that cannot be compressed within the permitted loss bound are retained in their original form with a compression-refused flag.

Provenance Chain Construction. [0015] The provenance chain is constructed as an ordered list from each distilled claim’s source artefacts to the distilled claim record. Each link in the chain is a signed reference comprising: source record identifier, the type of relationship (DERIVED_FROM, ABSTRACTED_FROM, or PATTERN_OF), and the CWSG snapshot timestamp at the time of distillation. The full provenance chain enables the evidence support evaluator (P214) to verify a distilled claim’s evidentiary basis without accessing the full source artefact corpus.

Distilled Claim Store Management. [0016] Distilled claim records are stored in the distilled claim store indexed by claim identifier, entity identifier, and provenance chain depth. The store is append-only for new records; existing records may only be modified by setting the staleness flag (no content modification). Claims in the store are retrievable by the Long-Term Memory Engine (P208) and the Causal World State Graph (P200) through the reasoning retrieval interface.

Staleness Evaluation. [0017] On each CWSG update cycle, the KDCE evaluates all distilled claims whose provenance chains reference entity nodes affected by the update. A distilled claim is marked stale if any of its source evidence artefacts have been invalidated or if the CWSG entity state on which the distilled claim is predicated has materially changed. Stale claims remain in the store with the stale flag set; they are not deleted, as the history of their period of validity may be needed for audit. Stale claims are excluded from retrieval by default but may be accessed explicitly for historical reasoning.

Technical Effect

[0018] The invention provides provenance-preserving knowledge compression for evidence-bound AI systems. By retaining full provenance chains in distilled claim records, the system enables compressed knowledge to be used directly by downstream reasoning systems without losing evidence traceability. By evaluating staleness on each CWSG update cycle, the system ensures that distilled claims reflect only currently valid evidence. By maintaining stale-flagged records rather than deleting them, the system preserves the complete history of the knowledge base’s evolution for audit and temporal reasoning.

Claims

A computer-implemented method for knowledge distillation and compression, the method comprising: (a) receiving high-utility knowledge candidates from a Knowledge Utility Scoring Engine and pattern candidates from an Abstraction Extraction Engine; (b) applying a structure-preserving compression algorithm to each candidate, bounded by a policy-defined maximum information loss coefficient, and retaining source evidence artefact identifiers, CWSG entity identifiers, and the logical inference chain in each distilled claim record; (c) constructing a provenance chain for each distilled claim as an ordered list of signed source references linking the distilled claim back to its evidentiary basis; (d) storing distilled claim records in an append-only distilled claim store indexed by claim identifier, entity identifier, and provenance chain depth; and (e) evaluating distilled claim staleness on each CWSG update cycle by checking whether source artefacts or predicated entity states have materially changed, and setting the staleness flag on affected claims without deleting them.
The method of claim 1, wherein candidates that cannot be compressed within the permitted information loss bound are retained in their original form with a compression-refused flag rather than rejected.
The method of claim 1, wherein provenance chain links are signed references comprising source record identifier, relationship type, and CWSG snapshot timestamp at distillation time.
The method of claim 1, wherein stale-flagged claims are excluded from standard reasoning retrieval but remain accessible for historical and temporal reasoning queries.
The method of claim 1, wherein the distilled claim store is append-only for new records, and only the staleness flag may be modified on existing records.
A Knowledge Distillation and Compression Engine comprising: one or more processors; memory storing a distilled claim store, provenance chain builder, and staleness evaluation index; wherein the processors are configured to execute the method of claim 1.
A non-transitory computer-readable medium storing instructions that, when executed by a processor, implement the method of claim 1.

Abstract

A knowledge distillation and compression engine for evidence-bound artificial intelligence applies structure-preserving compression to high-utility knowledge candidates, retaining full provenance chains linking distilled claims to source evidence artefacts and Causal World State Graph entities. Distilled claim records are maintained in an append-only store and evaluated for staleness on each CWSG update cycle, with stale claims flagged rather than deleted to preserve audit history. The compressed knowledge base enables downstream reasoning systems to use distilled claims without losing evidence traceability. +----------------------------------------------+ | v Reasoning Engine Retrieval Interface


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## Detailed Description

[0008] **Distillation Candidate Selection.** The KDCE retrieves knowledge utility scores from P232 and selects candidates whose utility score exceeds a configurable distillation threshold. Candidates include: frequently referenced CWSG causal relationships; highly cited evidence artefact summaries; and abstraction patterns identified by P231.

[0009] **Compression Protocol.** Each candidate is compressed by: extracting the core causal or factual claim; removing redundant elaboration while preserving logical structure; and constructing a structured claim record with formal logical form.

[0010] **Provenance Chain.** The distilled claim record includes a `provenance_chain` — an ordered array of evidence artefact content hashes from which the claim was derived. The provenance chain enables: tracing any distilled claim back to originating evidence; detecting when evidence supporting a claim has been superseded; and re-deriving the original claim content from evidence if required.

[0011] **Staleness Evaluation.** On each evidence admission cycle, the KDCE evaluates distilled claims whose provenance chain includes recently modified or contradicted evidence artefacts. Such claims are marked stale and excluded from active reasoning retrieval until re-evaluated.

[0012] **Reasoning Integration.** The reasoning engine priority-queries the distilled claim store before the full evidence corpus for applicable claims, reducing evidence retrieval latency. Claims retrieved from the distilled store are exposed with their provenance chain so that the reasoning engine can optionally verify them.

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## Claims

1. A knowledge distillation engine for an evidence-bound reasoning architecture that compresses knowledge claims while preserving provenance chains to originating evidence artefacts.
2. The system of claim 1 wherein distillation candidates are selected by knowledge utility score.
3. The system of claim 1 wherein distilled claims are evaluated for staleness on evidence admission cycles.
4. The system of claim 1 wherein the provenance chain enables re-derivation of distilled claims from originating evidence.
5. The system of claim 1 wherein stale distilled claims are excluded from active reasoning retrieval.
6. A method of compressing evidence-bound knowledge comprising selecting high-utility candidates, extracting core claims, preserving provenance chains, and evaluating staleness on new evidence admission.