Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures

Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/graph-native-cognitive-memory-for-ai-agents-formal-belief-revision-semantics-for-versioned-memory-architectures

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID graph-native-cognitive-memory-for-ai-agents-formal-belief-revision-semantics-for-versioned-memory-architectures | Route /signal-canvas/graph-native-cognitive-memory-for-ai-agents-formal-belief-revision-semantics-for-versioned-memory-architectures

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures

PDF: https://arxiv.org/pdf/2603.17244v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Watch and verify: Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures

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Subject: Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures

Verdict

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
On LoCoMo (token-level F1), Kumiho achieves 0.565 overall F1 (n=1,986) including 97.5% adversarial refusal accuracy.
Implicationpartial
Directly stated in abstract with specific numeric results
Verificationpartial
partial
Evidencepartial
On LoCoMo-Plus, a Level-2 cognitive memory benchmark testing implicit constraint recall, Kumiho achieves 93.3% judge accuracy (n=401)
Implicationpartial
Directly stated in abstract with specific numeric results
Verificationpartial
partial
Evidencepartial
still substantially outperforming all published baselines (best: Gemini 2.5 Pro, 45.7%)
Implicationpartial
Direct comparison with specific baseline results provided
Verificationpartial
partial
Evidencepartial
The architecture implements a dual-store model (Redis working memory, Neo4j long-term graph)
Implicationpartial
Directly stated architectural implementation details
Verificationpartial
partial
Evidencepartial
proving satisfaction of the basic AGM postulates (K*2--K*6) and Hansson's belief base postulates (Relevance, Core-Retainment)
Implicationpartial
Direct statement of formal contribution with specific postulates named
Verificationpartial
partial
Evidencepartial
Three architectural innovations drive the results: prospective indexing (LLM-generated future-scenario implications indexed at write time), event extraction (structured causal events preserved in summaries), and client-side LLM reranking.
Implicationpartial
Directly stated as innovations driving results, though causal relationship requires some inference
Verificationpartial
partial
Evidencepartial
The architecture is model-decoupled: switching the answer model from GPT-4o-mini (~88%) to GPT-4o (93.3%) improves end-to-end accuracy without pipeline changes
Implicationpartial
Direct statement of model-decoupling with specific accuracy improvements
Verificationpartial
partial
Evidencepartial
The structural primitives required for cognitive memory -- immutable revisions, mutable tag pointers, typed dependency edges, URI-based addressing -- are identical to those required for managing agent-produced work as versionable assets
Implicationpartial
Direct statement about architectural unification, though the equivalence claim requires some interpretation
Verificationpartial
partial

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Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures

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