ARXIV:2603.14588 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory

arXiv

A novel mathematical framework for enhancing AI agent memory retrieval and management.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A novel mathematical framework for enhancing AI agent memory retrieval and management.

Evidence 0 refs | 0 sources | 17% coverage

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PROBLEM

A novel mathematical framework for enhancing AI agent memory retrieval and management. Current systems employ cosine similarity for retrieval, heuristic decay for salience, and provide no formal contradiction detection.

METHOD

Full abstract

Persistent memory is a central capability for AI agents, yet the mathematical foundations of memory retrieval, lifecycle management, and consistency remain unexplored. Current systems employ cosine similarity for retrieval, heuristic decay for salience, and provide no formal contradiction detection. We establish information-geometric foundations through three contributions. First, a retrieval metric derived from the Fisher information structure of diagonal Gaussian families, satisfying Riemannian metric axioms, invariant under sufficient statistics, and computable in O(d) time. Second, memory lifecycle formulated as Riemannian Langevin dynamics with proven existence and uniqueness of the stationary distribution via the Fokker-Planck equation, replacing hand-tuned decay with principled convergence guarantees. Third, a cellular sheaf model where non-trivial first cohomology classes correspond precisely to irreconcilable contradictions across memory contexts. On the LoCoMo benchmark, the mathematical layers yield +12.7 percentage points over engineering baselines across six conversations, reaching +19.9 pp on the most challenging dialogues. A four-channel retrieval architecture achieves 75% accuracy without cloud dependency. Cloud-augmented results reach 87.7%. A zero-LLM configuration satisfies EU AI Act data sovereignty requirements by architectural design. To our knowledge, this is the first work establishing information-geometric, sheaf-theoretic, and stochastic-dynamical foundations for AI agent memory systems.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. A four-channel retrieval architecture achieves 75% accuracy without cloud dependency.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.

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Opportunity summary

Score7.0

PainA novel mathematical framework for enhancing AI agent memory retrieval and management.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A novel mathematical framework for enhancing AI agent memory retrieval and management.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory

SuperLocalMemory V3: Information-Geometric Foundations for Zero-LLM Enterprise Agent Memory

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