HOM-AIMOS: A Cryptographically Auditable Persistent Memory Backend for AI Agents

HOM-AIMOS serves as a robust local-first persistent memory backend designed specifically for AI agents. It integrates advanced features such as signed identity, append-only provenance, hybrid retrieval, and temporal reasoning, alongside a unique housekeeper identity responsible for autonomous maintenance independent of any enrolled user agent.

Key Features

• Complete Persistent-Memory Backend: Unlike typical provenance layers associated with vector stores, HOM-AIMOS offers a fully integrated persistent-memory solution.

• Provenance and Robust Architecture: The system architecture includes a comprehensive 275-service census and delineates six critical pipelines with 146 service connections aimed at optimizing memory operations.

Memory Operations

Save and Recall Functions

HOM-AIMOS operates through detailed stages for saving and recalling data, each managed by specific service components.

Save Operations (8 Stages)

#	Stage	Native Owner
1	Signed request and authorization	routes/aimos.js
2	Write validation	services/write/write-validator.js
3	Prediction-error routing gate	services/write/rpe-gate.js
4	Mnemonic encoding	services/context/mnemonic-encoder.js
5	Quality gate	services/write/quality-gate.js
6	Embedding	services/core/embeddings.js
7	Canonical persistence and provenance	services/write/persist-memory.js
8	Signed retained-memory epistemic label	services/security/memory-epistemic-classifier.js

Recall Operations (8 Stages)

#	Stage	Native Owner
1	Query understanding and path selection	services/retrieval/native-recall-pipeline.js
2	Embedding and candidate opening	services/core/embeddings.js
3	Similarity statistics	services/retrieval/similarity-stats.js
4	Trust scoring	services/learning/trust-score.js
5	Concept-graph retrieval	services/core/concept-graph.js
6	Verified epistemic projection and selection	services/retrieval/epistemic-trust-retrieval.js
7	Pre-disclosure calibration	services/retrieval/recall-calibrator.js
8	Bounded evidence and signed receipt	services/retrieval/native-recall.js

Cognitive Mutation

The memory maintained by HOM-AIMOS is dynamic rather than static post-admission. It enables mutable retrieval weights, which can adjust based on new evidence, within defined boundaries while retaining immutable canonical content. This cognitive mutation allows for:

• Outcome Adaptation: Signed evidence can be appended to the memory ledger to update its retrieval frequency.
• SPICED Consolidation: Strengthening of retained memories through a dedicated service.
• Hebbian Consensus: Enhancing supported hubs while managing conflicting evidence.

HOM-AIMOS also includes rigorous verification methods for all changes made to the memory, ensuring continuity and integrity against unauthorized modifications.

High-Performance Metrics

HOM-AIMOS is evaluated through various metrics showcasing its efficiency and reliability:

Result	Value
LongMemEval	459/500 — 91.8%
LoCoMo	1472/1986 — 74.12%
Mutation authorization rejection	7/7
Cognitive tamper cases detected	4/4

Security and Data Integrity

Central to the design of HOM-AIMOS is the emphasis on integrity over completeness. The system ensures that once data is securely embedded within its architecture, it cannot be altered without detection. Each memory transition is documented, preserved, and bound to cryptographic signatures which assures that unauthorized changes do not occur without detection.

Release Information

Currently available as the HOM-AIMOS 1.0 source release, this repository comes equipped with comprehensive security measures, detailed architecture maps, and clear verification protocols that should be reviewed prior to deploying or integrating this solution.

For more detailed information on installations, architecture, and operational guidelines, refer to the associated documentation, including SECURITY.md and ARCHITECTURE-MAP.md.

Conclusion

HOM-AIMOS stands as a revolutionary solution for persistent memory management in AI agents, equipped with extensive features for secure data handling and cognitive adaptability, suitable for developers and researchers focusing on advanced AI applications.