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Source note3 min read

Governed Shared Memory for Multi-Agent LLM Systems

Source

arXiv

arxiv.org/abs/2606.24535v1

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Plain-Language Brief

The paper proposes a solution for managing shared knowledge in multi-agent language model (LLM) environments. The authors identify four failure modes and introduce four primitives: scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation. These are implemented in MemClaw, a production multi-tenant memory service, which was evaluated using ArgusFleet. Key findings show successful reconstruction of derivation chains with correct writer identity and zero cross-fleet leakage. However, the study also discovered issues such as asymmetric scope enforcement and pipeline ordering conflict.

What the Document Says

  • The paper discusses the need for robust mechanisms for shared knowledge management in multi-agent LLM environments.
  • Four failure modes are identified: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse.
  • Four primitives are defined to address these issues: scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation.
  • These primitives are implemented in MemClaw, a production multi-tenant memory service.
  • Evaluation was done via ArgusFleet, testing four governance dimensions.
  • The study measures a live production service rather than using a baseline comparison.
  • Key evaluation results include successful reconstruction of derivation chains and zero cross-fleet leakage.
  • An issue of asymmetric scope enforcement was discovered during the study.
  • A pipeline ordering conflict was also found.

Key Concepts Mentioned

  • multi-agent LLM environments
  • shared knowledge management
  • failure modes
  • primitives
  • MemClaw
  • ArgusFleet
  • provenance
  • production multi-tenant memory service

Claims Made by the Document

  • Robust mechanisms for shared knowledge management are required in multi-agent LLM environments.
  • The identified failure modes can affect multi-agent LLM systems.
  • The proposed primitives can address the identified failure modes.
  • MemClaw and ArgusFleet were used to evaluate the effectiveness of these primitives.

Evidence Used

  • The text describes the need for robust mechanisms, identifies failure modes, defines primitives, mentions MemClaw and ArgusFleet, and details evaluation results.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

Multi-agent LLM environments require robust mechanisms for shared knowledge management. This paper formalizes the fleet-memory problem and identifies four foundational failure modes: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse. To address these, we define explicit systems-level primitives: scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation. These primitives are implemented in MemClaw, a production multi-tenant memory service, and evaluated via ArgusFleet, a reproducible harness testing four governance dimensions. Rather than a baseline comparison, this study measures a live production service, emphasizing real-world architectural insights and negative results. Key Evaluation Results Provenance: Successfully reconstructed 100% of depth-four derivation chains with correct writer identity at sub-second per-hop latency. Propagation: Demonstrated high intra-fleet visibility with zero cross-fleet leakage. Under strong write mode, write-to-visible latency was optimized to a single search round-trip. Production Architectural Issues Discovered Asymmetric Scope Enforcement: Tenant isolation held, but sub-tenant scope was initially bypassed on direct GET-by-id requests for agent-scoped credentials (disclosed and remediated during the study). Pipeline Ordering Conflict: While contradiction supersession works for admitted writes, a synchronous near-duplicate gate can prematurely reject contradictory writes before the asynchronous contradiction detector can evaluate them. Conclusion: Long-context retrieval alone is insufficient for production multi-agent memory. Governed shared memory demands explicit systems-level abstractions, and live evaluation is vital to expose enforcement and pipeline-ordering failures missed by design-only treatments.