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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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Source

Plain-Language Brief

The paper discusses the challenges in managing shared knowledge in multi-agent language models (LLMs) environments and proposes solutions to prevent four failure modes: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse. The authors introduce four systems-level primitives - scoped retrieval, temporal supersession, provenance tracking, and policy-governed memory propagation. They implement these primitives in a production multi-tenant memory service called MemClaw and evaluate it using ArgusFleet. The evaluation shows that the service successfully reconstructed derivation chains and demonstrated high intra-fleet visibility with zero cross-fleet leakage. However, issues were discovered during the study, such as asymmetric scope enforcement and pipeline ordering conflict.

What the Document Says

  • The paper discusses a problem in multi-agent LLM environments: managing shared knowledge requires robust mechanisms.
  • Four failure modes are identified: unauthorized leakage, stale propagation, contradiction persistence, and provenance collapse.
  • The authors propose four systems-level primitives to address these failures: scoped retrieval, temporal supersession, provenance tracking, policy-governed memory propagation.
  • These primitives are implemented in MemClaw, a production multi-tenant memory service.
  • Evaluation via ArgusFleet shows successful reconstruction of derivation chains and high intra-fleet visibility with zero cross-fleet leakage.
  • Issues discovered during the study include asymmetric scope enforcement and pipeline ordering conflict.

Key Concepts Mentioned

  • Multi-agent LLM environments
  • Shared knowledge management
  • Fleet-memory problem
  • Failure modes
  • Systems-level primitives
  • MemClaw
  • ArgusFleet

Claims Made by the Document

  • The proposed systems-level primitives can address the identified failure modes.
  • MemClaw and ArgusFleet effectively manage shared knowledge in multi-agent LLM environments.

Evidence Used

  • Description of the proposed systems-level primitives
  • Evaluation results showing successful reconstruction of derivation chains and high intra-fleet visibility

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

  • The study measures a live production service, which may not cover all potential issues or edge cases.

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

  • What are the potential solutions for pipeline ordering conflict?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper emphasizes live evaluation as essential to expose enforcement and pipeline-ordering failures. This implies that these failures may exist in designs that have not been tested in a live production environment.

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.