MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories
On the map: LLM agents and tool use → Agent memory systems
Abstract (arXiv)
Autonomous agents share a transport and can call each other's tools, but they cannot share what they know: no protocol lets two agents' memories reconcile a fact phrased two ways, link related facts held apart, or reconcile contradictory knowledge without silently discarding either claim. We present MELD, a self-managing coherence mechanism for a federation of agent memories whose run-time model is the knowledge graph itself. Each brain admits every incoming claim through a five-outcome procedure (insert, merge, relate, conflict, or reject), decided from three signals (scoped claim-key identity, embedding similarity, and a natural-language-inference verdict) under context and freshness gates, and acting through exactly one auditable, authenticated Patch, the only object that mutates state. A binding onto standard publish/subscribe transport with a per-claim status CRDT keeps sovereign brains coherent in claim status without a coordinator: self-healing after partitions and under lossy routing, and self-protecting against silent rewrite by a peer, under a benign-fault model. MELD does not adjudicate truth; a detected contradiction is preserved for later adjudication, never silently resolved. On HotpotQA distractor, distributed merge is recall-non-inferior to a centralized store under a pre-specified equivalence test and recall-superior to naive union at about 11% less live storage; the merge classifier separates at AUC 0.968 with a 0.013 false-merge rate on adjudicated candidate pairs; the status CRDT reconverges in 30/30 real partition-heal trials where last-writer-wins manages 11/30; and semantic routing delivers about 3x fewer messages at matched recall. We evaluate on a real computing continuum spanning an operator-grade 5G edge, national HPC, and a local tier, with empirically calibrated thresholds.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 13 of its 54 references that are in the map. First posted on arXiv 17 Aug 2026.
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13 of 54 references.
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- 2025Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control23 May
- 2025A-MEM: Agentic Memory for LLM Agents17 Feb
- 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memory20 Jan
Other references · 10
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2015A large annotated corpus for learning natural language inferencecited by 1,847
- 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
- 2023Self-Adaptive Large Language Model (LLM)-Based Multiagent Systemscited by 31
- 2016A Conflict-Free Replicated JSON Datatypecited by 6
- 2016Delta State Replicated Data Typescited by 4
- 2024Everywhere & Nowhere: Envisioning a Computing Continuum for Sciencecited by 2
- 2020Byzantine Eventual Consistency and the Fundamental Limits of Peer-to-Peer Databasescited by 1
- 2026Neural Router: Semantic Content Matching for Agentic AIcited by 1
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