AI summaryⓘ
The authors present MELD, a system that helps multiple autonomous agents share and manage knowledge without losing or silently discarding conflicting information. Instead of combining memories blindly, MELD uses a clear procedure to decide how new information is handled—whether to insert, merge, relate, mark as conflicting, or reject it—based on how similar or related the claims are. MELD keeps these decisions consistent across agents without a central controller, allowing the system to recover from communication problems and protect against unauthorized changes. Their tests show MELD works well compared to traditional methods and reduces communication overhead. MELD preserves contradictions for later human review rather than deciding which information is true on its own.
Autonomous agentsKnowledge graphMemory reconciliationConflict detectionMerge classifierCRDT (Conflict-free Replicated Data Type)Natural language inferencePublish/subscribe transportDistributed systemsSemantic routing
Authors
Lauri Lovén, Jaakko Sauvola, Jukka Riekki, Sasu Tarkoma
Abstract
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.