Propose, Verify, Commit: Evidence-Grounded Memory for Long-Horizon Multi-Actor Conversations

Abstract:Long-horizon conversational memory is especially challenging in multi-actor settings, where relevant evidence is distributed across participants and contexts and previously established information may later be revised. We introduce EGMEMORY, which formulates long-horizon multi-actor memory as a searchable state machine that separates persistent message-level evidence from an explicit active state. At write time, adaptive state resolution and an evidence-grounded propose-verify-commit protocol govern how this state evolves. At read time, adaptive evidence navigation iteratively resolves the state and supporting evidence required for a query, using conversational structure to narrow the search space and lexical-semantic relevance to rank candidates. The system operates through prompting and tool use without memory-specific policy training. EGMEMORY achieves 68.2% on GroupMemBench and 77.9% on EverMemBench, outperforming the strongest evaluated baselines by 22.7 and 21.4 percentage points, respectively. It further reaches 73.6% on the dyadic LoCoMo benchmark, demonstrating generalization beyond multi-actor conversations. We will release the codebase upon formal publication.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.23465 [cs.CL]

(or arXiv:2609.23465v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.23465 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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