Problem
Research questions and friction points this paper is trying to address.
long-horizon
multi-actor
conversational memory
evidence
Propose, Verify, Commit: Evidence-Grounded Memory for Long-Horizon Multi-Actor Conversations
🤖 AI Summary
本文提出EGMEMORY，通过证据驱动的提议-验证-提交协议解决多参与者长对话中的记忆挑战，实现高效状态管理和查询。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Evidence-Grounded Memory
Propose-Verify-Commit Protocol
Adaptive Evidence Navigation
Long-Horizon Conversations
Multi-Actor Settings
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