SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue
SpeakerMem-R1 uses dual-track speaker memory to improve multi-party dialogue recall across GroupMem, SocialMem, and EverMem benchmarks.
SpeakerMem-R1 keeps speaker-labeled verbatim messages and derived person- and group-level states, then joins evidence by entity, event, and time. Writer-R1 is trained with SpeakerLevenshtein and speaker-conditioned GRPO to reduce attribution and update errors for local deployment. Binary accuracies are 47.9% on GroupMemBench, 69.2% on SocialMemBench, and 61.9% on EverMemBench, with 62.33% on the public EverMemBench leaderboard. It scores 70.85% on 1,986 LoCoMo questions, and reinforcement learning raises a 305-question set from 57.38% to 68.20%.
- Dual-track memory stores verbatim messages plus person- and group-level states.
- Writer-R1 is trained with SpeakerLevenshtein and speaker-conditioned GRPO.
- Reported accuracies: 47.9% GroupMemBench, 69.2% SocialMemBench, 61.9% EverMemBench.
- Public EverMemBench score is 62.33%; LoCoMo score is 70.85% on 1,986 questions.
- RL lifts a 305-question evaluation from 57.38% to 68.20%.
- Organizations
- EverMind-AI
- AI models
- SpeakerMem-R1Writer-R1
From the original article
1 min read at arxiv.orgLong-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and how states change over time.
By Haobo Zheng · arxiv.org
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