2022hpsk/SpeakerMemR1
SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue
About the project
A memory framework for multi-party dialogue: it stores utterances verbatim with speaker labels and builds structured person- and group-level states to answer questions over long conversations. Includes an inference package, benchmark runners, and Writer-model training recipes.
Useful for
- Run evaluation on GroupMemBench, SocialMemBench, or EverMemBench with the provided runners
- Train a Qwen2.5-3B Writer with SpeakerLevenshtein rewards for local memory writing
- Compare memory quality against BM25 and dense baselines via run_baseline.py
README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.
Why it’s trending
- 0 stars so far today, about 9 expected by the end of the day.
- The spike has held for 2 days in a row — not a one-off blip.
- The repository is 6 days old and already has 81 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet.
- Top new repositories this week: #162.
Stars per day
Bars are daily stars, the line is the usual pace. Red marks spike days.
Numbers
- Total stars
- 81
- Today
- 0 · ≈ 9 by evening
- Forks
- 13
- Issues and pull requests
- 0
- Watchers
- 0
- Language
- Python
- License
- MIT
- Created
- September 20, 2026
- Last push
- September 24, 2026
Star trust
Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.
These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.
Spotted in
- September 26, 2026Top new repositories this week: #162
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