2022hpsk / SpeakerMemR1
SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue
Why this job: matched by what the repo talks about: memory, multi, track, speaker
What it does
Excited to share our work on SpeakerMem-R1! Recent benchmarks reveal a surprising gap: general-purpose memory systems struggle with multi-party conversations and can even underperform simple BM25 retrieval in some settings. These limitations highlight two core challenges: message attribution and state reconstruction in long-term multi-party dialogue.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 49 days ago
- Stars
- 105
- +2 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 99% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- None
- No issues filed
- Pull requests
- 0 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
More: languages, topics and project record
Languages
- Python 99%
- Shell 1%
Topics
- conversational-memory
- llm
- multi-party-dialogues
Project record
- JobAgent memory
- StatusListed
- Commits7
- Forks13
- Watching0
- Stars in 28 days+2
- CreatedSep 20, 2026
- First seen by usNov 7, 2026
- CheckedNov 8, 2026