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How to evaluate SpeakerMem on SocialMemBench with a local Writer with Speaker Mem R1

Run the main SpeakerMem evaluation with a locally served Writer model instead of an external LLM.

  • Research
  • Advanced
  • 2 hours
  • From the README
  • Any agent

Credentials. The answerer and judge calls need API credentials, which must stay in a local .env file outside version control.

Who it is forA researcher reproducing the paper's main SocialMemBench result who wants the memory-writing step to run on their own hardware.

The problemThe full dual-track pipeline needs a writer and answerer, and the paper's main numbers depend on configuration choices like top-k and no-ASK settings.

The outcomeA local results folder with SpeakerMem question-level outputs for SocialMemBench using the default main configuration.

What you need first

  • Python with the ability to create a virtual environment
  • A CPU environment is enough for the quickstart and offline checks
  • Model service credentials (kept outside version control) for full benchmark runs
  • Benchmark payloads placed as described in DATA_SOURCES.md for full evaluations

Steps

  1. Place the SocialMemBench payload as described in DATA_SOURCES.md under code/ and set credentials from code/.env.example.
  2. Change into the code directory with cd code.
  3. Run PYTHONPATH=.:speakermem_pkg/src python speakermem_pkg/examples/socialmembench.py --benchmark socialmem --domain all --top-k 10 --s2-k 2 --s2-source-k 1 --results-dir ../results/local_run. (inferred, check the README)
  4. Add --writer-vllm-url and --writer-vllm-model to point the run at your locally served Writer, and use --ingest-only first if you want to build the memory store before QA.
  5. Run python speakermem_pkg/examples/socialmembench.py --help to check any other options you need.

Prompt for your agent

In the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1), run speakermem_pkg/examples/socialmembench.py on SocialMemBench with --top-k 10 --s2-k 2 --s2-source-k 1 and a local Writer URL given as <writer-url>. Expected output is results written to the results directory you choose. Verify that the summary reports accuracy for the socialmem domains and that no ASK route was enabled, since the README says the principal comparison uses the no-ASK configuration.

Caveat. The README gives no full reproduction time or hardware needs, and the full-LLM sensitivity study also needs an external held-out memory store that the repo does not include.

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