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How to benchmark BM25 baseline retrieval on GroupMemBench with Speaker Mem R1

Run the baseline runner on a benchmark split to get accuracy numbers to compare against SpeakerMem.

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

Credentials. The run needs model service credentials that must be stored in a local .env file and never committed to the repo.

Who it is forA memory-systems researcher who wants to check how a simple BM25 retriever performs on multi-party benchmarks before judging a new memory method.

The problemReported gains are only meaningful against a controlled baseline, and the paper notes that simple BM25 retrieval can be a strong comparison point.

The outcomeA results folder with question-level outputs and metrics for the BM25 baseline on GroupMemBench.

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 benchmark payloads as described in DATA_SOURCES.md under code/.
  2. Create the environment and install with python -m pip install -r code/requirements.txt and python -m pip install -e code/speakermem_pkg.
  3. Set the model credentials in a local .env file based on code/.env.example, and keep it out of version control.
  4. Change into the code directory with cd code.
  5. Run python run_baseline.py --baseline bm25 --benchmark groupmem --domain all --top-k 10 --agent-model deepseek-v4-flash --judge-model deepseek-v4-flash --results-dir ../results/local_baseline. (inferred, check the README)

Prompt for your agent

Use the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1) from the code directory to run run_baseline.py with --baseline bm25 on the groupmem benchmark, writing to <results-dir>. Expected output is question-level results and a metrics summary in that folder. Verify that the run finished without missing-data errors and that the metrics include accuracy.

Caveat. The benchmark payloads are not in the repo, so you must obtain them yourself and check each upstream license before using or redistributing the outputs.

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