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How to reproduce the sidecar vs direct video benchmark with Cdaf

Run the repo's synthetic benchmark to check the token and accuracy numbers yourself.

Who it is forA researcher or engineer who wants to verify the accuracy and token claims before adopting the format.

The problemPublished token savings are hard to trust without seeing the same measurement run on your own machine.

The outcomeYou get a RESULTS.md report comparing direct video answers with sidecar answers on the same questions.

What you need first

  • Python 3.10 or newer
  • Node.js with npx for the agent skill install
  • A Gemini API key (bring your own) for generating sidecars, or the local provider for no-key generation
  • ffmpeg, only if you run the synthetic benchmark

Steps

  1. Install ffmpeg so the benchmark can synthesize test videos.
  2. Run python benchmarks/bench.py make to synthesize the testset.
  3. Run python benchmarks/bench.py run with GEMINI_API_KEY set to query both conditions through Gemini.
  4. Run python benchmarks/bench.py report to write benchmarks/RESULTS.md.

Prompt for your agent

In the repo UditAkhourii/cdaf, run the benchmark steps `python benchmarks/bench.py make`, `run`, and `report` in order, and write the results to benchmarks/RESULTS.md. Verify that the report lists accuracy and mean prompt tokens for both the direct video condition and the CDAF sidecar condition.

Caveat. The benchmark uses synthesized videos with known colors, hard cuts, and word overlays, so its results do not yet show performance on natural footage.

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