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How to verify Evo VLM's recorded results on a CPU with Evo VLM

Replay the recorded evidence and run the final checks on a CPU, without model weights or new inference.

  • Top pick
  • Research
  • Intermediate
  • 15 minutes
  • From the README
  • Any agent

Who it is forA research reviewer or lab member who needs to check the Dev128 and Speed32 numbers before citing Evo VLM.

The problemPublished accuracy and latency numbers are hard to trust without a way to confirm they match the underlying per-query evidence.

The outcomeThe reviewer confirms that the recorded metrics recompute from the per-query evidence and that file hashes and checks pass.

What you need first

  • Git and Python 3 installed locally
  • A clone of https://github.com/zzzz7788990213-ops/EvoVLM
  • For GPU inference only: a CUDA-capable GPU, PyTorch, Transformers, and the external Qwen model snapshots described in the runtime guide

Steps

  1. git clone https://github.com/zzzz7788990213-ops/EvoVLM.git
  2. cd EvoVLM/research/evolving-vlm-inference-main
  3. python3 -B -m evaluation.portable_reproduce
  4. bash reproduce.sh check
  5. Open the sealed metric trace at report/generated/report_number_trace.json and compare its values with the results table in the top-level README.

Prompt for your agent

Clone https://github.com/zzzz7788990213-ops/EvoVLM, enter research/evolving-vlm-inference-main, run `python3 -B -m evaluation.portable_reproduce` and then `bash reproduce.sh check`. Report the pass or fail status of each command and whether the recomputed Dev128 correct counts match the results table in the README. Do not run GPU inference and do not use `bash reproduce.sh` without a mode.

Caveat. These checks replay recorded evidence and do not rerun inference, so they cannot confirm that the numbers reproduce on new hardware, and the README says latency is specific to the recorded environment.

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