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Anonymous Release — Code
Source code for the benchmark, evaluation, and judge-training pipeline that
accompanies the companion data release
phyground and the LoRA judge adapter under
../model/.
This drop contains 43 Python source files plus the HTML/CSS/JS assets needed by the human-annotation app. Shell scripts, configuration YAMLs, prompts/answers JSONs, generated dashboards, databases, and binary assets are intentionally excluded — see the dataset and model cards for the artifacts and prompts those scripts consume.
Layout
dataprocessing/
common/ # Vertex AI / OpenAI client helpers, video-id utilities
refine/ # Prompt-set construction: enhance, dedup, hard-subset,
# humaneval-set assembly, removal sync
analysis/ # Ablation: prompt-enhancement effect on judges
evals/
eval_types.py # Typed result containers for VLM-as-judge runs
physics_criteria.py # Physical-law sub-rubric definitions
sub_questions.py # Sub-question rendering for CoT/SubQ prompts
prompts/ # Prompt-template loaders (YAMLs withheld; see model card)
human_eval/ # Flask-based human-annotation app + tests + templates
judge_training/
data/ # Build SFT data for ms-swift from raw judgement logs
# (schema, sampling, naming, Claude-CoT/DB builders)
Companion artifacts
- Dataset (250 prompts × 8 video models = 2 000 videos, sub-rubric
ground truth):
../datasets/— see itsREADME.md. - Model (LoRA judge adapter, prompt template, inference script):
../model/— see itsREADME.mdandinfer.py.
Dependencies (top-level)
The pipeline relies on the following open-source components. Versions match those reported in the paper.
| Component | Used for |
|---|---|
transformers, peft, qwen-vl-utils[decord] |
Judge inference |
ms-swift, deepspeed |
Judge LoRA training (ZeRO-2) |
vllm (OpenAI-compatible server) |
Hosting the base VLM for evaluation |
google-genai / Vertex AI |
Gemini family runs |
anthropic / Vertex AI |
Claude family runs |
openai Python SDK |
OpenAI / GPT family runs |
flask, sqlite3, selenium |
Human-annotation web app |
License
Code is released under the same anonymous-review terms as the rest of this release. No identifying metadata is included.
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