Data + runnable evaluation code for the BEAR benchmark.
Each task folder holds its data (*.json + images/videos) and a run.sh.
The shared runners and util/ live at the repository root. You run in two steps:
Inference — a VLM answers every question → produces a final_*.json.
Scoring — eval.py uses a GPT judge (for multiple-choice) or geometry
(for pointing/bbox) to grade those replies and print the final accuracy.
