README.md

---
license: apache-2.0
task_categories:
- video-text-to-text
tags:
- video-understanding
- temporal-consistency
- benchmark
---

# TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models

[Paper](https://huggingface.co/papers/2605.09904) | [GitHub Repository](https://github.com/cjzcjz666/toc_bench)

TOC-Bench is a diagnostic benchmark designed to evaluate **temporal object consistency** in Video Large Language Models (Video-LLMs). While models often excel at general video understanding, TOC-Bench specifically tests their ability to maintain the identity, state, and continuity of objects across challenges such as occlusion, disappearance, reappearance, and state transitions.

## Benchmark Details

- **Size**: 2,323 high-quality, human-verified QA pairs over 1,951 videos.
- **Grounded reasoning**: Each item is object-track grounded, linking subjects to per-frame trajectories and structured temporal event timelines.
- **Dimensions**: Covers 10 diagnostic dimensions, including event counting, event ordering, identity-sensitive reasoning, and hallucination-aware verification.
- **Filtering**: Employs a three-layer temporal-necessity filtering protocol to ensure that questions require temporal visual evidence rather than language priors or single-frame shortcuts.

## Evaluation

The official repository provides a standardized evaluation harness to score model predictions.

### Running Inference
To run a model on the benchmark using `eval_runner.py`:

```bash
python eval_runner.py \
  --bench <REPAIRED_BENCHMARK_JSON> \
  --videos-root <VIDEO_ROOT> \
  --video-registry <VIDEO_REGISTRY_JSON> \
  --model <MODEL_KEY> \
  --frames 16 \
  --concurrency 4 \
  --out predictions.jsonl
```

### Computing Metrics
After generating predictions, you can compute accuracy and diagnostic metrics using:

```bash
python compute_metrics.py \
  --bench <REPAIRED_BENCHMARK_JSON> \
  --preds predictions.jsonl \
  --save-summary summary.json
```

## Citation

If you find this benchmark useful in your research, please cite the following paper:

```bibtex
@article{tocbench2024,
  title={TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models},
  author={...},
  journal={arXiv preprint arXiv:2605.09904},
  year={2024}
}
```