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license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
base_model: Qwen/Qwen3-8B
language:
- en
tags:
- video-understanding
- long-video-understanding
- agentic-llm
- video-question-answering
- vision-language-model
- grpo
- reinforcement-learning
- icml-2026
---
<h2 align="center">🎬 VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding by Decoupling Answer Authority</h2>
<p align="center">
<a href="https://github.com/Echochef/VideoSEAL"><img alt="Code" src="https://img.shields.io/badge/Code-GitHub-black?logo=github"></a>
<a href="https://huggingface.co/CewEhao/VideoSEAL_8B"><img alt="HF Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-VideoSEAL__8B-yellow"></a>
<img alt="ICML 2026" src="https://img.shields.io/badge/ICML-2026-blue">
</p>
<p align="center">
🤗 HuggingFace model:
<a href="https://huggingface.co/CewEhao/VideoSEAL_8B">CewEhao/VideoSEAL_8B</a>
·
💻 Code:
<a href="https://github.com/Echochef/VideoSEAL">Echochef/VideoSEAL</a>
</p>
## 👉 Introduction
This is the official model card for **VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding by Decoupling Answer Authority** (ICML 2026).
VideoSEAL provides offline build utilities for long video indexing:
- OCR subtitles (SRT) → OCR captions + (optional) embeddings
- Clip captions (VLM) → clip captions + (optional) embeddings
- Merge into a unified semantic index under `indexes/semantic/<video_id>/`
- (Optional) generate a global `full_story.txt` summary
## 📦 Layout
- 🧰 Shell entrypoints: `scripts/`
- 🐍 Python package: `videoseal/`
- ✅ Tests: `test/`
- 🧩 OCR toolchain (vendored): `third_party/video-subtitle-extractor/`
## ⚙️ Configuration
- Defaults live in the scripts under `scripts/`.
- Put real API keys/endpoints in your shell environment / job launcher.
## 🏗️ Run offline build
```bash
cd /path/to/VideoSEAL
export MLLM_API_KEY="sk_your_api_key"
export EMBEDDING_API_KEY="sk_your_api_key"
export AGENT_LLM_API_KEY="sk_your_api_key"
export VISUAL_INSPECT_API_KEY="sk_your_api_key"
VIDEO=/path/to/video.mp4 BENCHMARK=LVBench ./scripts/run_offline_build.sh
```
## ✅ Run tests
```bash
/root/miniconda3/envs/rllm/bin/python -m unittest discover -s test -v
```
## 🏋️ GRPO training (video tool workflow)
This repo vendors a minimal copy of the `rllm/` + `verl/` Python packages (under the repo root)
to make the video tool-agent GRPO workflow runnable without an extra repo checkout.
### 🧪 Training environment (conda)
```bash
conda create -n videoseal python=3.12 -y
conda activate videoseal
pip install vllm==0.11.0
cd rllm
pip install -e .
cd ../verl
pip install -e .
```
### 🚀 Launcher
- `scripts/train/run_video_workflow_grpo.sh`
### 🧩 Example
```bash
cd /path/to/VideoSEAL
# Export real API keys/endpoints in your environment before launching.
TRAIN_PARQUET='["/path/to/train.parquet"]' \
VAL_PARQUET='/path/to/val.parquet' \
MODEL_PATH='Qwen/Qwen3-8B' \
./scripts/train/run_video_workflow_grpo.sh train
```
### 🔎 Quick checks
```bash
./scripts/train/run_video_workflow_grpo.sh test-reward
pytest -q tests/rewards/test_video_reward_tool_env_integration.py
```
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