README.md

metadata

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

🎬 VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding by Decoupling Answer Authority

🤗 HuggingFace model: CewEhao/VideoSEAL_8B  ·  💻 Code: Echochef/VideoSEAL

👉 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

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

/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)

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

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

./scripts/train/run_video_workflow_grpo.sh test-reward
pytest -q tests/rewards/test_video_reward_tool_env_integration.py