MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

Maven, a Multi-stage Agentic Video Event aNnotation pipeline, transforms raw videos into high-quality, structured training data for Vision Language Models (VLMs) focused on video event reasoning. This pipeline generates multi-task Chain-of-Thought (CoT) reasoning traces centered on an "Event of Focus" by synthesizing a Multi-Scale Spatio-Temporal Event Description (MSTED) from three complementary caption levels. Crucially, Maven features agent-driven domain adaptation, allowing automatic prompt redesign for new video datasets and question types, and a hierarchical refinement loop for error diagnosis and targeted pipeline modifications. Applied to over 5,300 traffic videos, fine-tuning Cosmos-Reason2-8B on Maven-generated data achieved a +38.8-point gain in MCQ accuracy over zero-shot on a private CCTV evaluation set, surpassing Gemini 2.5 Pro and 3.1 Flash. On AccidentBench, CCTV-only training improved Cosmos-Reason2 by +10.7 MCQ points, matching Gemini 2.5 Pro, with RL post-training exceeding both Gemini baselines.

Key takeaway

For Machine Learning Engineers developing Vision Language Models for video event reasoning, Maven provides a critical solution for scalable, high-quality data generation. You should consider adopting this agentic pipeline to automate structured Chain-of-Thought annotation, especially when adapting to new domains like traffic or surveillance. This approach reduces manual prompt engineering, improves model generalizability, and can lead to performance surpassing models like Gemini 3.1 Flash on complex reasoning tasks.

Key insights

Maven's agentic, multi-stage pipeline generates structured video event annotations and CoT Q&A, enabling VLM domain adaptation and high performance.

Principles

• Explicit intermediate representations prevent information loss.
• Agent-driven adaptation automates pipeline configuration.
• Hierarchical error refinement improves data quality iteratively.

Method

Maven uses three stages: three-level video captioning, MSTED synthesis from captions, and multi-task CoT Q&A generation from MSTED. An agent redesigns prompts via backward inference, and refinement traces errors to apply targeted edits.

In practice

• Fine-tune Cosmos-Reason2-8B with Maven-labeled data for video reasoning.
• Implement MSTED as a verification checkpoint before Q&A generation.

Topics

• Agentic Pipelines
• Video Reasoning
• Vision Language Models
• Data Annotation
• Domain Adaptation
• Chain-of-Thought

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.