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

2026-05-21 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, medium

Summary

MAVEN (Multi-stage Agentic Video Event aNnotation) is a multi-stage agentic pipeline designed to generate high-quality, structured training data for Vision Language Models (VLMs) in video event reasoning. It converts raw videos into multi-task training data with Chain-of-Thought (CoT) reasoning traces, centered on an Event of Focus. MAVEN synthesizes a Multi-Scale Spatio-Temporal Event Description (MSTED) from three caption levels, feeding into Q&A generation. The system supports agent-driven domain adaptation, automatically redesigning prompts for new datasets. A hierarchical refinement loop identifies and corrects annotation errors by modifying prompts or pipeline structure. MAVEN labeled over 5,300 traffic videos, fine-tuning Cosmos-Reason2-8B. This achieved a +38.8-point gain in MCQ accuracy over zero-shot on a private CCTV set, surpassing Gemini 2.5 Pro and 3.1 Flash. On AccidentBench, it boosted Cosmos-Reason2 by +10.7 MCQ points, eventually outperforming both Gemini baselines with RL post-training.

Key takeaway

For AI Scientists developing Vision Language Models, MAVEN demonstrates that agentic, multi-stage annotation pipelines can significantly improve VLM performance on complex video reasoning tasks. You should consider integrating similar agent-driven domain adaptation and hierarchical error correction mechanisms into your data generation workflows to achieve superior model accuracy and adaptability across diverse video datasets. This approach can reduce manual labeling effort while boosting VLM capabilities.

Key insights

MAVEN's agentic, multi-stage pipeline automates high-quality, domain-adaptable video annotation for VLM training.

Principles

• Agent-driven adaptation redesigns prompts for new domains.
• Hierarchical refinement corrects errors by tracing root causes.

Method

Synthesize MSTED from captions, generate Q&A, then use agent-driven prompt redesign and hierarchical error classification for iterative quality improvement.

In practice

• Apply agentic pipelines for VLM training data generation.
• Use MSTED as an intermediate representation for video reasoning.

Topics

• Video Reasoning
• VLM Training
• Agentic AI
• Data Annotation
• Multi-stage Pipelines
• Domain Adaptation
• Traffic Surveillance

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.