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MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains
arXiv – CS AI|Xuying Ning, Dongqi Fu, Tianxin Wei, Mengting Ai, Jiaru Zou, Ting-Wei Li, Hanghang Tong, Yada Zhu, Hendrik Hamann, Jingrui He||7 views
🤖AI Summary
Researchers introduce MC-Search, the first benchmark for evaluating agentic multimodal retrieval-augmented generation (MM-RAG) systems with long, structured reasoning chains. The benchmark reveals systematic issues in current multimodal large language models and introduces Search-Align, a training framework that improves planning and retrieval accuracy.
Key Takeaways
- →MC-Search is the first benchmark specifically designed for agentic multimodal retrieval-augmented generation with complex reasoning chains.
- →The benchmark contains 3,333 high-quality examples averaging 3.7 reasoning hops across five representative reasoning structures.
- →Testing revealed systematic issues in leading MLLMs including over-retrieval, under-retrieval, and modality-misaligned planning.
- →Search-Align framework uses process-supervised fine-tuning to improve planning and retrieval fidelity in open-source models.
- →The research introduces new process-level metrics for evaluating reasoning quality beyond simple answer accuracy.
#multimodal-ai#benchmark#retrieval-augmented-generation#mllm#reasoning-chains#ai-research#machine-learning#evaluation-metrics
Read Original →via arXiv – CS AI
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