[2603.00873] MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains

Computer Science > Artificial Intelligence arXiv:2603.00873 (cs) [Submitted on 1 Mar 2026] Title:MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains Authors:Xuying Ning, Dongqi Fu, Tianxin Wei, Mengting Ai, Jiaru Zou, Ting-Wei Li, Hanghang Tong, Yada Zhu, Hendrik Hamann, Jingrui He View a PDF of the paper titled MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains, by Xuying Ning and 9 other authors View PDF HTML (experimental) Abstract:With the increasing demand for step-wise, cross-modal, and knowledge-grounded reasoning, multimodal large language models (MLLMs) are evolving beyond the traditional fixed retrieve-then-generate paradigm toward more sophisticated agentic multimodal retrieval-augmented generation (MM-RAG). Existing benchmarks, however, mainly focus on simplified QA with short retrieval chains, leaving adaptive planning and multimodal reasoning underexplored. We present MC-Search, the first benchmark for agentic MM-RAG with long, step-wise annotated reasoning chains spanning five representative reasoning structures. Each example specifies sub-questions, retrieval modalities, supporting facts, and intermediate answers, with fidelity ensured by HAVE (Hop-wise Attribution and Verification of Evidence), resulting in 3,333 high-quality examples averaging 3.7 hops. Beyond answer accuracy, MC-Search introduces new process-level metrics for reasoning quality, stepwise retri...