Multimodal retrieval over text corpora remains a fundamental challenge: the best vision-language encoder achieves only 27.6 nDCG@10 on MM-BRIGHT, a reasoning-intensive multimodal retrieval benchmark, underperforming strong text-only systems. We argue that effective multimodal retrieval requires three tightly integrated capabilities that existing approaches address only in isolation: expanding the query's latent intent, retrieving with a model trained for complex reasoning, and reranking via explicit step-by-step reasoning over candidates. We introduce \textbf{MARVEL} (\textbf{M}ultimodal \textbf{A}daptive \textbf{R}easoning-intensi\textbf{V}e \textbf{E}xpand-rerank and retrieva\textbf{L}), a unified pipeline that combines LLM-driven query expansion, \textbf{MARVEL-Retriever} -- a reasoning-enhanced dense retriever fine-tuned for complex multimodal queries -- and GPT-4o-based chain-of-thought reranking with optional multi-pass reciprocal rank fusion. Evaluated on MM-BRIGHT across 29 technical domains, MARVEL achieves \textbf{37.9} nDCG@10, surpassing the best multimodal encoder by \textbf{+10.3 points} and outperforming all single-stage baselines in 27 of 29 domains and matching or approaching the best baseline in the remaining two highly-specialized domains (Crypto, Quantum Computing), demonstrating that reasoning-intensive multimodal retrieval is best addressed through a unified expand-retrieve-rerank framework. https://github.com/mm-bright/multimodal-reasoning-retrieval

翻译：多模态文本检索仍然是一个根本性挑战：最佳视觉语言编码器在MM-BRIGHT（一个推理密集型多模态检索基准）上仅达到27.6 nDCG@10，性能不及纯文本系统。我们认为，有效的多模态检索需要三个紧密集成的能力，而现有方法仅孤立地处理其中部分：扩展查询的潜在意图、使用针对复杂推理训练的模型进行检索、以及通过对候选结果进行显式逐步推理来重排。我们提出\textbf{MARVEL}（\textbf{M}ultimodal \textbf{A}daptive \textbf{R}easoning-intensi\textbf{V}e \textbf{E}xpand-rerank and retrieva\textbf{L}），这是一个统一流水线，结合了LLM驱动的查询扩展、\textbf{MARVEL-Retriever}（一种针对复杂多模态查询微调的推理增强型稠密检索器），以及基于GPT-4o的思维链重排（可选用多轮互惠排名融合）。在涵盖29个技术领域的MM-BRIGHT上评估，MARVEL实现了\textbf{37.9} nDCG@10，超越最佳多模态编码器\textbf{+10.3个点}，在27个领域中优于所有单阶段基线，并在剩余两个高度专业化领域（密码学、量子计算）中匹配或接近最佳基线，证明推理密集型多模态检索最好通过统一的“扩展-检索-重排”框架来解决。 https://github.com/mm-bright/multimodal-reasoning-retrieval