MRMR: A Realistic and Expert-Level Multidisciplinary Benchmark for Reasoning-Intensive Multimodal Retrieval
Siyue Zhang · Yuan Gao · Xiao Zhou · Yilun Zhao · Tingyu Song · Arman Cohan · Anh Tuan Luu · Chen Zhao
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Siyue Zhang · Yuan Gao · Xiao Zhou · Yilun Zhao · Tingyu Song · Arman Cohan · Anh Tuan Luu · Chen Zhao
Bingsen Chen · Boyan Li · Ping Nie · Yuyu Zhang · Xi Ye · Chen Zhao
Existing benchmarks for Deep Research Agents (DRAs) treat report generation as a single-shot writing task, which fundamentally diverges from how human researchers iteratively draft and revise reports via self-reflection or peer feedback. Whether DRAs can reliably revise reports with user feedback...
Yilun Zhao · Jinbiao Wei · Tingyu Song · Siyue Zhang · Chen Zhao · Arman Cohan
Reasoning-intensive retrieval aims to surface evidence that maximizes downstream reasoning utility rather than only topical similarity. This capability is increasingly vital for agentic retriever-in-the-loop systems such as Deep-Research. However, existing retriever evaluation benchmarks, exempli...
Tianyu Yang · Sihong Wu · Yilun Zhao · Zhenwen Liang · Lisen Dai · Chen Zhao · Minhao Cheng · Arman Cohan · Xiangliang Zhang
Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities. However, current models still face significant challenges in real-world visual math tasks, often misinterpreting diag...