Yilun Zhao Yale University, Jinbiao Wei, Tingyu Song University of the Chinese Academy of Sciences, Siyue Zhang, Chen Zhao NYU Shanghai, Arman Cohan Yale University and Allen Institute for Artificial Intelligence
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, exemplified by Bright, provide narrow gold sets and evaluate retrievers in isolation, which obscures their value inside realistic agent workflows. We introduce Bright-Pro, an evaluation framework that assesses the effectiveness of retrievers in agentic search systems. Bright-Pro covers a broad range of queries across diverse professional domains. For each query, we provide expert-annotated reasoning aspects, positive documents, a reference response, and evaluation rubrics, enabling fine-grained assessment of retriever performance. Beyond static evaluation, we further assess retrievers in the context of agentic search systems, measuring their practical utility when serving as core components within agentic workflows. Using Bright-Pro, we evaluate classical lexical, general-purpose, and reasoning-intensive retrievers, providing actionable insights for future retriever development.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Tianyu Yang, Sihong Wu, Yilun Zhao Yale University, Zhenwen Liang Tencent AI Lab, Lisen Dai Columbia University, Chen Zhao NYU Shanghai, Minhao Cheng Pennsylvania State University, Arman Cohan Yale University and Allen Institute for Artificial Intelligence 等 9 位作者
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 diagrams, failing to align mathematical symbols with visual evidence, or producing inconsistent reasoning steps. Moreover, existing evaluations mainly focus on checking final answers rather than verifying the correctness or executability of each intermediate step. A growing body of recent research addresses these issues by integrating structured perception, explicit alignment, and verifiable reasoning within unified frameworks.To establish a clear roadmap for understanding and comparing different MMR approaches, we systematically review them around four fundamental questions: (1) What to extract from multimodal inputs, (2) How to represent and align textual and visual information, (3) How to perform the reasoning, and (4) How to evaluate the correctness of the overall reasoning process. Finally, we discuss open challenges and share our thoughts on future research directions.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Bingsen Chen New York University and New York University Shanghai, Boyan Li, Ping Nie, Yuyu Zhang, Xi Ye University of Alberta, Chen Zhao NYU Shanghai
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 remains unexplored. We introduce Mr Dre, an evaluation suite that establishes multi-turn report revision as a new axis. Mr Dre consists of (1) a unified long-form report evaluation protocol spanning comprehensiveness, factuality, and presentation, and (2) a human-verified feedback simulation pipeline for systematic multi-turn revision evaluation. Our analysis of five diverse DRAs reveals a critical limitation: while agents can address most user feedback, they also regress on 16–27% of previously covered content and citation quality. Over multiple revision turns, even the best-performing agents leave significant headroom, as they continue to disrupt content outside the feedback’s scope and fail to preserve earlier edits. We further show that these issues are not easily resolvable through inference-time fixes such as prompt engineering and a dedicated sub-agent for revision.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表