Jianzhe Ma Renmin University of China, Wenxuan Wang Renmin University of China, Qin Jin Renmin University of China
Geometry problem solving, a crucial aspect of mathematical reasoning, is vital across various domains, including education, the assessment of AI’s mathematical abilities, and multimodal capability evaluation. The recent surge in deep learning technologies, particularly the emergence of multimodal large language models, has significantly accelerated research in this area. This paper presents a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of state-of-the-art performance, existing challenges, and promising future directions. Our objective is to offer a comprehensive and practical reference of deep learning for geometry problem solving, thereby fostering further advancements in this field. We maintain a list of relevant papers: https://github.com/majianz/dl4gps.
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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
Wenting Chen, Guolin Huang, Wenxuan Wang Renmin University of China, Zhongrui Zhu
Despite achieving high accuracy on medical benchmarks, LLMs exhibit the Einstellung Effect in clinical diagnosis—relying on statistical shortcuts rather than patient-specific evidence, causing misdiagnosis in atypical cases. Existing benchmarks fail to detect this critical failure mode. We introduce MedEinst, a counterfactual benchmark with 5,383 paired clinical cases across 49 diseases. Each pair contains a control case and a “trap” case with altered discriminative evidence that flips the diagnosis. We measure susceptibility via Bias Trap Rate—probability of misdiagnosing traps despite correctly diagnosing controls. Evaluation shows frontier models achieve high baseline accuracy but severe bias trap rates. Thus, we propose ECR-Agent, aligning LLM reasoning with Evidence-Based Medicine via two components: (1) Dynamic Causal Inference (DCI) performs structured reasoning through dual-pathway perception, dynamic causal graph reasoning across three levels (association, intervention, counterfactual), and evidence audit for final diagnosis; (2) Critic-Driven Graph Memory Evolution (CGME) iteratively refines the system by storing validated reasoning paths in an exemplar base and consolidating disease-specific knowledge into evolving illness graphs. Source code is to be released.
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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
Wenxuan Wang Renmin University of China, Zizhan Ma The Chinese University of Hong Kong, Guo Yu, Yiu-Fai Cheung Department of Computer Science and Engineering, The Chinese University of Hong Kong, Meidan Ding Shenzhen University, Jie Liu City University of Hong Kong, Wenting Chen, Linlin Shen Shenzhen University
Large language models (LLMs) show significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. However, concerns persist regarding the reliability of these benchmarks, which often lack clinical fidelity, robust data management, and safety-oriented evaluation metrics. To address these shortcomings, we introduce MedCheck, the first lifecycle-oriented assessment framework specifically designed for medical benchmarks. Our framework deconstructs benchmark development into five stages from design to governance, and provides a comprehensive checklist of 46 medically-tailored criteria. Using MedCheck, we conducted an in-depth empirical evaluation of 56 medical LLM benchmarks. Our analysis uncovers widespread, systemic issues, including a profound disconnect from clinical practice, a crisis of data integrity due to unmitigated contamination risks, and a systematic neglect of safety-critical evaluation dimensions like model robustness and uncertainty awareness. Based on these findings, MedCheck is both a diagnostic tool for existing benchmarks and an actionable guideline for a more standardized, reliable, and transparent approach to evaluating AI in healthcare.
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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
Yichen Xu, Liangyu Chen Renmin University of China, Liang Zhang, Zihao Yue Renmin University of China, Jianzhe Ma Renmin University of China, Wenxuan Wang Renmin University of China, Qin Jin Renmin University of China
Charts are a universally adopted medium for data communication, yet existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. To address this limitation, we introduce PolyChartQA, the first large-scale multilingual benchmark for chart question answering, comprising 22,606 charts and 26,151 QA pairs across 10 diverse languages. PolyChartQA is constructed through a scalable pipeline that enables efficient multilingual chart generation via data translation and code reuse, supported by LLM-based translation and rigorous quality control. We systematically evaluate multilingual chart understanding with PolyChartQA on state-of-the-art LVLMs and reveal a significant performance gap between English and other languages, particularly low-resource ones. Additionally, we introduce a companion multilingual chart question answering training set, PolyChartQA-Train, on which fine-tuning LVLMs yields substantial gains in multilingual chart understanding across diverse model sizes and architectures. Together, our benchmark provides a foundation for developing globally inclusive vision-language models capable of understanding charts across diverse linguistic contexts. Codes and datasets are available on https://github.com/Road2Redemption/PolyChartQA.
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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
Ada Chen CMU, Carnegie Mellon University, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao The Chinese University of Hong Kong, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang Renmin University of China 等 9 位作者
Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing the emergence of Computer-Using Agents (CUAs), capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps. However, as these agents grow in capability, they also introduce novel safety and security risks. Vulnerabilities in LLM-driven reasoning, with the added complexity of integrating multiple software components and multimodal inputs, further complicate the security landscape. In this paper, we present a systematization of knowledge on the safety and security threats of CUAs. We conduct a comprehensive literature review and distill our findings along four research objectives: (i) define the CUA that suits safety analysis; (ii) categorize current safety threats among CUAs; (iii) propose a comprehensive taxonomy of existing defensive strategies; (iv) summarize prevailing benchmarks, datasets, and evaluation metrics used to assess the safety and performance of CUAs. Building on these insights, our work provides future researchers with a structured foundation for exploring unexplored vulnerabilities and offers practitioners actionable guidance in designing and deploying secure Computer-Using Agents.
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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
Ziheng Wang Renmin University of China, Zihao Yue Renmin University of China, Wenxuan Wang Renmin University of China, Qin Jin Renmin University of China
This paper explores attention attractors—tokens that draw significantly high attention—in large language models. We analyze them from three perspectives: (1) Functionality: We demonstrate their role in aggregating information from preceding contexts to facilitate future predictions. (2) Distribution: Through layer-wise and token-wise analysis, we reveal that attention attractors are widely distributed across layers but predominantly originate from low-semantic words like “_the”. (3) Mechanism: We demonstrate the correlation between attention weights allocated to tokens with their specific activation dimension values. We hope these findings provide new insights into the attention mechanisms of large language models and inspire further exploration. Code will be released at https://github.com/luyouqi233/AttentionAttractor.
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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
Youliang Yuan The Chinese University of Hong Kong-Shenzhen, Qiuyang Mang, Jingbang Chen The Chinese University of Hong Kong, Hong Wan, Xiaoyuan Liu Xiaohongshu and The Chinese University of Hong Kong, Shenzhen, Junjielong Xu Tencent and The Chinese University of Hong Kong, Shenzhen, Jen-tse Huang, Wenxuan Wang Renmin University of China 等 10 位作者
In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model’s reasoning ability. This is evidenced by a high incidence of “false positives”—solutions that reach the correct answer through an unsound process.Through a systematic analysis with human verification, we establish a taxonomy of these failure modes, identifying patterns like Miracle Steps—abrupt jumps to a correct output without a valid preceding derivation. Probing experiments suggest that these Miracle Steps are linked to answer-recall shortcuts, including memorization from pretraining, where the model accesses the correct answer independently of its reasoning chain.To mitigate this systemic issue, we introduce the Rubric Reward Model (RRM), a process-oriented reward function that evaluates the entire reasoning trajectory against problem-specific rubrics.The RRM explicitly penalizes logical flaws and encourages rigorous deduction.When integrated into an RL pipeline, RRM-based training consistently outperforms outcome-only supervision across four math benchmarks.Notably, it boosts Verified Pass@1024 on AIME2024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%.Our work demonstrates that rewarding the solution process is crucial for building accurate and reliable models.
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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