Yang Xiang, Yixin Ji Soochow University, Ruotao Xu, Dan Qiao Suzhou University, Zheming Yang ByteDance Inc., Juntao Li, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology
Large reasoning models (LRMs) have achieved remarkable performance in complex reasoning tasks, driven by their powerful inference-time scaling capability.However, LRMs often suffer from overthinking, which results in substantial computational redundancy and significantly reduces efficiency.Early-exit methods aim to mitigate this issue by terminating reasoning once sufficient evidence has been generated, yet existing approaches mostly rely on handcrafted or empirical indicators that are unreliable and impractical.In this work, we introduce Dynamic Thought Sufficiency in Reasoning (DTSR), a novel framework for efficient reasoning that enables the model to dynamically assess the sufficiency of its chain-of-thought (CoT) and determine the optimal point for early exit.Inspired by human metacognition, DTSR operates in two stages: (1) Reflection Signal Monitoring, which identifies reflection signals as potential cues for early exit, and (2) Thought Sufficiency Check, which evaluates whether the current CoT is sufficient to derive the final answer.Experimental results on the Qwen3 models show that DTSR reduces reasoning length by 28.9%–34.9% with minimal performance loss, effectively mitigating overthinking.We further discuss overconfidence in LRMs and self-evaluation paradigms, providing valuable insights for early-exit reasoning.
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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
He Geng, Yangmin Huang IFLYTEK CO.LTD., Lixian Lai Xunfei Healthcare, Qianyun Du IFLYTEK CO.LTD., Hui Chu, Zhiyang He Xunfei Healthcare Technology Co., Ltd., Jiaxue Hu IFLYTEK CO.LTD., Xiaodong Tao Xunfei Healthcare Co. Ltd
Aligning Large Language Models (LLMs) with high-stakes medical standards remains a significant challenge, primarily due to the dissonance between coarse-grained preference signals and the complex, multi-dimensional nature of clinical protocols. To bridge this gap, we introduce \textit{ProMedical}, a unified alignment framework grounded in fine-grained clinical criteria. We first construct \textit{ProMedical-Preference-50k}, a dataset generated via a human-in-the-loop pipeline that augments medical instructions with rigorous, physician-derived rubrics. Leveraging this corpus, we propose the Explicit Criteria Injection paradigm to train a multi-dimensional reward model. Unlike traditional scalar reward models, our approach explicitly disentangles safety constraints from general proficiency, enabling precise guidance during reinforcement learning. To rigorously validate this framework, we establish \textit{ProMedical-Bench}, a held-out evaluation suite anchored by double-blind expert adjudication. Empirical evaluations demonstrate that optimizing the Qwen3-8B base model via \textit{ProMedical-RM}-guided GRPO yields substantial gains, improving overall accuracy by 22.3% and safety compliance by 21.7%, effectively rivaling proprietary frontier models. Furthermore, the aligned policy generalizes robustly to external benchmarks, demonstrating performance comparable to state-of-the-art models on UltraMedical. We publicly release our datasets, reward models, and benchmarks to facilitate reproducible research in safety-aware medical alignment.
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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
Xinyi Wang, Wei Dai Wechat, Kyle Qiao, Ke Wang, Peng Chen, Gang Cao, Kang Qin, Zhongpu Wang WeChat 等 13 位作者
Automated interaction with graphical user interfaces (GUIs) is central to General Artificial Intelligence yet remains challenging within Super App ecosystems, characterized by non-standard rendering and absent accessibility metadata. While GUI agents often rely on explicit accessibility trees or static imitation, they are less explored for dynamic environments marked by sparse feedback and implicit visual cues. We present GUI0, a framework synergizing autonomous data synthesis with dual-agent co-evolution. GUI0 establishes a domain-aware foundation model via synthesized corpora and employs curriculum-driven reinforcement learning, where a curriculum agent generates boundary tasks to optimize an actor agent.Empirical results demonstrate three key advantages: (1) State-of-the-art performance on the SuperAPP benchmark, outperforming Gemini-2.5-Pro and Claude-4-Sonnet; (2) universal efficacy across diverse base models, consistently yielding substantial improvements on both Qwen2.5-VL and GUI-Owl variants; and (3) robust zero-shot generalization to standard GUIs (e.g., +62.7% on ScreenSpot Pro).
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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
Yan Wang Yale University, Yitao Xu George Mason University, Nanhan Shen, Jinyan Su, Jimin Huang University of Manchester and The Fin AI, Zining Zhu Stevens Institute of Technology
Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumption by introducing COMMITTEEAUDIT, a post hoc framework that analyzes routing behavior at the level of expert groups rather than individual experts. Across three representative models and the MMLU benchmark, we uncover a domain invariant Standing Committee. This is a compact coalition of routed experts that consistently captures the majority of routing mass across domains, layers, and routing budgets, even when architectures already include shared experts. Qualitative analysis further shows that Standing Committees anchor reasoning structure and syntax, while peripheral experts handle domain-specific knowledge. These findings reveal a strong structural bias toward centralized computation, suggesting that specialization in Mixture of Experts models is far less pervasive than commonly believed. Crucially, this inherent bias indicates that current training objectives, such as load-balancing losses that enforce uniform expert utilization, may be working against the model’s natural optimization path, thereby limiting training efficiency and performance.
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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