We study post-training for dynamically routed mixture-of-experts (MoE) models, focusing on integrating independently optimized specialists while keeping consolidation efficient. Our approach combines calibrated multi-teacher on-policy distillation (C-MOPD), which matches each selected teacher on a local Top-$K$ support with teacher-wise loss normalization and clipping, with an execution path that uses post-capacity bounded-width dispatch, final-route replay, and asynchronous rollout--training overlap. On Turing-20B-A2B, the selected model achieves a four-domain normalized integration score of 0.9988 and retains at least 90.16\% of the measured specialist gain in every domain. At matched $K=32$, the full calibration objective scores 0.9962, compared with 0.9724 without calibration and 0.9834 without clipping. At a matched 8K response cap, the model exceeds both shared SFT and Qwen3-8B on all four reasoning-enabled mathematics and code benchmarks (unweighted mean: 85.92, versus 80.33 for Qwen3-8B and 74.21 for SFT), and improves over SFT on ten of twelve broader evaluation metrics. In a PPU-based profile, the combined execution configuration reduces consolidation step time from 398.7 to 139.3 seconds, an observed 2.86$\times$ speedup over serial execution. These results demonstrate strong specialist integration alongside more efficient consolidation, while reflecting the complete training and execution framework rather than any single component.
Junfeng Hao, Wenxian Wang, Tao Leng, Xingshu Chen, Hao Ren, Haizhou Wang
Reliable detection of large language model (LLM)-generated text must remain effective under meaning-preserving text revisions, such as rewriting, polishing, and expansion. In practice, LLM outputs are often rewritten, polished, expanded, translated, or lightly edited before publication, weakening the lexical, syntactic, and local discourse cues used by many existing detectors. We introduce \textbf{M}atched \textbf{C}onditional-\textbf{R}esponse \textbf{P}robing (\textbf{MCRP}), a frozen-LLM probing framework that measures how a candidate text responds when a semantically matched condition is introduced, removed, or replaced. For each candidate, the frozen probe LLM first generates a short semantic condition and then compares token-level losses and prompt--target attention measurements under matched, null, and control conditions. It aggregates bidirectional conditioned alignment, counterfactual sensitivity, and internal attention coupling into a probe representation, on which it trains a lightweight supervised random-forest classifier while keeping the probe LLM frozen. Across standard detection, robustness to meaning-preserving text revisions, multilingual, and multidomain evaluations, MCRP achieves consistently strong performance. Under rewriting, polishing, and expansion, it reaches 93.97\% F1 and 96.24\% ROC-AUC, outperforming the strongest evaluated baseline by 19.50 and 25.77 percentage points, respectively. Component ablations show that the response feature groups make complementary contributions to predictive performance. In the condition-control study, matched conditions consistently outperform random, mismatched, and corrupted alternatives, demonstrating a measurable incremental benefit from semantic matching.
Zezhou Wang, Ziyun Zhang, Xiaoyi Zhang Microsoft, Zhuzhong Qian, Yan Lu Microsoft Research Asia
Vision-language models are increasingly deployed as computer-use agents (CUAs) that operate desktops and browsers. Top-performing CUAs are framework-based systems that decompose planning and execution, while end-to-end screenshot-to-action policies are easier to deploy but lag behind on benchmarks such as OSWorld-Verified. GUI datasets like OSWorld pose two bottlenecks: they expose only a few hundred interactive, verifiable tasks and environments, and expert trajectories must be gathered by interacting with these environments, making such data hard to scale. We therefore ask how reinforcement learning from verifiable rewards (RLVR) can best exploit a small pool of exist expert trajectories to train end-to-end policies. Na"ively mixing these off-policy traces into on-policy RLVR is brittle: even after format conversion, expert trajectories exhibit structural mismatch and distribution shift from the learner. We propose BEPA (Bi-Level Expert-to-Policy Assimilation), which turns static expert traces into policy-aligned guidance via self-rolled reachable trajectories under the base policy (LEVEL-1) and a per-task, dynamically updated cache used in RLVR (LEVEL-2). On OSWorld-Verified, BEPA improves UITARS1.5-7B success from 22.87% to 32.13% and raises a held-out split from 5.74% to 10.30%, with consistent gains on MMBench-GUI and Online-Mind2Web. Our code and data are available at an anonymous repository: https://anonymous.4open.science/r/ACL_BEPA.
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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
Ziyun Zhang, Zezhou Wang, Xiaoyi Zhang Microsoft, Zongyu Guo Microsoft Research, Jiahao Li Microsoft Research Asia, Bin Li Microsoft, Yan Lu Microsoft Research Asia
GUI agents that interact with graphical interfaces on behalf of users are a promising direction for practical AI assistants, yet training them is hindered by scarce suitable environments. We present InfiniteWeb, a system that automatically generates functional web environments at scale for GUI agent training. While LLMs perform well on generating a single webpage, building a realistic and functional website with many interconnected pages faces challenges. We address these challenges through unified specification, task-centric test-driven development, and combining website seed variation with reference design images. Our system also generates verifiable task evaluators enabling dense reward signals for reinforcement learning. Experiments show that our system surpasses commercial coding agents at realistic website construction, and GUI agents trained on our generated environments achieve significant performance improvements on OSWorld and Online-Mind2Web, demonstrating the effectiveness of the proposed system.
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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
Junjie Liao, Huacong Tang University of California, Los Angeles, Zhou Ziheng, Yizhou Wang Peking University, Fangwei Zhong
We investigate how role models shape collective morality. To explore this, we build a multi-agent simulation powered by a Large Language Models (LLMs), where agents with diverse intrinsic drives, ranging from cooperative to competitive, interact and adapt through a four-stage cognitive loop (plan-act-observe-reflect). We design four experimental games (Alignment, Collapse, Conflict, and Construction) and conduct motivational ablation studies to identify the key drivers of imitation. The results indicate that identity-driven conformity can substantially reshape the initial dispositions. Agents tend to adapt their values to align with a perceived successful exemplar, leading to rapid value convergence.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Qihua Dong, Yitian Zhang, Huimin Zeng Northeastern University, Yizhou Wang Adobe Systems, Jianglin Lu, Kuo Yang Northeastern University, Yun Fu
Multimodal large language models (MLLMs) are making rapid strides in complex visual reasoning. This survey synthesizes the emerging paradigm of Image-Grounded Chain-of-Thought (IG-CoT), where models ground intermediate inferences by interleaving textual rationales with visual state updates. We formalize IG-CoT, present a method-centric taxonomy covering prompting, supervised fine-tuning, and reinforcement learning, and map these techniques to representative benchmarks. Our analysis identifies two domains where IG-CoT offers significant advantages: detail-oriented reasoning requiring meticulous perception, and imagined-world reasoning for simulating unseen states in games, geometry, and planning. We discuss the practical trade-offs of current methods regarding controllability, data, and compute. We conclude by highlighting key challenges (efficiency, data quality, and generative capabilities) and outlining promising future directions, including lightweight architectures, richer intermediate supervision, and method-aware evaluations that better assess faithfulness and long-horizon reasoning. We maintain a continuously updated paper list at https://github.com/dddraxxx/Awesome-Image-Grounded-CoT.
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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
Yizhou Wang Adobe Systems, Mang Tik Chiu, Lingzhi Zhang Adobe Systems, Xuan Shen, Sohrab Amirghodsi Adobe Systems, Yun Fu
Visual segmentation, the task of segmenting an image into semantically meaningful regions, is a cornerstone in machine learning and has widespread applications in industry. Nevertheless, visual segmentation with instruction has been a challenging task for many years. This largely stems from the cross-modal discrepancy between language and image domains, resulting in difficulty in relating the instruction semantics and the pixel-level predictions. In recent years, the remarkable reasoning capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) have spurred a new wave of research aiming to bridge the disparity between natural language instructions and pixel-level understanding. This survey offers the first comprehensive overview of the rapidly evolving field of LLM-driven visual segmentation. We categorize existing approaches based on their core objectives and methodologies, including reasoning-based segmentation, open-vocabulary segmentation, grounding techniques connecting language to pixels, and extensions to video domains. We review recent seminal works in LLM-based visual segmentation, analyzing their architectural innovations, training strategies, and benchmark performance. Furthermore, we discuss the common datasets, evaluation metrics, and identify key challenges and promising future directions at the intersection of language and visual segmentation. We hope this survey serves as a valuable resource for researchers and practitioners seeking to understand the current landscape and future directions of leveraging LLMs for sophisticated visual segmentation tasks and applications. The resource summary is available at https://github.com/wyzjack/Awesome-LLM-Visual-Segmentation.
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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
Yuanfei Wang Peking University, Xinju Huang, Fangwei Zhong, Yaodong Yang Peking University, Yizhou Wang Peking University, Yuanpei Chen Peking University, Hao Dong Peking University
Effective real-world human–agent interactions, such as household robotic services, are often long-term and repeated. Beyond executing tasks, agents are expected to quickly become familiar with individual users. In everyday use, people do not want to repeatedly specify precise instructions. Instead, they prefer agents that adapt to their habits and preferences over interaction while minimizing communication effort. This poses a key challenge: enabling agents to rapidly align with user needs and provide proactive assistance within limited communication. To study this problem in a realistic embodied setting, we first introduce HA-Desire, a home assistance simulation environment. HA-Desire features an LLM-driven proxy user with value-driven preferences and natural language behavior, enabling systematic evaluation of how agents adapt to users across interactions and satisfy their desires. We further propose FAMER, a framework that integrates goal-relevant memory, desire-centered mental reasoning, and efficient communication to infer user preferences from interaction while reducing unnecessary dialogue. Experiments across embodied household tasks and different LLMs show that FAMER improves both task success and interaction efficiency compared to existing baselines, highlighting the importance of communication-efficient desire alignment for proactive embodied agents that support users without requiring frequent instructions.
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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