Recycling a target model's own predictions offers a training-free source of proposals for speculative decoding. In vision-language models, however, existing recycling indexes predictions by the last token alone, conflating predictions computed under different images and prefixes; we find that such reuse fails most often at image-sensitive decoding states. Longer keys reduce this aliasing but match fewer states. We introduce \method, which stores target predictions under one-, two-, and three-token keys: contextual memories live only for the current request and are thus implicitly conditioned on its image, while token-level memory persists across requests to retain coverage. At each lookup, entropy-confidence weighting fuses the retrieved predictions according to their key specificity and concentration. Best-first search composes the fused transitions into a candidate tree, supplemented by suffix-matched continuations, and one target pass verifies the tree under greedy decoding. Without training or an auxiliary draft model, \method\ achieves mean speedups over autoregressive decoding of $1.66\speed$ and $1.56\speed$ across seven single-turn benchmarks on Qwen2.5-VL-7B and LLaVA-1.6-7B, exceeding the strongest training-free baseline, Token Recycling ($1.51\speed$ and $1.37\speed$). Our implementation is available in https://github.com/Senmo996/RECAP-official-code.
Modern text-to-speech (TTS) systems can synthesize natural and expressive speech, yet a practical speech foundation model must go beyond naturalness to offer stable quality, precise controllability, and a broad set of composable capabilities within a unified framework. We present TTSonic, a unified speech generation model that supports zero-shot TTS, voice cloning, prosody cloning, instruction following, voice conversion, and cloning from noisy reference speech. Rather than relying on separate task-specific models, TTSonic accommodates diverse conditioning signals, including text, natural language instructions, reference speech, and text-described timbre, within the same model framework. Specifically, we develop a semantic--acoustic disentangled speech tokenizer through ASR-supervised semantic learning and reconstruction-guided generative refinement. Serving as the representational foundation of our unified framework, the tokenizer enables the Text-to-Semantic autoregressive model to focus on semantic and prosodic planning, while the acoustic flow-matching model specializes in high-quality speech rendering under diverse acoustic and timbre conditions. Building on this framework, we incorporate CoT-guided instruction-following planning and Text2Timbre voice design to support controllable speech generation. Experiments across zero-shot synthesis, multilingual and cross-lingual TTS, instruction following, voice design, voice conversion, and noisy-reference cloning demonstrate the competitive performance and broad applicability of TTSonic.
Object rearrangement planning in cluttered environments is often non-monotone, requiring objects to be temporarily relocated before reaching their final destinations. Although search-based planners can solve such tasks, they incur substantial computational cost and require expensive replanning when the environment changes. We propose the Visual Adaptive Rearrangement Planner (VARP), which learns non-monotone rearrangement directly from visual state representations generated from expert demonstrations. VARP predicts pick-and-place actions from the current and goal visual state representations, and incorporates an Action Refinement Strategy (ARS) to improve action feasibility and a Progress Enforcement Strategy (PES) to mitigate progress stagnation. By continuously updating actions from the latest visual state representations, VARP adapts online to disturbances without replanning from scratch or performing online combinatorial search. In challenging rearrangement benchmarks, VARP achieves a 98.3\% success rate with only 0.4 s planning time per task, 97.5\% success under execution-time disturbances, and 94.5\% under continuous severe disturbances, while generalizing effectively to unseen object counts. These results demonstrate that VARP provides a scalable and robust solution for non-monotone object rearrangement.
Instruction tuning for multi-task network traffic analysis requires data with clearly defined sources, supervision targets, and evaluation conditions. We introduce TrafficInstruct, a newly collected dataset comprising raw packet captures and 55,000 structured supervised fine-tuning records across ten tasks: malware traffic detection, botnet detection, malicious DNS-over-HTTPS detection, Web attack detection, advanced persistent threat detection, encrypted VPN detection, Tor behavior detection, encrypted application/service classification, website fingerprinting, and application classification under concept drift. An automated workflow captures traffic from controlled software executions, attack experiments, and application activities. Labels are supported by task-specific collection evidence, including process or socket attribution, activity records, and execution outcomes. Filtering, sanitization, and task-specific transformations produce instructions, traffic observations, and structured answers. We verify sample sources and partition the dataset by complete collection sessions, keeping all samples from the same session within one partition. The benchmark defines allowed inputs, deterministic label mappings, fixed training/validation/test partitions, and task-wise scoring rules. We evaluate majority-class, random-forest, and ET-BERT baselines, an unadapted 27B language model, and independently trained task-specific LoRA adapters on a shared frozen base. Adapters are selected using validation data and evaluated on the corresponding test sets. Task-specific adaptation improves classification performance, supporting instruction tuning across diverse traffic-analysis tasks.
People increasingly use large language models (LLMs) to interpret others' behaviour, often assuming that LLMs infer motives as humans do. Aligned LLMs attribute more benevolent and less malevolent motives to a whistleblower than humans do, but ratings alone cannot reveal whether these differences arise in internal representations or at report. Here we compare linear-probe readouts of LLMs' internal states with their own ratings at the same prompt position for four motives, in two preregistered studies. For the two malevolent motives, self-interest and intent to harm, internal and expressed readouts did not differ: the lower ratings reflect weaker signal along the probed directions, not suppression at output. For the two benevolent motives, duty and concern for others, expressed ratings exceeded the internal readouts, indicating amplification at report. In free text the model rarely volunteered a motive against the colleague, far less often than humans did, yet produced one readily once the scenario supplied a reason. Shifting the internal representation along a malevolent-motive direction brought both malevolent ratings to the human level, whereas the rating of duty yielded only past a threshold at which the other motives had overshot. LLMs' more charitable motive profile thus has two distinct sources: reduced malevolent signal in the probed representation and amplified benevolent attribution at report.
Chenyan Wu, Yandong Li, Xianfeng Tang, Zhe Huang, James Wang
Reconstructing multi-human body meshes from a single monocular image is a challenging problem, requiring both individual body meshes and relative 3D positions to produce a coherent scene representation. We introduce MUG (Multi-hUman Graph network), which uses a single graph neural network to construct coherent multi-human meshes from only 2D poses. Our approach demonstrates that pose-based methods can jointly perform depth reasoning and multi-human mesh generation. Existing image-based methods often rely on lab-collected datasets with accurate 3D labels, introducing domain gaps when applied to in-the-wild or art images due to differences in appearance and context. In contrast, MUG leverages the geometric consistency of 2D poses across diverse datasets to mitigate such discrepancies. MUG operates in three phases. First, it constructs a novel heterogeneous graph from multi-human 2D poses, connecting nodes across people and within individuals to capture inter-human interactions and body geometry, including skeletal and mesh structures. Second, a dual-branch graph neural network predicts inter-human depth relations and root-joint-relative mesh coordinates. Finally, these outputs are combined to construct complete multi-human 3D meshes. Despite using only 2D poses and a streamlined architecture, MUG outperforms existing multi-human mesh estimation methods across datasets from diverse domains, including RH, MuPoTS-3D, and 3DPW. Qualitative results show that MUG can handle art images, through-wall scenarios, and poor lighting conditions when paired with advanced 2D pose networks. Both qualitative and quantitative evaluations highlight its generalization ability in open-world scenarios.
Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral cloning leaves them brittle; lacking corrective training signals, minor execution errors rapidly compound into unrecoverable, out-of-distribution failures. To address this limitation, we propose Adaptive Failure-Informed Learning (AFIL), an end-to-end framework that leverages failure trajectories as adaptive negative guidance for diffusion- and flow-based VLA policies. AFIL uses a pretrained VLA to generate failure rollouts online, avoiding the need for handcrafted failure-mode design or human-in-the-loop recovery. It then jointly trains Dual Action Generators (DAGs) for successful and failed behaviors while sharing a common vision-language backbone, enabling efficient failure-aware policy learning with limited parameter overhead. During sampling, the failure generator adaptively steers action generation away from failure-prone regions and toward more reliable success modes, with guidance strength determined by the per-diffusion-step distance between success and failure distributions. Experiments across in-domain and out-of-domain robotic manipulation tasks, covering both short- and long-horizon settings, show that AFIL consistently improves task success rates and robustness over existing VLA baselines, demonstrating its effectiveness, efficiency, and generality.
Shufang Xie Renmin University of China, Qizhi Pei, Ang Lv, Jingyang Hu, Lijun Wu Shanghai Artificial Intelligence Laboratory, Rui Yan Wuhan University
AI development is often framed as the outcome of isolated research and engineering efforts, yet evidence from deployed systems suggests that language models interact through a shared data ecosystem. While the optimization of individual models is extensively studied, the emergent properties of this interconnected population remain largely unexplored, limiting our ability to predict long-term ecosystem trajectories We term this process data pollination, the unintentional circulation of synthetic model outputs through shared online platforms and web-scale training corpora, and formalize it as a population-based evolutionary framework to investigate stability dynamics under synthetic data training. Our theoretical analysis and controlled experiments involving 320 language models demonstrate that population dynamics can mitigate the model collapse observed in single-lineage recursive training, yielding stable or improving performance across diverse benchmarks. Crucially, we find that ecological diversity functions as a fundamental resilience mechanism that safeguards the ecosystem against collapse, highlighting the critical importance of maintaining model diversity for sustainable AI development.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jia-Nan Li, Jian Guan, Songhao Wu, Wei Wu Ant Research, Rui Yan Wuhan University
Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in user values and needs. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs. We establish a systematic preference space characterizing psychological and behavioral dimensions, alongside diverse persona representations for robust preference inference in real-world scenarios. Building upon this foundation, we introduce AlignX, a large-scale dataset of over 1.3 million personalized preference examples, and develop two complementary alignment approaches: in-context alignment directly conditioning on persona representations and preference-bridged alignment modeling intermediate preference distributions. Extensive experiments demonstrate substantial improvements over existing methods, with an average 17.06% accuracy gain across four benchmarks while exhibiting a strong adaptation capability to novel preferences, robustness to limited user data, and precise preference controllability. These results validate our approach toward user-adaptive AI systems.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Ziwen Xu Zhejiang University, Chenyan WU Zhejiang University of Technology, Hengyu Sun, Haiwen Hong Alibaba Group, Mengru Wang, Yunzhi Yao Zhejiang University, Longtao Huang Alibaba Group, Hui Xue 等 12 位作者
Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model’s valid-generation manifold. Finally, we introduce a new steering approach guided by this analysis that improves preference while better preserving utility.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Song Jin, Juntian Zhang, Xun Zhang, Zeying Tian, Fei Jiang Meituan, Guojun Yin Meituan, Wei Lin, Yong Liu Renmin University of China 等 9 位作者
Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we introduce DiningBench, a hierarchical, multi-view benchmark designed to evaluate VLMs across three levels of cognitive complexity: Fine-Grained Classification, Nutrition Estimation, and Visual Question Answering. Unlike previous datasets, DiningBench comprises 3,021 distinct dishes with an average of 5.27 images per entry, incorporating fine-grained “hard” negatives from identical menus and rigorous, verification-based nutritional data. We conduct an extensive evaluation of 29 state-of-the-art open-source and proprietary models. Our experiments reveal that while current VLMs excel at general reasoning, they struggle significantly with fine-grained visual discrimination and precise nutritional reasoning. Furthermore, we systematically investigate the impact of multi-view inputs and Chain-of-Thought reasoning, identifying five primary failure modes. DiningBench serves as a challenging testbed to drive the next generation of food-centric VLM research. All codes are released in https://github.com/meituan/DiningBench.
尚无 AI 解读，展示原文摘要
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
Songhao Wu, Ang Lv, Ruobing Xie Tencent, Samm Sun, Di Wang, Rui Yan Wuhan University, Yankai Lin Renmin University of China
Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts. This design inherently separates the routing decision from each expert’s internal capabilities, leading to suboptimal performance. In this work, we address this limitation with Union-of-Experts (UoE), an MoE variant that performs "expert-autonomous routing”. The core mechanism of UoE is to pre-designate a minute fraction of neurons within each expert as "routing neurons”. Experts autonomously select relevant tokens by comparing the activation intensity of these neurons, aligning routing decisions with each expert’s functional profile. To prevent the waste of activations from unselected experts’ routing neurons, we aggregate all routing neuron outputs and sum them into the final layer output. This aggregation acts as a novel virtual shared expert whose parameters are distributed across the individual experts, and improves overall parameter efficiency. We pre-train UoE models with up to 3B parameters, demonstrating that they outperform traditional MoEs with matched efficiency. Furthermore, our analysis of the routing neurons provides valuable insights into expert-autonomous selection and the broader routing mechanisms of MoE models.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Kaiyi Zhang, Ang Lv, Jinpeng Li, Yongbo Wang, Feng Wang, Haoyuan hu, Rui Yan Wuhan University
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving the complex reasoning abilities of large language models (LLMs). However, current RLVR methods face two significant challenges: the near-miss reward problem, where a small mistake can invalidate an otherwise correct reasoning process, greatly hindering training efficiency; and exploration stagnation, where models tend to focus on solutions within their ”comfort zone”, lacking the motivation to explore potentially more effective alternatives. To address these challenges, we propose StepHint, a novel RLVR algorithm that utilizes multi-level stepwise hints to help models explore the solution space more effectively. StepHint partitions valid reasoning chains into reasoning steps using our proposed adaptive partitioning method. The initial few steps are used as hints, and simultaneously, multiple-level hints (each comprising a different number of steps) are provided to the model. This approach directs the model’s exploration toward a promising solution subspace while preserving its flexibility for independent exploration. By providing hints, StepHint mitigates the near-miss reward problem, thereby improving training efficiency. Additionally, the external reasoning pathways help the model develop better reasoning abilities, enabling it to move beyond its ”comfort zone” and mitigate exploration stagnation. StepHint outperforms competitive RLVR enhancement methods across six mathematical benchmarks and two out-of-domain benchmarks.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Hao Luo Wuhan University, Xiao Yan Wuhan University, Xinyan Li, Qiming Zeng Wuhan University, Yuhao Lin, Shanshan Feng Wuhan University and The Agency for Science, Technology and Research, Hao Wang Wuhan University, Jiawei Jiang Wuhan University
Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by generating detailed Chain-of-Thought (CoT) reasoning. However, they tend to apply a uniform, computation-intensive deep reasoning strategy to all problems, leading to unnecessary overhead on simple tasks. This significantly hinders their efficiency in real-world applications. While existing methods have improved reasoning efficiency to some extent, they still face critical challenges such as conflicting objectives, limited adaptability. To address these limitations, we propose AdaMix, an adaptive reasoning framework via decoupled optimization. To mitigate optimization conflicts, AdaMix first constructs two specialized adapters: an efficiency-oriented short adapter and an accuracy-oriented long adapter. It then incorporates a difficulty-aware routing model that assesses problem complexity to predict a reasoning intensity coefficient. This coefficient is used to dynamically interpolate a mixed adapter from the two base adapters, enabling fine-grained reasoning control. Our experiment demonstrates that our AdaMix reduces the average response length of DeepSeek-R1-Distill-Qwen-7B by 54.9% while improving accuracy by up to 4.8% on five mathematical datasets, thus indicating a favorable accuracy-efficiency trade-off.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Instruction tuning plays a crucial role in enhancing large language models (LLMs) to better understand complex user instructions. While various data selection and revision methods have been explored to optimize instruction tuning datasets, they face two main challenges: unreasonable pruning of potentially valuable low-quality data and the persistence of noise or semantic drift during revision. To address these issues, we propose a novel automated iterative framework for instruction data optimization. Our framework introduces Instruction Quality Differentiation to identify valuable high-quality and low-quality data across multiple dimensions. For low-quality data, we propose a Feedback-driven Iterative Refinement mechanism with an “evaluate-refine-review” process and design an Output Alignment module to improve data quality. Experiments on seven public benchmark datasets show that our framework outperforms state-of-the-art methods, achieving 2.09% and 2.60% improvements on the Alpaca and Dolly datasets, respectively, with high data efficiency. Our code and data are available at the anonymous link https://github.com/surihuhang/From-Selection-to-Refinement–-Iterative-Optimization-for-Instruction-Data.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Lang Gao Mohamed bin Zayed University of Artificial Intelligence, Xuhui Li, Chenxi Wang, Mingzhe Li, Wei Liu, Zirui Song, Jinghui Zhang Mohamed bin Zayed University of Artificial Intelligence, Rui Yan Wuhan University 等 10 位作者
As large language models (LLMs) increasingly imitate personal writing styles, personalization has become a key challenge for machine-generated text (MGT) detection. Yet personalized MGT detection remains largely underexplored. In this work, we introduce StyloBench, the first benchmark for evaluating detector robustness under personalization, built from literary and blog texts paired with their LLM-generated imitations. Experiments across diverse detectors show pronounced performance instability under personalization, with frequent inversions relative to general-domain behavior. To better understand this limitation, we conduct an in-depth analysis and attribute it to a feature-inversion trap, i.e., features that are effective for separating human-written text (HWT) from MGT in general flip their effect in personalized contexts, ultimately misleading detectors. Motivated by this, we propose StyloCheck, a diagnostic framework for predicting detector robustness under personalization. StyloCheck identifies the inverted features and quantifies detector dependence using perturbed texts pronounced in the features. In our experiments, StyloCheck predicts both the direction and magnitude of cross-domain performance shifts with an 85% correlation to actual outcomes. We hope this work will raise awareness of the structural risks introduced by personalization and motivate more robust approaches to personalized MGT detection. The code is available at: https://github.com/mbzuai-nlp/Personalized_MGT_Detect
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Chengyan Wu, Bolei Ma, Ningyuan Deng The Hong Kong University of Science and Technology, He Yanqing Institute of Scientific and Technical Information of China, Yun Xue, Liu Xiaoyong
Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies lack more robust feature alignment and finer aspect-level alignment. In this paper, we propose a novel framework, MSMO: Multi-Scale and Multi-Objective optimization for cross-lingual ABSA. During multi-scale alignment, we achieve cross-lingual sentence-level and aspect-level alignment, aligning features of aspect terms in different contextual environments. Specifically, we introduce code-switched bilingual sentences into the language discriminator and consistency training modules to enhance the model’s robustness. During multi-objective optimization, we design two optimization objectives: supervised training and consistency training, aiming to enhance cross-lingual semantic alignment. To further improve model performance, we incorporate distilled knowledge of the target language into the model. Results show that MSMO significantly enhances cross-lingual ABSA by achieving state-of-the-art performance across multiple languages and models.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表