论文检索

Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng

Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. The prevailing approach to aligning vision and language models involves freezing both the vision encoder and the language model while training small connector modules. However, this strategy heavily depends on the intrinsic capabilities of the language model, which can be suboptimal for lightweight models with limited representational capacity. In this work, we investigate this alignment bottleneck through the lens of mutual information, positing that the constrained capacity of the language model inherently limits the Effective Mutual Information (EMI) between multimodal inputs and outputs, thereby compromising alignment quality. To address this challenge, we propose TinyAlign, a novel framework inspired by Retrieval-Augmented Generation, which strategically retrieves relevant context from a memory bank constructed from training data to enrich multimodal inputs and enhance their alignment. Extensive empirical evaluations reveal that TinyAlign significantly reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. Remarkably, it allows models to achieve baseline-level performance with only 40% of the fine-tuning data, highlighting exceptional data efficiency. Our work thus offers a practical pathway for developing more capable lightweight VLMs while introducing a fresh theoretical lens to better understand and address alignment bottlenecks in constrained multimodal systems.

A Pranav, Shane Storks, Anne Lauscher

ACL removed the anonymity period for conference submissions in February 2024, allowing unrestricted preprinting during review.To examine how preprints and author recognition affect outcomes across institutional hierarchies, we track preprinting trends for 47k publications, survey 75 NLP researchers, interview 14 community members, and analyze 1.9k peer reviews. We observe that more elite institutions post preprints more frequently (52% vs. 36% by 2025). Most participants agree that preprinting gives these institutions an advantage in peer review, and indeed, reviewer knowledge of authors inflates scores at elite institutions (d = 0.43, p < 0.001) but not elsewhere, also lowering review quality. Nonetheless, the anonymity period was found largely ineffective; instead, underrepresented researchers emphasize struggles with visibility, review quality, and external structural barriers. To counteract these inequities, we make recommendations for review quality improvement and increasing investment in diversity initiatives that center the perspectives of affected communities.

Arnav Attri, Anuj Attri

Opinion summarization systems aggregate customer sentiments without capturing the emotional factors that drive purchasing decisions, resulting in shallow summaries that overlook the affective dimensions shaping customer experiences and fail to explain why customers feel the way they do. This gap exists because prior research has neglected the interplay between expressed opinions and their underlying emotional contexts. To bridge this gap, we introduce Emotion-Aware Opinion Summarization (EAOS), a framework leveraging Large Language Models (LLMs) to integrate emotional dimensions into opinion summaries, moving beyond conventional sentiment polarity. To support this task, we develop a large-scale (40K product–summary pairs) training dataset, an evaluation benchmark, a compact 1B-parameter model that matches 70B-scale performance via knowledge distillation, and methods for generating and evaluating emotion-aware summaries. A user study shows that 82% of readers prefer our emotion-aware summaries (p < .001), confirming that adding emotion helps in making purchase decisions.

Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu

Large language models (LLMs) for code generation have achieved remarkable progress in synthesizing functional code from natural language instructions. However, a critical challenge persists in generating visually accurate and structurally sound front-end code that faithfully renders user-intended layouts and interfaces. Most existing works focus primarily on functional correctness, overlooking the visual fidelity and rendering quality essential for front-end development. To address this gap, we present a comprehensive data construction and training pipeline to enhance front-end code generation capabilities in code LLMs. We use a three-stage training approach: continual pre-training on synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning with checklist-based rewards to improve model performance. Our comprehensive evaluation on front-end code generation benchmarks reveals that even strong base models struggle with visual faithfulness and layout complexity. Our fully-trained model demonstrated substantial improvements over baseline approaches across all domains, achieving competitive performance with frontier models while maintaining generation efficiency, underscoring the critical importance of stage-aligned data curation and vision-grounded optimization in developing reliable front-end code generation systems. Our code and data are open-sourced at https://github.com/leanfeng1/FrontCoder.

Taojie Zhu, Dongyang Xu, Ding Zou, Sen Zhao, Qiaobo Hao, Zhiguo Yang, Yonghong He

Post-training paradigms for Large Language Models (LLMs), primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dilemma: SFT provides stability (low variance) but suffers from high fitting bias, while RL enables exploration (low bias) but grapples with high gradient variance. Existing unified optimization strategies often employ naive loss weighting, overlooking the statistical conflict between these distinct gradient signals. In this paper, we provide a rigorous theoretical analysis of this bias-variance trade-off and propose DYPO (Dynamic Policy Optimization), a unified framework designed to structurally mitigate this conflict. DYPO integrates three core components: (1) a Group Alignment Loss (GAL) that leverages intrinsic group dynamics to significantly reduce RL gradient variance; (2) a Multi-Teacher Distillation mechanism that corrects SFT fitting bias via diverse reasoning paths; and (3) a Dynamic Exploitation-Exploration Gating mechanism that adaptively arbitrates between stable SFT and exploratory RL based on reward feedback. Theoretical analysis confirms that DYPO linearly reduces fitting bias and minimizes overall variance. Extensive experiments demonstrate that DYPO significantly outperforms traditional sequential pipelines, achieving an average improvement of 4.8% on complex reasoning benchmarks and 13.3% on out-of-distribution tasks.

Chak Tou Leong, Dingwei Chen, Heming Xia, Qingyu Yin, Sunbowen Lee, Jian Wang, Wenjie Li

Large reasoning models (LRMs) have achieved remarkable success through step-by-step chains of thought, yet they often suffer from excessive redundancy or unfaithful reasoning. Existing methods for shaping LRM behavior typically rely on reinforcement learning or fine-tuning with gold-standard reasoning traces, a paradigm that is both computationally expensive and difficult to scale. In this paper, we reveal that LRMs possess latent reasoning beliefs that internally track their own reasoning traits, which can be captured through simple logit probing without specialized training. Building on this insight, we propose Reasoning Belief Engineering (RELIEF), a simple yet effective framework that shapes LRM behavior by aligning the model’s self-concept with a target belief blueprint. Crucially, RELIEF completely bypasses the need for reasoning-trace supervision. It internalizes desired traits by fine-tuning on synthesized, self-reflective QA pairs that affirm the target belief. Extensive experiments on efficiency and faithfulness tasks demonstrate that RELIEF matches or outperforms behavior-supervised and preference-based baselines while requiring significantly lower training costs. Our analysis further validates that shifting a model’s reasoning belief effectively shapes its actual behavior.

Xin Yin, Zixiang Ding, Yiang Zhang, Qiang Wang, Rui Wang, Chao Ni, Zhe Cui

Large Language Models (LLMs) have achieved strong performance on many code-related tasks, yet they still struggle with repository-level scenarios where reasoning depends on long, noisy, and structurally complex contexts. While existing retrieval methods, including both similarity-based and graph-based approaches, can identify relevant code snippets, they often retrieve excessive contexts that intensify the "lost-in-the-middle" phenomenon and dilute model attention with redundant contexts. To address this, we present RepoDistill, a novel framework that integrates retrieval with learned budget allocation for fine-grained context compression. RepoDistill first employs a plug-and-play lightweight GraphRAG to retrieve context that follows logical flows. It then applies Compression-Aware Budget Allocation guided by Compression-Aware Policy Optimization, which formulates context management as a multi-step decision problem and learns allocation policies for contexts. Experiments show that RepoDistill outperforms baselines, achieving gains of up to +7.00 on SWE-QA, +24.4% on CoderEval, and +0.25 on LongCodeU. Furthermore, a compact 4B-parameter model trained with RepoDistill can serve as an effective context compressor for closed-source LLMs, reducing input tokens by up to 66% while maintaining comparable performance. We release our code at https://anonymous.4open.science/r/RepoDistill-12B0.

Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun

Composed Image Retrieval (CIR) enables users to search for target images using both a reference image and manipulation text, offering substantial advantages over single-modality retrieval systems. However, existing CIR methods suffer from representation space fragmentation: queries and targets comprise heterogeneous modalities and are processed by distinct encoders, forcing models to bridge misaligned representation spaces only through post-hoc alignment, which fundamentally limits retrieval performance. As evidenced by t-SNE visualization in Fig.(a), this architectural asymmetry manifests as three distinct, well-separated clusters in the feature space, directly demonstrating how heterogeneous modalities create fundamentally misaligned representation spaces from initialization. In this work, we propose CSMCIR, a unified representation framework that achieves efficient query-target alignment through three synergistic components. First, we introduce a Multi-level Chain-of-Thought (MCoT) prompting strategy that guides Multimodal Large Language Models to generate discriminative, semantically compatible captions for target images, establishing modal symmetry. Building upon this, we design a symmetric dual-tower architecture where both query and target sides utilize the identical shared-parameter Q-Former for cross-modal encoding, ensuring consistent feature representations and further reducing the alignment gap. Finally, this architectural symmetry enables an entropy-based, temporally dynamic Memory Bank strategy that provides high-quality negative samples while maintaining consistency with the evolving model state. Extensive experiments on four benchmark datasets demonstrate that our CSMCIR achieves state-of-the-art performance with superior training efficiency. Comprehensive ablation studies further validate the effectiveness of each proposed component.

Xingjian Diao, Zheyuan Liu, Chunhui Zhang, Weiyi Wu, Keyi Kong, Lin Shi, Kaize Ding, Soroush Vosoughi, Jiang Gui

Large Vision-Language Models (LVLMs) have exhibited strong reasoning capabilities through chain-of-thought mechanisms that generate step-by-step rationales. However, such slow-thinking approaches often lead to overthinking, where models produce excessively verbose responses even for simple queries, resulting in test-time inefficiency and even degraded accuracy. Prior work has attempted to mitigate this issue via adaptive reasoning strategies, but these methods largely overlook a fundamental bottleneck: visual perception failures. We argue that stable reasoning critically depends on low-level visual grounding, and that reasoning errors often originate from imperfect perception rather than insufficient deliberation. To address this limitation, we propose Gated Perception-Reasoning Optimization (GPRO), a meta-reasoning controller that dynamically routes computation among three decision paths at each generation step: a lightweight fast path, a slow perception path for re-examining visual inputs, and a slow reasoning path for internal self-reflection. To learn this distinction, we derive large-scale failure attribution supervision from approximately 790k samples, using teacher models to distinguish perceptual hallucinations from reasoning errors. We then train the controller with multi-objective reinforcement learning to optimize the trade-off between task accuracy and computational cost under uncertainty. Experiments on five benchmarks demonstrate that GPRO substantially improves both accuracy and efficiency, outperforming recent slow-thinking methods while generating significantly shorter responses.

Xinzhe Li

Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via tree search (LITS) achieves strong performance but is highly inefficient. We propose Chain-in-Tree (CiT), a plug-in framework that decides when to branch during search instead of expanding at every step. CiT introduces lightweight Branching Necessity (BN) evaluations, including BN-DP (direct prompting) and BN-SC (self-consistency). Integrated into Tree of Thoughts, ReST-MCTS, and RAP, BN-DP reduces token generation, model calls, and runtime by 75-85% on GSM8K and Math500, with often negligible or no accuracy loss. BN-SC typically yields substantial savings (up to 80%) generally but shows instability in 1-4 out of 14 settings, caused by a small subset of examples that produce extremely long reasoning steps. We theoretically prove that BN-DP never increases policy invocations and release unified implementations applicable across LITS frameworks. The full codebase is publicly available at https://github.com/xinzhel/chain_in_tree.

Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Aleksandr Gordeev, Vladimir Dokholyan, Maksim Kuprashevich

With the increasing quality and spread of LLM assistants, the amount of generated content is growing rapidly. In many cases and tasks, such texts are already indistinguishable from those written by humans, and the quality of generation continues to increase. At the same time, detection methods are advancing more slowly than generation models, making it challenging to prevent misuse of generative AI technologies. We propose GigaCheck, a dual-strategy framework for AI-generated text detection. At the document level, we leverage the representation learning of fine-tuned LLMs to discern authorship with high data efficiency. At the span level, we introduce a novel structural adaptation that treats generated text segments as "objects." By integrating a DETR-like vision model with linguistic encoders, we achieve precise localization of AI intervals, effectively transferring the robustness of visual object detection to the textual domain. Experimental results across three classification and three localization benchmarks confirm the robustness of our approach. The shared fine-tuned backbone delivers strong accuracy in both scenarios, highlighting the generalization power of the learned embeddings. Moreover, we successfully demonstrate that visual detection architectures like DETR are not limited to pixel space, effectively generalizing to the localization of generated text spans. To ensure reproducibility and foster further research, we publicly release our source code.

Yuyan Zhou, Weiyu Chen, James Kwok

Diffusion-based Large Language Models (dLLMs) are emerging as a powerful alternative to traditional autoregressive models. These models learn to generate text by iteratively denoising masked sequences. In this work, we identify a critical problem in dLLMs: the model’s attention is wastefully expended on uninformative mask tokens, diluting its focus on meaningful context. We term this phenomenon “attention dilution”. We further show that this artifact is amplified by token-level noising, whereas models employing sequence-level noise exhibit a reduced effect. To resolve this problem, we introduce Truncated Block Generation, a novel sampling algorithm that not only mitigates attention dilution but also enables faster inference and flexible-length sequence generation. Extensive experiments validate our analysis and demonstrate the marked effectiveness of our proposed method in enhancing both the performance and efficiency of dLLMs.

Wenpeng Xing, Moran Fang, Guangtai Wang, Changting Lin, Meng Han

While Large Language Models (LLMs) have achieved remarkable performance, they remain vulnerable to jailbreak attacks that circumvent safety constraints. Existing strategies, ranging from heuristic prompt engineering to computationally intensive optimization, often face significant trade-offs between effectiveness and efficiency. In this work, we propose Contextual Representation Ablation (CRA), a novel inference-time intervention framework designed to dynamically silence model guardrails. Predicated on the geometric insight that refusal behaviors are mediated by specific low-rank subspaces within the model’s hidden states, CRA identifies and suppresses these refusal-inducing activation patterns during decoding without requiring expensive parameter updates or training. Empirical evaluation across multiple safety-aligned open-source LLMs demonstrates that CRA significantly outperforms baselines. By revealing that safety constraints can be surgically ablated from internal representations, our findings expose the intrinsic fragility of current alignment mechanisms and underscore the urgent need for more robust latent-space defenses.

Yi Chen, Yuying Ge, Rui Wang, Yixiao Ge, Junhao Cheng, Ying Shan, Xihui Liu

Recent reinforcement learning (RL) approaches, such as outcome-supervised GRPO, have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) remains underexplored. Progress has been further limited by the lack of evaluation settings that jointly test perception and reasoning under controlled generalization challenges. To enable such analysis, we present **SEED-Bench-R1**, a structured testbed featuring real-world video tasks and hierarchical evaluation across in-distribution, cross-environment, and cross-environment-task scenarios. Our analysis reveals that standard outcome-supervised GRPO often yields "logical incoherence"—achieving correct answers through flawed reasoning—due to its exclusive focus on final-answer rewards and rigid KL penalties. To address this, we propose **GRPO-CARE**, a consistency-aware RL framework that eliminates KL penalties while introducing a two-tiered reward system: a base reward for accuracy and an adaptive bonus for consistency. This bonus, derived from a slowly evolving reference model through group-relative likelihood calibration, rewards reasoning paths that logically support the final answer without requiring expensive process supervision. Experiments on SEED-Bench-R1 show that GRPO-CARE consistently outperforms standard GRPO, achieving a 6.7% gain on the hardest evaluation level and a 24.5% increase in reasoning consistency. Moreover, models trained with GRPO-CARE transfer effectively to diverse video understanding and even language-only reasoning benchmarks, validating its robustness and generality.

Hui Huang, Julien Velcin, Yacine Kessaci

Long-document question answering is challenging because relevant evidence is often scattered across distant sections. Traditional long-document QA/RAG pipelines often suffer from context fragmentation, retrieving locally plausible but structurally misaligned passages. We present the Hierarchical Quantized Document Retriever (HQDR), a framework that aligns hierarchical graph representations with a universal token vocabulary and integrates explicit structure into retrieval. By grounding continuous structural features in a fixed, discrete semantic space, HQDR captures universal hierarchical patterns rather than overfitting to specific layouts. We further propose a hybrid scoring mechanism that decouples semantic matching from structural alignment. Extensive experiments on QASPER and Natural Questions demonstrate that HQDR achieves consistent gains over strong baselines and exhibits superior robustness when transferring between datasets with distinct structural characteristics.

Weiyu Ma, Yuqian Fu, Zecheng Zhang, Bernard Ghanem, Guohao Li

We introduce AVACraft — the first multimodal benchmark environment for complex decision-making in StarCraft II, supporting both traditional Multi-Agent Reinforcement Learning (MARL) and modern Vision-Language Model (VLM) paradigms. Existing StarCraft II environments like SMAC rely on abstract state representations that deviate from human perception and lack support for emerging VLM-based decision-making. AVACraft mitigates these limitations via a unified framework, which provides RGB visual inputs, natural language observations and structured state information, enabling systematic comparisons between training-based and zero-shot decision-making methods. Our benchmark features 21 carefully designed scenarios covering micromanagement, coordination and strategic planning, with standardized evaluation protocols for both paradigms. We establish comprehensive baselines using four MARL algorithms (IQL, QMIX, QTRAN, VDN) and multiple state-of-the-art VLMs (GPT-4o, Qwen-VL, etc.). Experimental results reveal their complementary strengths: MARL methods achieve up to 27.1% win rate after 1M training steps in complex scenarios, while VLMs deliver superior zero-shot performance (75–81% win rate) and human-aligned decision processes without any training. Systematic analysis (including expert human evaluation) also identifies key trade-offs between training efficiency, performance ceilings and interpretability across the two paradigms. Our implementation is available at https://anonymous.4open.science/r/VLM-Play-StarCraft2-70C4 .

Rui Sun, Jie Ding, Chenghua Gong, Tianjun Gu, Yihang Jiang, Juyuan Zhang, Liming Pan, Linyuan Lü

Optimizing communication topology in LLM–based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://github.com/Sundiasy/TopoDIM.

Xingjin Wang, Howe Tissue, Lu Wang, Linjing Li, Daniel Dajun Zeng

We observe that entropy in reinforcement learning functions analogously to the learning rate in LLMs. Maintaining stable entropy, as demonstrated in DAPO, helps stabilize RL training, while rapid entropy annealing (i.e., so-called entropy collapse) accelerates local performance improvement and enables faster convergence. We argue that these two processes are not antithetical, but can be effectively controlled and scheduled within a single training run, similar to learning rate scheduling. We propose Entropy Schduling (ES), which optimizes different pre-set goals (e.g. k in optimizing Pass@k) by controlling and scheduling entropy at each step of the RL process. We find that maintaining stable entropy early in training followed by entropy annealing achieves superior performance. Moreover, since stable-state entropy and annealed entropy exhibit distinctly different learning dynamics, curriculum learning can be seamlessly integrated to maximize model performance based on different entropy phases. We show that entropy scheduling is straightforward to implement and intuitive in design. Extensive experiments suggest that it delivers consistent and stable performance improvements across diverse models and algorithms.

Jiaxin Wu, Jie Zhao, Huangxinrong, Cuihong Zhang, Xiuzhu Wu

Whether the personality of LLMs can be intentionally reshaped remains controversial. Existing studies often limited to small models, argue for its immutability. Crucially, prior studies fail to uncover that different LLMs exhibit significant compliance divergence when exposed to personality-inducing contexts. To bridge this gap, we introduce Personality Induction Framework (PIF), which systematically reshapes the personality of different LLMs via multi-agent collaboration. Specifically, via Generator-Judge agents, PIF paraphrases MBTI questions to create semantically equivalent but expressively diverse inducing contexts, enabling LLMs to learn personality patterns instead of superficial token matching. Also, PIF achieves fine-grained personality modulation by controlling the intensity of inducing contexts. Extensive experiments on worldwide mainstream LLMs show that PIF reliably transforms their original personalities into desired target personalities. Notably, we find that the outputs of most Western LLMs behave like “Chameleons”, exhibiting high personality plasticity; whereas the outputs of most Eastern LLMs act as “Guardians”, manifesting pronounced cognitive resistance. Strikingly, extreme induction intensity (100%) triggers a counter-intuitive “Alignment Rebound” in Guardians, resulting in the opposite direction rather than compliance. These findings suggest that LLM personality is a dynamic equilibrium shaped by the trade-off between instruction compliance and cognitive resistance.

Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu 等

Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning–answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby reducing factual fidelity. To address this issue, we propose MR-ALIGN, a Meta-Reasoning informed alignment framework that enhances factuality without relying on external verifiers. MR-ALIGN quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. This re-weighting reshapes token-level signals into probability-aware segment scores, encouraging coherent reasoning trajectories that are more conducive to factual correctness. Empirical evaluations across four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. These results highlight that aligning the reasoning process itself, rather than merely the outputs, is pivotal for advancing factuality in LRMs.