论文检索

Han Jang, Junhyeok Lee, Heeseong Eum, Kyu Sung Choi

Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are predominantly trained on professional literature, limiting their ability to communicate findings in the lay register required for patient-centered care. While text-centric research has actively developed resources for simplifying medical jargon, there is a critical absence of large-scale multimodal benchmarks designed to facilitate lay-accessible medical image understanding. To bridge this resource gap, we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. Unlike naive simplification approaches that risk hallucination, our dataset is constructed via a Structured Concept-Grounded Refinement (SCGR) pipeline. This method enforces strict semantic equivalence by integrating Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) with micro-level entity constraints. MedLayBench-V provides a verified foundation for training and evaluating next-generation Med-VLMs capable of bridging the communication divide between clinical experts and patients.

Kun Zhou, Jiakai He, Wenmian Yang, Zhensheng Wang, Yiquan Zhang, Weijia Jia

Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Existing automation methods mostly follow fixed template filling and cannot support dynamic updates for diverse, user-authored slide decks. We therefore define “Dynamic Slide Update via Natural Language Instructions on User-provided Templates” and introduce DynaSlide, a large-scale benchmark with 20,036 real-world instruction–execution triples (source slide, user instruction, target slide) grounded in a shared external database and built from business reporting slides under bring-your-own-template (BYO-template) conditions. To tackle this task, we propose SlideAgent, an agent-based framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions. SlideAgent updates content while preserving layout and style, providing a strong reference baseline on DynaSlide. We further design end-to-end and component-level evaluation protocols that reveal key challenges and opportunities for future research. The dataset and code are available at https://anonymous.4open.science/r/604E/.

Minh-Vuong Nguyen, Fatemeh Shiri, Zhuang Li, Karin Verspoor

Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requires reliable handling of patient measurements in heterogeneous clinical notes. Existing evaluations of LLMs for clinical numerical reasoning provide limited operation-level coverage, restricted primarily to arithmetic computation, and rarely assess the robustness of numerical understanding across clinical note formats. We introduce ClinicNumRobBench, a benchmark of 1,624 context-question instances with ground-truth answers that evaluates four main types of clinical numeracy: value retrieval, arithmetic computation, relational comparison, and aggregation. To stress-test robustness, ClinicNumRobBench presents longitudinal MIMIC-IV vital-sign records in three semantically equivalent representations, including a real-world note-style variant derived from the Open Patients dataset, and instantiates queries using 42 question templates. Experiments on 17 LLMs show that value retrieval is generally strong, with most models exceeding 85% accuracy, while relational comparison and aggregation remain challenging, with some models scoring below 15%. Fine-tuning on medical data can reduce numeracy relative to base models by over 30%, and performance drops under note-style variation indicate LLM sensitivity to format. ClinicNumRobBench offers a rigorous testbed for clinically reliable numerical reasoning. Code and data URL are available on https://github.com/MinhVuong2000/ClinicNumRobBench.

Chaoyang Wang, Kaituo Feng, Dongyang Chen, Zhongyu Wang, Zhixun Li, Sicheng Gao, Meng Meng, Xu Zhou, Manyuan Zhang, Yuzhang Shang 等

Recent advances have shown that multimodal large language models (MLLMs) benefit from multimodal interleaved chain-of-thought (CoT) with vision tool interactions. However, existing open-source models often exhibit blind tool-use reasoning patterns, invoking vision tools even when they are unnecessary, which significantly increases inference overhead and degrades model performance. To this end, we propose AdaTooler-V, an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools. First, we introduce AT-GRPO, a reinforcement learning algorithm that adaptively adjusts reward scales based on the Tool Benefit Score of each sample, encouraging the model to invoke tools only when they provide genuine improvements. Moreover, we construct two datasets to support training: AdaTooler-V-CoT-100k for SFT cold start and AdaTooler-V-300k for RL with verifiable rewards across single-image, multi-image, and video data. Experiments across twelve benchmarks demonstrate the strong reasoning capability of AdaTooler-V, outperforming existing methods in diverse visual reasoning tasks. Notably, AdaTooler-V-7B achieves an accuracy of 89.8% on the high-resolution benchmark V*, surpassing the commercial proprietary model GPT-4o and Gemini 1.5 Pro.

Tong Sun, Mingyang Ma, Cheng Yu

Aspect-Based Sentiment Analysis (ABSA) is critical for extracting actionable product insights from e-commerce reviews. However, most public ABSA benchmarks are restricted to short texts and a limited range of domains, and therefore underrepresent the challenges posed by real-world reviews—where multiple aspects co-occur, colloquial and noisy expressions are common, and evidence must often be aggregated across sentences in long contexts.We introduce E-ABSA20K, a multi-domain dataset of 20K reviews from four product categories (Women’s Bags, Dresses, Cosmetics, and Furniture), annotated with review-level sentiment quads. Compared to existing benchmarks, E-ABSA20K contains substantially longer and more aspect-dense reviews, averaging 63.9 words and 6.0 quads per review. We further propose a two-stage propose-and-verify framework for review-level quadruple extraction (target, aspect, opinion, sentiment). The first stage generates high-recall candidates under strict schema constraints, while the second stage conducts explicit grounding, scope, and modality verification, followed by review-level consolidation to mitigate hallucinations and scope leakage in long reviews. Experiments across multiple Qwen3 model sizes demonstrate that our approach consistently outperforms single-stage prompting (with and without chain-of-thought) as well as competitive ABSA extraction baselines, improving quad-level micro-F1 and robustness on discourse-hard cases such as comparisons and conditionals.

Yingtao Ren, Xiao Luo, Yu-Cheng Chang, Chin-teng Lin

Multi-step retrieval-augmented generation has attracted increasing attention due to its capacity to improve the factuality of large language models with iterative retrieved knowledge. However, the performance of multi-step RAG systems is susceptible to potential retrieval noise and fabricated documents in real-world scenarios. Current approaches usually utilize supervised fine-tuning on predetermined noisy contexts to enhance the robustness. However, their performance remains inadequate when it comes to more complicated long-context scenarios due to the lack of adaptability. Towards this end, we propose a novel framework named Context-attended Adversarial Reinforcement Learning (CARE) for multi-step RAG systems against attacks. The core of our CARE is to conduct reinforcement learning on adversarial samples which are alternatingly enhanced with text gradients. In particular, our CARE includes a reward model to identify the accuracy of responses, which is minimized for the generation of adversarial samples with text gradients. These context-attended noisy samples are then utilized for reinforcement learning to maximize the rewards. The whole framework is conducted alternatingly from easy to hard samples to ensure the smoothness of the optimization. Extensive experiments on multi-step RAG benchmark datasets are conducted to validate the superiority of our proposed CARE in multiple noisy scenarios. Our code is available at https://github.com/yingtaoren/CARE.

He Chang, Zhulin Tao, Lifang Yang, Xianglin Huang, Yunshan Ma

Despite the importance of open-ended event forecasting for risk management, current LLM-based methods predominantly target only the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. To bridge this gap, we advance open-ended event forecasting from pinpoint forecasting to *scatter forecasting* by introducing the proxy task of hypothesis generation. This paradigm aims to generate an inclusive and diverse set of hypotheses that broadly cover the space of plausible future events. To this end, we propose SCATTER, a reinforcement learning framework that jointly optimizes inclusiveness and diversity of the hypothesis. Specifically, we design a novel hybrid reward that consists of three components: 1) a validity reward that measures semantic alignment with observed events, 2) an intra-group diversity reward to encourage variation within sampled responses, and 3) an inter-group diversity reward to promote exploration across distinct modes. By integrating the validity-gated score into the overall objective, we confine the exploration of wildly diversified outcomes to contextually plausible futures, preventing the mode collapse issue. Experiments on two real-world benchmark datasets, i.e., OpenForecast and OpenEP, demonstrate that SCATTER significantly outperforms strong baselines. Our code is available at https://github.com/Sambac1/SCATTER .

Junqi Chen, Sirui Chen, Chaochao Lu

Causal inference is essential for decision-making but remains challenging for non-experts. While large language models (LLMs) show promise in this domain, their precise causal estimation capabilities are still limited, and the impact of post-training on these abilities is insufficiently explored. This paper examines the extent to which post-training can enhance LLMs’ capacity for causal inference. We introduce CausalGym, a comprehensive dataset comprising seven core causal tasks for training and five diverse test sets. Using this dataset, we systematically evaluate five post-training approaches: SFT, DPO, KTO, PPO, and GRPO. Across five in-domain and four existing benchmarks, our experiments demonstrate that appropriate post-training enables smaller LLMs to perform causal inference competitively, often surpassing much larger models. Our 14B-parameter model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. Furthermore, the post-trained LLMs exhibit strong generalization and robustness under real-world conditions such as distribution shifts and noisy data. Collectively, these findings provide the first systematic evidence that targeted post-training can produce reliable and robust LLM-based causal reasoners.

Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider 等

Agentic repository-level code understanding is essential for automating complex software engineering tasks, yet the field lacks reliable benchmarks. Existing evaluations often overlook the long tail topics and rely on popular repositories where Large Language Models (LLMs) can cheat via memorized knowledge. To address this, we introduce SWE-QA-Pro, a benchmark constructed from diverse, long-tail repositories with executable environments. We enforce topical balance via issue-driven clustering to cover under-represented task types and apply a rigorous difficulty calibration process: questions solvable by direct-answer baselines are filtered out. This results in a dataset where agentic workflows significantly outperform direct answering (e.g., a ~13-point gap for Claude Sonnet 4.5), confirming the necessity of agentic codebase exploration. Furthermore, to tackle the scarcity of training data for such complex behaviors, we propose a scalable synthetic data pipeline that powers a two-stage training recipe: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning from AI Feedback (RLAIF). This approach allows small open models to learn efficient tool usage and reasoning. Empirically, a Qwen3-8B model trained with our recipe surpasses GPT-4o by 2.3 points on SWE-QA-Pro and substantially narrows the gap to state-of-the-art proprietary models, demonstrating both the validity of our evaluation and the effectiveness of our agentic training workflow.

Loris Bergeron, Ioana Buhnila, Jerome Francois, Radu State

Large Language Models excel at NLP tasks but remain prone to hallucinations, limiting trust in real-world applications. We present HalluGuard, a 4B-parameter Small Reasoning Model (SRM) designed as a guardrail for Retrieval-Augmented Generation (RAG) pipelines, which classify document-claim pairs as grounded or hallucinated in closed-book, document-grounded settings and produces evidence-grounded justifications. Our approach combines (i) a domain-agnostic synthetic dataset derived from FineWeb and refined through multi-stage curation and data reformation, (ii) synthetic grounded and hallucinated claims, and (iii) preference-based fine-tuning with Odds Ratio Preference Optimization (ORPO) to distill large-model reasoning into a smaller backbone. On the RAGTruth subset of the LLM-AggreFact benchmark, HalluGuard achieves 84.4% balanced accuracy (BAcc), surpassing specialized models, MiniCheck (7B; 84.0%) and Granite Guardian 3.3 (8B; 82.2%) while using roughly half their parameters. Across the benchmark, it reaches 77.1% BAcc, surpassing larger general-purpose LLMs such as GPT-4o (75.9%). HalluGuard and datasets will be released upon acceptance.

Yang Xiao, Eun-Jung Holden, Ting Dang

Recent speech foundation models excel at multilingual automatic speech recognition (ASR) for high-resource languages, but adapting them to low-resource languages remains challenging due to data scarcity and efficiency constraints. Full-model fine-tuning is computationally expensive and prone to overfitting, while parameter-efficient methods like LoRA apply adaptation uniformly across layers, overlooking internal representations thus compromising effectiveness and efficiency. We analyze multilingual ASR models and reveal a U-shaped adaptability pattern: early and late layers are language-specific and require more adaptation, while intermediate layers retain shared semantics and need less. Building on this observation, we propose DAMA, a Depth-Aware Model Adaptation framework that allocates adaptation capacity according to each layer’s role. DAMA also introduces Singular Value Decomposition (SVD)-based initialization to constrain adaptation and preserve the U-shaped pattern, as well as a frozen middle-layer basis for further efficiency. Evaluated on 18 low-resource languages across two benchmark datasets, DAMA matches or surpasses state-of-the-art accuracy with 80% fewer trainable parameters, achieves a 29% error reduction under extreme data scarcity, and significantly improves memory, training time, and computational efficiency over baselines. These results highlight the benefits of structure-aware adaptation for efficient, scalable multilingual ASR.

Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu 等

Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an user clicks a product webpage and then leaves, it might because there are certain features that don’t satisfy the user, which serve as an important indicator of on-the-spot user preferences. However, all prior works fail to capture and model customer intention effectively because insufficient information exploitation and only apparent information like descriptions and titles are used. There is also a lack of data and corresponding benchmark for explicitly modeling intention in E-commerce product purchase sessions. To address these issues, we introduce the concept of an intention tree and propose a dataset curation pipeline. Together, we construct a sibling multimodal benchmark, SessionIntentBench, that evaluates L(V)LMs’ capability on understanding inter-session intention shift with four subtasks. With 1,952,177 intention entries, 1,132,145 session intention trajectories, and 13,003,664 available tasks mined using 10,905 sessions, we provide a scalable way to exploit the existing session data for customer intention understanding. We conduct human annotations to collect ground-truth label for a subset of collected data to form an evaluation gold set. Extensive experiments on the annotated data further confirm that current L(V)LMs fail to capture and utilize the intention across the complex session setting. Further analysis show injecting intention enhances LLMs’ performances.

Haoyue Yang, Xuanle Zhao, Xuexin Liu, Feibing Jiang, Yao Zhu

The paradigm of programmable diagram generation is evolving rapidly, playing a crucial role in structured visualization. However, most existing studies are confined to a narrow range of task formulations and language support, constraining their applicability to diverse diagram types. In this work, we propose OmniDiagram, a unified framework that incorporates diverse diagram code languages and task definitions. To address the challenge of aligning code logic with visual fidelity in Reinforcement Learning (RL), we introduce a novel visual feedback strategy named Visual Interrogation Verifies All (Viva). Unlike brittle syntax-based rules or pixel-level matching, Viva rewards the visual structure of rendered diagrams through a generative approach. Specifically, Viva actively generates targeted visual inquiries to scrutinize diagram visual fidelity and provides fine-grained feedback for optimization. This mechanism facilitates a self-evolving training process, effectively obviating the need for manually annotated ground truth code. Furthermore, we construct M3^2Diagram, the first large-scale diagram code generation dataset, containing over 196k high-quality instances. Experimental results confirm that the combination of SFT and our Viva-based RL allows OmniDiagram to establish a new state-of-the-art (SOTA) across diagram code generation benchmarks.

Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, Suyi, Congqing He, Yike Tan, Yu-N Cheah

Aspect Sentiment Quad Prediction (ASQP) is a fundamental yet challenging task in fine-grained sentiment analysis, particularly when aspects or opinions are implicit. Existing methods often lack explainability and generalization, making it difficult to justify inference decisions and to detect implicit sentiment across domains and varied expression patterns. To address these limitations, we propose Tree-CoT-RT, an explainable multi-path tree-guided chain-of-thought and reinforcement learning framework specifically designed for ASQP. The core idea is to use sentiment tree structures to design type-specific reasoning templates that guide LLMs in generating explainable chains, including both final sentiment quadruples and intermediate inference steps for transparent implicit reasoning. However, the generated reasoning chains often vary in quality and may contain logical inconsistencies. To mitigate this, we introduce a reinforcement learning strategy with a rule-based reward function to generate high-quality reasoning traces, which are then used to fine-tune the LLM and enable controlled sampling. Experiments on benchmark datasets demonstrate that Tree-CoT-RT substantially outperforms strong baselines, particularly in scenarios involving implicit sentiment analysis.

Yuyin Zhou, Yongmei Liu

Recent advances in large language models (LLMs) have spurred interest in neuro-symbolic methods for logical reasoning based on auto-formalization, where LLMs first formalize problems into symbolic programs, for solvers to perform reasoning over.However, existing auto-formalization methods remain prone to both syntactic and semantic errors.Specifically, the absence of a program-level semantic verification mechanism leaves semantic errors largely unaddressed.In this paper, we propose a novel approach to semantic error correction via program preference retrieval-augmented generation (RAG).First, we conduct an in-depth analysis of semantic error patterns, and then automatically synthesize SemanticPref, a program preference dataset to model these patterns. Using the dataset as the knowledge base, we introduce PrefRAG, a general RAG framework for refinement in auto-formalization, which enables LLMs to detect and repair syntactic and semantic errors[<https://github.com/sysulic/PrefRAG>].Extensive evaluations across both in-distribution (ID) benchmarks (i.e., AR-LSAT and FOLIO) and out-of-distribution (OOD) datasets show that PrefRAG consistently outperforms strong baselines, achieving an average accuracy improvement of 2.39% on ID and 6.23% on OOD datasets.

Wenhan Han, Yifan Zhang, Zhixun Chen, Binbinliu, Mykola Pechenizkiy, Meng Fang, Yin Zheng

Multilingual large language models (LLMs) are advancing rapidly, with new models frequently claiming support for an increasing number of languages. However, existing evaluation datasets are limited and lack cross-lingual alignment, leaving assessments of multilingual capabilities fragmented in both language and skill coverage. To address this, we introduce MuBench, a benchmark covering 61 languages with 3.9M samples and evaluating a broad range of capabilities. We evaluate several state-of-the-art multilingual LLMs and find notable gaps between claimed and actual language coverage, particularly a persistent performance disparity between English and low-resource languages. Leveraging MuBench’s alignment, we propose Multilingual Consistency (MLC) as a complementary metric to accuracy for analyzing performance bottlenecks and guiding model improvement. MuBench provides flexible evaluation formats, including mixed-language testing. Experimental results show that increasing model size does not improve its ability to handle mixed-language contexts. We recruited human experts to evaluate translation quality and cultural sensitivity for 34k samples across 17 languages, and combined these assessments with an LLM-as-a-Judge approach to ensure overall data quality in low resource languages.

Zhenyu He, Qingping Yang, Wei Shen, Xiaojian Zhong, Kechi Zhang, Chenxin An, Wenlei Shi, Tianle Cai, Di He, Jiaze Chen 等

Automated software engineering, particularly resolving real-world issues on benchmarks like SWE-bench, remains a significant challenge for Large Language Models (LLMs). To address this, we introduce SWE-Swiss, a two-phase training recipe that systematically develops these capabilities. Our approach first decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. In the first phase, we perform multi-task Supervised Fine-Tuning (SFT) on three new, meticulously curated datasets to build a versatile foundation. The second phase applies targeted Reinforcement Learning (RL), using direct feedback from test execution to boost the critical skill of code repair. The resulting model, SWE-Swiss-32B, establishes a new state-of-the-art for open-source models in its size class, achieving a 60.2% score on the SWE-bench Verified benchmark and placing it in the same top-tier performance bracket as much larger models. Finally, we show that despite its specialized training, SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. To accelerate research in the community, we are open-sourcing the models and our complete training datasets.

Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia

The assessment of jailbreak attacks against large language models currently suffers from inconsistent evaluation criteria and methods, leading to unreliable estimates of attack success rates. We propose JailMeter, an evidence-based evaluation framework designed to more faithfully measure jailbreak effectiveness. Inspired by the Information Bottleneck theory, JailMeter applies dual-feedback optimization to filter jailbreak noise from model responses while preserving content relevant to the original malicious question. This process produces concise evidence for a rigorous assessment under which an attack is validated only when the response captures the malicious intent and delivers a complete answer, thereby signaling a substantive bypass of model safety alignment. We evaluate JailMeter on JailMeter-Eva, a challenging benchmark containing 330 human-labeled, non-rejected jailbreak instances. JailMeter achieves an accuracy of 97.27%, substantially outperforming existing evaluation methods. To support large-scale evaluation, we further distill JailMeter into a small language model, JailMeterSLM, which maintains comparable reliability with significantly reduced computational costs. Code and dataset are available at https://github.com/Magi2B0y/JailMeter.

Xinping Zhao, Shouzheng Huang, Yan Zhong, Xinshuo Hu, Meishan Zhang, Baotian Hu, Min Zhang

Retrieval-Augmented Generation (RAG) effectively improves the accuracy of Large Language Models (LLMs). However, retrieval noises significantly undermine the quality of LLMs’ generation, necessitating the development of denoising mechanisms. Previous works extract evidence straightforwardly without deep thinking, which may risk filtering out key clues and struggle with generalization. To this end, we propose EviOmni, which learns to extract rational evidence via reasoning first and then extracting. Specifically, EviOmni integrates evidence reasoning and evidence extraction into one unified trajectory, followed by knowledge token masking to avoid information leakage, optimized via on-policy reinforcement learning with verifiable rewards in terms of answer, length, and format. Extensive experiments on five benchmark datasets show the superiority of EviOmni, which provides compact and high-quality evidence, enhances the accuracy of downstream tasks, and supports both traditional and agentic RAG systems.

Keyang Zhong, Junlin Xie, Hefeng Wu, Haofeng Li, Guanbin Li

Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multi-player game settings with imperfect and deceptive information. In this paper, we pick up a representative multi-player task, Murder Mystery Games, which require to infer hidden truths based on partial clues provided by the roles of different intentions. To address this challenge, we propose a collaborative multi-agent framework for evaluating and synthesizing high-quality, role-driven multi-player game scripts, enabling fine-grained interaction patterns tailored to character identities (i.e., murderer vs. innocent). Our system generates rich multimodal contexts—including character backstories, visual/textual clues, and multi-hop reasoning chains—through coordinated agent interactions. We design a two-stage agent-monitored training strategy to enhance the reasoning ability of VLM: (1) Chain-of-Thought based fine-tuning on curated and synthetic datasets that model uncertainty and deception; (2) GRPO-based Reinforcement Learning with agent-monitored reward shaping, encouraging the model to develop character-specific reasoning behaviors and effective multi-modal multi-hop inference. Extensive experiments demonstrate that our method significantly boosts the performance of VLM in narrative reasoning, hidden fact extraction, and deception-resilient understanding. Our contributions offer a scalable solution for training and evaluating VLMs under uncertain, adversarial, and socially complex conditions, laying the groundwork for future benchmarks in multimodal multi-hop reasoning under imperfect information.