论文检索

Zhigan Zhang, Jie Sui

The rapid spread of fake news on digital platforms presents significant societal challenges, demanding detection methods capable of addressing intricate manipulation strategies. Existing methods predominantly rely on monolithic verification approaches, which fail to decompose the complex blend of factual inaccuracies, logical fallacies, and propaganda techniques in modern misinformation. To address this gap, we propose PARD, a Protocol-Adaptive Role-Specific multi-agent framework that decomposes verification into factual, logical, and contextual dimensions. PARD dynamically selects the optimal interaction protocol from a library that includes Round-Robin Discussion, Point-Counterpoint Dialogue, and Cross-Examination to tailor the reasoning process for more effective detection. We evaluate PARD with LIAR-RAW+, an extended version of the LIAR-RAW dataset enriched with fine-grained factual, logical, and contextual annotations. Experimental results demonstrate that PARD consistently outperforms baseline methods in both predictive accuracy and explanatory quality, supported by its efficient dynamic governance mechanism

Xingyuan Li, Mengyue Wu

Detecting medical conditions from speech acoustics is fundamentally a weakly-supervised learning problem: a single, often noisy, session-level label must be linked to nuanced patterns within a long, complex audio recording. This task is further hampered by severe data scarcity and the subjective nature of clinical annotations. While semi-supervised learning (SSL) offers a viable path to leverage unlabeled data, existingaudio methods often fail to address the core challenge that pathological traits are not uniformly expressed in a patient’s speech. We propose a novel, audio-only SSL framework that explicitly models this hierarchy by jointly learning from frame-level, segment-level, and session-level representations within unsegmented clinical dialogues. Our end-to-end approach dynamically aggregates these multi-granularity features and generates high-quality pseudo-labels to efficiently utilize unlabeled data. Extensive experiments show the framework is model-agnostic, robust across languages and conditions, and highly data-efficient—achieving, for instance, 90% of fully-supervised performance using only 11 labeled samples. This work provides a principled approach to learning from weak, far-end supervision in medical speech analysis.The code is available at https://github.com/fispresent/semi_pathological.

Yiming Lei, Yize Fan, Zeming Liu, Jiaji Dong, Hui Qiu, Haitao Leng, Qingjie Liu, Kehai Chen, Tingting Gao, Yunhong Wang

Recent advancements in Multimodal Large Language Models (MLLMs) have achieved significant success in understanding static pre-recorded video scenarios (e.g., event-centric or narrative-driven content). However, existing MLLMs are largely trained on datasets restricted to static content due to the scarcity of high-quality interleaved data, causing them to struggle with dynamic interactions. Distinct from pre-recorded videos, live streaming is characterized by high-density, interleaved multimodal turns, where viewer comments (danmaku) are tightly coupled with real-time audio-visual evidence and evolving dialogue context. In such settings, purely textual annotations fail to capture fine-grained visual and temporal dependencies. To bridge this gap, we introduce **Live-Aid**, the first large-scale interleaved live interaction Chinese dataset with **human-annotated**, temporally aligned video responses, spanning over **1,100 hours** and 80,037 dialogue turns across 8,053 video sessions. Building on this, we leverage these high-quality annotations within a novel multi-agent pipeline to construct evaluation tasks targeting core capabilities of live interactions. Extensive evaluations of strong Video-LLMs and Omni-LLMs reveal critical limitations in interleaved multi-turn interactions requiring temporal reasoning, highlighting the value of **Live-Aid** in advancing interleaved multimodal reasoning and dynamic audio-visual dependencies.

Kai Wang, Haoyang You, Yang Zhang, Zhongjie Wang

A core challenge for faithful LLM role-playing is sustaining consistent characterization throughout long, open-ended dialogues, as models frequently fail to recall and accurately apply their designated persona knowledge without explicit cues. To tackle this, we propose the Memory-Driven Role-Playing paradigm. Inspired by Stanislavski’s "emotional memory” acting theory, this paradigm frames persona knowledge as the LLM’s internal memory store, requiring retrieval and application based solely on dialogue context, thereby providing a rigorous test of depth and autonomous use of knowledge. Centered on this paradigm, we contribute: (1) MREval, a fine-grained evaluation framework assessing four memory-driven abilities—Anchoring, Selecting, Bounding, and Enacting; (2) MRPrompt, a prompting architecture that guides structured memory retrieval and response generation; and (3) MRBench, a bilingual (Chinese/English) benchmark for fine-grained diagnosis. The novel paradigm provides a comprehensive diagnostic for four-stage role-playing abilities across 12 LLMs. Crucially, experiments show that MRPrompt allows small models (e.g., Qwen3-8B) to match the performance of much larger closed-source LLMs (e.g., Qwen3-Max and GLM-4.7), and confirm that upstream memory gains directly enhance downstream response quality, validating the staged theoretical foundation.

Xiaoyong Mei, Tingting Zuo, Da Chen, Guangyu Hu, Xiangyu Wen, Chao Duan, Mingyan Zhang, Fudan Zheng

Multi-role dialogue summarization requires modeling complex interactions among multiple speakers while preserving role-specific information and factual consistency. However, most existing methods optimize for automatic metrics such as ROUGE and BERTScore, which favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences. We propose a novel framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization. Our method first distills structured reasoning traces (e.g., step-by-step inferences and intermediate reflections) from a large teacher model and uses them as auxiliary supervision to initialize a reasoning-aware summarizer via staged supervised fine-tuning. It then applies GRPO with a dual-principle reward that blends metric-based signals with human-aligned criteria targeting key information coverage, implicit inference, factual faithfulness, and conciseness. Experiments on multilingual multi-role dialogue benchmarks show that our method matches strong baselines on ROUGE and BERTScore. Specifically, results on CSDS confirm the framework’s stability in semantic consistency, while in-depth analysis on SAMSum demonstrates clear gains in factual faithfulness and model-based preference alignment. These findings underscore the value of reasoning-aware and preference-aware training for reliable dialogue summarization. Code will be made accessible upon acceptance, checkpoints and datasets are now available at https://huggingface.co/NebulaPixel.

Zhuoqun Li, Zhaopei Huang, Wenxuan Wang, Qin Jin

Understanding multimodal user personas in long-term dialogues is essential for building personalized and human-like dialogue systems. However, existing datasets suffer from limited persona diversity and static, overly simplified settings, making them insufficient for capturing the complexity of real-world interactions. To address these limitations, we introduce LongMP-Bench, a benchmark designed to evaluate the capabilities of models in understanding evolving user personas within long-term multimodal dialogues. We present a multi-step, scalable data construction pipeline that generates long-term interaction records centered around multimodal personas, followed by human refinement for quality assurance. The resulting dataset contains long conversations from 150 users, each exhibiting visual consistency and dynamic persona development over time. Built on this dataset, we define a suite of tasks to comprehensively assess models’ ability to track persona evolution, integrate visual and textual inputs, and apply persona understanding in realistic dialogue scenarios. Extensive experiments on LongMP-Bench highlight the substantial challenges in multimodal persona understanding, especially in tracking persona shifts and leveraging multimodal context effectively. We will release our benchmark and code to facilitate future research in multimodal and personalized dialogue systems.

Qian Chen, Mengqiang Hu, Xin Guo

Dialogue summarization (DS) plays a vital role in improving customer service efficiency by automatically generating concise summaries from lengthy multi-turn dialogues. However, existing studies largely overlook the fine-grained sentiment dynamics expressed by customers, and most DS datasets lack detailed sentiment annotations. These limitations hinder both accurate service quality assessment and the development of sentiment-aware summarization models. To address these challenges, we propose a three-stage approach to building an aspect-aware sentiment dataset, comprising: (1) aspect-anchored dialogue rewriting, (2) dialogue-anchored explainable label generation, and (3) label-dialogue integrated summarization. Building upon this scheme, we construct FOCUS, a \textbf{F}ine-grained customer-\textbf{O}riented \textbf{C}hinese dialog\textbf{U}e \textbf{S}ummarization dataset. FOCUS is the first Chinese dataset with 12,948 dialogues annotated for multi-level aspects, sentiment polarity, opinion content, emotions, as well as customer-oriented formatted and free-style sentiment summaries. To demonstrate the challenges and utility of FOCUS, we benchmark a range of summarization models on FOCUS and observe that current methods often exhibit misalignment between aspects and sentiments. Meanwhile, we find that a Chain-of-Thought approach can enhance faithfulness and interpretability, highlighting promising directions for future research on this dataset. FOCUS serves as a valuable resource to advance research in sentiment-aware DS and related tasks.

Ragib Amin Nihal, Rui Wen, Kazuhiro Nakadai, Jun Sakuma

Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints gradually. These attacks target different harm categories through distinct conversational approaches. Existing multi-turn methods often rely on heuristic or ad hoc exploration strategies, providing limited insight into underlying model weaknesses. The relationship between conversation patterns and model vulnerabilities across harm categories remains poorly understood. We propose Pattern Enhanced Chain of Attack (PE-CoA), a framework of five conversation patterns to construct multi-turn jailbreaks through natural dialogue. Evaluating PE-CoA on twelve LLMs spanning ten harm categories, we achieve state-of-the-art performance, uncovering pattern-specific vulnerabilities and LLM behavioral characteristics: models exhibit distinct weakness profiles, defense to one pattern does not generalize to others, and model families share similar failure modes. These findings highlight limitations of safety training and indicate the need for pattern-aware defenses. Code available on: https://github.com/Ragib-Amin-Nihal/PE-CoA

Zhiheng Han, Yao Zhang, Jun Wang, Zhenglu Yang

Medical multimodal large language models are increasingly deployed in high-stakes clinical settings, yet current safety evaluations largely overlook a critical failure mode: covert semantic drift that accumulates across clinically plausible multi-turn interactions. Such drift can lead models to gradually exaggerate or conceal critical medical findings without triggering explicit safety mechanisms. We propose MSIA (Medical Semantic Infiltration Attack), a framework for modeling and inducing multi-turn medical semantic jailbreaks in clinical dialogues. MSIA enables the controlled optimization of cumulative semantic drift under stealth constraints through adaptive strategy selection and closed-loop reward feedback grounded in medical evidence. Experiments on chest X-ray–based multimodal medical dialogues show that MSIA consistently outperforms existing jailbreak methods across GPT-4o, Claude, and Gemini, achieving an average attack success rate of 76.67%. These results expose substantial and previously underestimated vulnerabilities of medical LLMs in realistic multi-turn clinical interactions. Code is available here: https://github.com/HeYamo/MSIA.

Rui Li, Junfeng Liu, Xiangwen Kong, Zhifang Sui

Large Language Models (LLMs) as judges across various scenarios such as assessing model responses is becoming an increasingly accepted paradigm. However, existing judgment approaches often rely on trained judgers using fixed preference data, which tend to overlook diverse user preferences and struggle to adapt to real-world human-AI dialogue scenarios. To address these limitations, we propose SenseJudge, a customizable judgment framework driven by human preferences and SenseBench, a diverse and challenging instruction following benchmark derived from real-world multi-turn interactions. We applied the automatic judgment framework and benchmark to two tasks: 1) LLMs as personalized judges, and 2) model ranking. We conducted extensive experiments, and the results demonstrate that the SenseJudge framework surpasses other judgment methods and models in the LLMs-as-personalized-judges task and achieves model ranking that aligns with real human sense. Additionally, we conducted analyses on position bias and consistency, alongside ablation studies, which affirmed the robustness of SenseJudge.

Ruirui Wang, Haoran Zhang, Tian Lan, Zehua Duo, Jiang Li, Guanglai Gao, Xiangdong Su

Theory of Mind (ToM) is widely regarded as central to effective persuasion, yet existing evaluations often fail to capture the infer–apply loop that arises in real-world dialogue. We introduce Theory-of-Mind-Guided Elaboration-Likelihood Persuasion (ToMELP), a benchmark that jointly conditions on the audience persona p and the Elaboration Likelihood Model (ELM) route r \in \{\text{central}, \text{peripheral}\} within persuasive conversations. The benchmark tests whether large language models can perform ToM inference over multi-turn interactions and leverage these inferences for controllable persuasive generation. ToMELP provides a structured interface with evidence annotations, enabling automated evaluation of persuasive effectiveness, route alignment/deviation, evidence quality under the central route, and robustness to perturbations.

Yanyan Luo, Xue Han, Chunxu Zhao, Ruiqiao Bai, Yaxing Zhang, Qian Hu, Lijun Mei, Junlan Feng

While LLMs enable personalized chatbots, their effectiveness in child-centered personalization remains unclear, as systematic evaluation of child-specific preferences is still lacking. To address this gap, we introduce ChildEval, a benchmark for evaluating LLMs’ ability to infer and follow child-centered preferences in long-context conversations. ChildEval contains 29K synthesized persona profiles of children aged 3–6, providing relatively static background information. Each persona is associated with a child preference—which may align with, conflict with, or be independent of the persona—expressed either explicitly in a single sentence or implicitly through 6–10 turn dialogues. Explicit and implicit preferences are designed to reflect the same underlying preference but differ in expression, capturing dynamic aspects of preference expression rather than changes in the static persona. The benchmark spans five top-level and fourteen sub-level categories covering children’s daily lives and development. We further propose fine-grained, child-centric evaluation protocols to systematically assess open-source LLMs. Experimental results demonstrate how different personalized representations affect LLM responses and suggest that finetuning on ChildEval can enhance child-centered performance. Our code and dataset are available at https://github.com/ziyanluo/ChildEval.

Chunyang Gao, Yi Huang, Jingyu Yao, Xiaoting Wu, Junlan Feng

Despite the remarkable evolution of Large Language Models (LLMs) from simple assistants to versatile agents, effective personalization remains a significant challenge. Existing approaches often treat user preferences as static or merely time-varying traits, overlooking the dynamic nature of human behavior: preferences can shift, and even conflict, depending on context. To address this limitation, we propose a fine-grained taxonomy to differentiate between stable preferences, which are context-agnostic, and situational preferences, which are context-dependent. Building on this taxonomy, we introduce S2Pref, a new dataset of 10k meticulously curated entries. Each entry is grounded in a multi-turn dialogue that implicitly manifests either a stable or a situational preference, as defined by our hierarchical taxonomy. We further design three complementary evaluation tasks to benchmark LLMs on their ability to prioritize contextual signals, proactively resolve ambiguity, and efficiently infer user preferences. Our dataset and diagnostic tasks provide a practical testbed for advancing dynamic, context-aware personalization in conversational agents.

Yuxiao Ye, Yiming Zhang, Yiran Rex Ma, Huiyuan Xie, Huining Zhu, Zhiyuan Liu

Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. Recent work on LLM-based MASs has mainly focused on architecture design, such as role assignment and workflow orchestration. In contrast, this paper targets the interaction process itself, aiming to improve agents’ communication efficiency by helping them convey their intended meaning more effectively through language. To this end, we propose LinguaGame, a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation. Our approach models dialogue as a signalling game over communicative intents and strategies, solved with a training-free equilibrium approximation algorithm for inference-time decision adjustment. Unlike prior game-theoretic MASs, whose game designs are often tightly coupled with task-specific objectives, our framework relies on linguistically informed reasoning with minimal task-specific coupling. Specifically, it treats dialogue as intentional and strategic communication, requiring agents to infer what others aim to achieve (intents) and how they pursue those goals (strategies). We evaluate our framework in simulated courtroom proceedings and debates, with human expert assessments showing significant gains in communication efficiency. We release our code and data on GitHub.

Yingyu Shan, Zeming Liu, Silin Li, Boao Qian, Jiashu Yao, Yuhang Guo, Haifeng Wang

Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities. However, current assistants overlook the progressive omission that occurs in human dialogue as shared context accumulates, leading to more elliptical expressions for efficient communication. Thus, current assistants still struggle to interpret such elliptical expressions accurately, which limits their effectiveness in real-world applications. In practical smart home scenarios, assistants face two major challenges caused by elliptical commands: (1) referential ambiguity caused by different environmental expectations among multiple users; and (2) intention ambiguity resulting from user preferences that evolve over time or change with the environment. To address these challenges, we introduce PEC-Home, the first simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes. Extensive experiments on various LLMs, including GPT-4o, show that existing home assistants struggle to execute user-intended operations based solely on elliptical commands. Even when equipped with tools for storing and retrieving user dialogue history, execution accuracy remains below that achieved with complete commands. Our code and dataset will be publicly available.

Shaz Furniturewala, Gerard Christopher Yeo, Kokil Jaidka

Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users’ learning and engagement are understudied. We analyze the linguistic and interactional features from both LLM and participant chats across 397 human–LLM conversations about socio-political issues to identify the mechanisms and conditions under which LLM explanations shape changes in political knowledge and confidence. Mediation analyses reveal that LLM explanatory richness partially supports confidence by fostering users’ reflective insight, whereas its effect on knowledge gain operates entirely through users’ cognitive engagement. Moderation analyses show that these effects are highly conditional and vary by political efficacy. Confidence gains depend on how high-efficacy users experience and resolve uncertainty. Knowledge gains depend on high-efficacy users’ ability to leverage extended interaction, with longer conversations benefiting primarily reflective users. In summary, we find that learning from LLMs is an interactional achievement, not a uniform outcome of better explanations. The findings underscore the importance of aligning LLM explanatory behavior with users’ engagement states to support effective learning in designing Human-AI interactive systems.

Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai 等

Large Language Models (LLMs) are exhibiting emergent human-like abilities and are envisioned as the tool for simulating an individual’s communication patterns, behaviors, and personality traits. However, current evaluations of LLM-based persona simulation remain limited: most rely on synthetic dialogues and lack fine-grained analysis of the capability for persona simulation. To address these limitations, we introduce TwinVoice, a comprehensive benchmark for assessing persona simulation across diverse real-world contexts. TwinVoice encompasses three dimensions: Social Persona (public social interactions), Interpersonal Persona (private dialogues), and Narrative Persona (role-based expression). It further decomposes the evaluation into six fundamental capabilities, including opinion consistency, memory recall, logical reasoning, lexical fidelity, persona tone, and syntactic style. Experimental results reveal that while advanced models achieve moderate accuracy in persona simulation, they still fall short of capabilities such as syntactic style and memory recall. Our data, code, and evaluation results are available.

Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, Tong Ruan

With the remarkable performance of large language models (LLMs) in medicine, particularly their ability to support clinical decision-making in medical dialogues, a key limitation remains: the static reasoning patterns derived from human expert experience are often inadequate for the dynamic and diverse nature of real-world multi-turn conversations. While recent large reasoning models (such as R1) enable deeper and more complex thought processes to address such challenges, they also introduce significant redundancy. Meanwhile, recent studies on reusing atomic thoughts demonstrate a practical pathway toward dynamic and precise reasoning in general domains. In this paper, we investigate the role of atomic thought-based experience in medical dialogue tasks. First, we collect human expert clinical experience. Then, we propose a novel distillation framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. Based on this framework, we construct training data from ReMeDi and fine-tune student models, which demonstrate enhanced performance in both static and interactive medical dialogue scenarios. Furthermore, we examine the impact of experience across various models, datasets, and scenarios. Crucially, transferring this experience empowers weaker models to generate high-quality reasoning data, matching the annotation capabilities of stronger LLMs while significantly reducing costs. The code is available in this repository https://github.com/VioletAmethystLunar/Atomic-Thoughts-Medical-Dialogue.

Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, Yanmin Qian

Emotional Text-to-Speech aims to synthesize speech with human-like naturalness and expressiveness. However, existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. Based on cognitive appraisal theories, we argue that emotional expression is not generated in isolation but is deeply influenced by speaker’s Personal Experience and the conversational Context.To overcome the information bottleneck inherent in traditional annotations, we present Emotional-Context-Speech, a large-scale, context-aware speech corpus derived from multi-speaker audiobooks. This dataset provides not only transcriptions but also dialogue context, personal experience, open-vocabulary emotion labels, and paralinguistic descriptions.Experimental results demonstrate that TTS model trained using additional context and experience descriptions as inputs, called Emotional-Context-TTS, significantly outperforms existing methods in terms of emotional expression accuracy and naturalness.

Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong

Large Language Model (LLM) agents have demonstrated considerable potential for social simulation, yet struggle to accurately model individual value systems. Most existing methods mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems. The value systems of LLMs are typically assessed using static multiple-choice questions, which fail to evaluate the value orientation in real-world dialogue interactions. To address these issues, we propose ExpertIVS, a framework employing 14 Sociological Expert Agents to interpret World Values Survey (WVS) responses through structured professional perspectives, rather than direct responses concatenation. These expert agents perform deep semantic reconstruction to generate robust and internally consistent individual profiles. To evaluate the consistency between LLMs and individual value systems during dynamic interactions, we further introduce a multi-agent debate mechanism. Extensive experiments across 480 individuals from 12 countries demonstrate that ExpertIVS achieves 90.78% value restoration fidelity and significantly outperforms baselines in value generalization (+5.3%). Moreover, ExpertIVS exhibits strong personality discriminability and behavioral consistency, enabling a shift from mere response concatenation to genuine sociological role-playing.