Task-oriented dialogue (TOD) systems are vital for facilitating complex, goal-directed interactions across sectors like customer support and online retail. However, they face persistent limitations: labor-intensive manual metadata tuning and sparse reinforcement learning (RL) rewards that fail to diagnose invocation errors. To address this, we propose ToolDNA, a dynamic adaptation framework enabling autonomous co-evolution of policy networks and tool metadata via RL, anchored by two synergistic loops. An RL loop optimizes policies by generating rollout trajectories (reasoning, actions, descriptive updates) from user inputs, with multi-dimensional rewards refining invocations. A tool metadata loop—coordinated by a dedicated Tool Manager—evolves metadata through policy-generated candidates during rollouts and Feedback LLM-derived refinements from historical data. These mutually reinforcing loops close traditional reward gaps, forming a closed-loop trial-error-reflection cycle for self-improvement. Extensive experiments on a real-world dataset of 3,100 customer service dialogues confirm ToolDNA’s superiority, with notable gains over baselines: it achieves +11% problem resolution and +54% accuracy over commercial LLMs with prompt engineering; +25%/+35% over supervised fine-tuning; and +15%/+15% over traditional RL baseline. Linguistic analysis corroborates evolved metadata retain semantic intent while enhancing parseability. Case studies in two typical contexts, i.e., car inventory search and loan calculation, further validates its ability to resolve critical ambiguities. ToolDNA pioneers scalable self-improvement for robust, deployable tool-augmented agents with minimal human oversight. We release our code to facilitate future research.
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Multi-turn jailbreaking is a critical approach for evaluating the safety of large language models (LLMs). However, existing methods largely rely on heuristic strategies or trained attack agents, lacking a unified state-action formulation and systematic search over strategy compositions, and often struggling to balance attack success rate with query overhead. We propose RAMP, which formulates multi-turn jailbreaking as a risk-aware PDDL planning problem. Specifically, we characterize dialogue safety using predicate-based states, abstract common jailbreak strategies as high-level actions, and introduce a closed-loop framework that iteratively plans and executes each turn via a Judge, a Transitioner, and a Planner. Experimental results show that RAMP achieves consistently strong attack performance across both open-source and closed-source target models, while remaining effective under stricter turn budgets and yielding a favorable efficiency–effectiveness trade-off. Ablation studies, interpretability analyses, and extended experiments further show that multi-step planning, clue accumulation, and consistent findings across evaluator settings are key factors underlying these gains.
Large language models (LLMs) are widely used for text understanding and generation, with increasing deployment in applications involving sensitive user inputs. This raises significant privacy concerns, motivating the adoption of differential privacy (DP) to protect prompts during LLM inference. However, most existing DP methods assume single-turn interactions, whereas real-world usage often relies on multi-turn dialogue. Consequently, these single-turn-based methods break down in multi-turn settings, where recurring tokens repeatedly consume the privacy budget under DP, leading to accumulated privacy loss and degraded cross-turn semantic coherence.To address these challenges, we propose DP^3, a differentially private prompt perturbation framework for multi-turn LLM inference. DP^3 constructs a perturbation mapping table to reuse perturbations for recurring tokens, reducing redundant privacy costs. It also defines a context-aware utility function that combines embedding distance with attention-based contextual representations to maintain semantic consistency across turns. Additionally, DP^3 introduces a two-stage bucketed exponential mechanism to manage long-tail phenomena in large candidate spaces.Experimental results on multi-turn dialogue tasks demonstrate that DP^3 offers a better privacy-utility trade-off and stronger resistance to inference attacks compared to existing methods. Our code is publicly available at https://github.com/XidianNSS/DP3.
Safety-Aware Dialogue System for Postoperative Oral Cancer Care with Structured Clarification and a Clinically Curated Dataset
PDF ↗Clinical dialogue systems are increasingly vital for patient education and follow-up care; however, effective responses often depend on the clinical context that patients often fail to provide in detail. Responding directly to vague messages can therefore lead to generic or clinically misaligned advice, a challenge that is particularly pronounced in post-op oral cancer (OC) care due to speech impairment and functional limitations. Moreover, post-op OC patients often experience psychological distress, making safety-aware language more likely to arise in dialogue. Dialogue systems in this setting must therefore address both clarifying missing clinical context and ensuring psychological safety. We propose a safety-aware dialogue system that applies information-gain guided clarification before RAG-based response generation and screens user utterances for emotional distress and suicidal ideation. Expert evaluations show that the proposed system improves the quality and clinical appropriateness of generated responses relative to strong baselines, while the safety module closely aligns with expert judgments on clinically concerning utterances. Furthermore, we release a clinically curated Chinese post-op OC QA dataset with expert-validated annotations, which we use throughout our experiments.
Large language model (LLM)-based agents have been successfully deployed in many tool-augmented settings, but their scalability is fundamentally constrained by context length. Existing context-folding methods mitigate this issue by summarizing past interactions, yet they are typically designed for single-query or single-intent scenarios. In more realistic user-centric dialogues, we identify two major failure modes: (i) they irreversibly discard fine-grained constraints and intermediate facts that are crucial for later decisions, and (ii) their summaries fail to track evolving user intent, leading to omissions and erroneous actions. To address these limitations, we propose U-Fold, a dynamic context-folding framework tailored to user-centric tasks. U-Fold retains the full user–agent dialogue and tool-call history but, at each turn, uses two core components to produce an intent-aware, evolving dialogue summary and a compact, task-relevant tool log. Extensive experiments on \tau-bench, \tau^2-bench, VitaBench, and harder context-inflated settings show that U-Fold consistently outperforms ReAct (achieving a 71.4% win rate in long-context settings) and prior folding baselines (with improvements of up to 27.0%), particularly on long, noisy, multi-turn tasks. Our study demonstrates that U-Fold is a promising step toward transferring context-management techniques from single-query benchmarks to realistic user-centric applications.
Dunhuang art, a cornerstone of global heritage, demands fine-grained visual perception anchored by specialized cultural knowledge. Given the strong performance of multimodal large language models (MLLMs) on generic multimodal benchmarks, to what extent can they understand artifacts from Dunhuang art that are grounded in cultural context? To this end, we construct Dunhuang-Bench, a large-scale benchmark comprising 486 images and 22,970 QA pairs. It incorporates diverse task formats to evaluate MLLMs’ cultural understanding: Question Answering with Text Description, Multi-turn Dialogue, and Question Answering with Choices. Guided by Panofsky’s theory of iconology, we design two tasks including visual perception and knowledge reasoning for the evaluation of content understanding. In addition, we follow the theory of formal analytic tradition to design another task of artistic appreciation in our Dunhuang-Bench. Extensive evaluations of 20 mainstream MLLMs on Dunhuang-Bench reveal a consistent performance drop from perception and appreciation to reasoning. Moreover, CoT and few-shot prompting show marginal or negative impact, highlighting the limits of prompting-based improvements. Dunhuang-Bench thus provides a challenging benchmark for advancing multimodal cultural understanding. Data and code will be publicly available.
Empathy is essential for fostering natural interactions in spoken dialogue systems, as it enables machines to recognize the emotional tone of human speech and deliver empathetic responses. Recent research has made significant progress in developing empathetic spoken chatbots based on large language models (LLMs). However, several challenges still exist when training such models, including reliance on costly empathetic speech instruction data and a lack of emotional expressiveness in the generated speech. Finetuning LLM with cross-modal empathetic instruction data may also lead to catastrophic forgetting and a degradation of its general capability. To address these challenges, we propose FreezeEmpath, an end-to-end empathetic spoken chatbot trained in a simple and efficient manner. The entire training process relies solely on existing speech instruction data and speech emotion recognition (SER) data, while keeping the LLM’s parameters frozen. Experiments demonstrate that FreezeEmpath is able to generate emotionally expressive speech and outperforms other empathetic models in empathetic dialogue, SER, and SpokenQA tasks, demonstrating the effectiveness of our training strategy.
Beyond Semantic Similarity: Appraisal-Guided Chain-of-Thought Reasoning and Retrieval for Multimodal Emotional Support Conversations
PDF ↗Emotional support conversation systems strive to emulate the empathetic depth of human therapists, yet current approaches often fail due to the "Cognitive Gap"—the inability to discern the latent psychological evaluations driving a user’s distress. Existing retrieval-augmented generation paradigms exacerbate this by relying on semantic similarity, frequently retrieving historical dialogues that are surface analogous but therapeutically incongruent. To bridge this gap, we introduce Appraisal-Guided Chain-of-Thought Reasoning & Retrieval (AG-CTR²) for better emotional support. Specifically, we bootstrap the MLLM to generate appraisal-guided reasoning chains and apply a dual-signal verification mechanism using ground-truth emotion labels and a teacher model to verify and correct them. Under such instance-level guidance, we finetune the MLLM to internalize such reasoning capability. At inference, the model utilizes its generated appraisal chain as a structured query to help retrieve historical therapeutic responses based on psychological situation similarity rather than content surface proximity. Extensive experiments and analyses on two ESC benchmarks demonstrate that AG-CTR² significantly outperforms state-of-the-art baselines.
Retrieval-Augmented Generation (RAG) systems are widely used to mitigate the stateless nature of Large Language Models (LLMs) in long-term and personalized interactions by incorporating external memory. However, existing approaches often prioritize memory organization, such as knowledge graphs, while overlooking a critical semantic gap between implicit, intent-driven queries and explicit, narrative-based memories. To bridge this gap, we propose QueryLink, a novel framework that leverages Query-Memory Alignment to project both queries and memories into a shared semantic space. It significantly boosts recall by facilitating multi-grained retrieval of semantically relevant information. To further enhance memory retrieval, we leverage Coherent Memory Chunking, a mechanism that processes memories in multi-turn dialogue units, preserving semantic integrity, rather than relying on fixed-size segments. Extensive experiments on the LoCoMo and LongMemEval benchmark demonstrate that QueryLink significantly outperforms SOTA methods, achieving at least a 7% improvement in reasoning accuracy (measured by LLM). Additionally, QueryLink can be integrated as a plug-and-play component to boost existing vector-based systems like A-MEM, leading to improvements of over 6% in both F1 and B1 scores.The code is available at https://github.com/Dontplay0112/querylink.
FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval
PDF ↗Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing data synthesis methods typically rely on a reverse synthesis paradigm, generating user queries from pre-sampled tools. This approach inevitably introduces artificial explicitness, yielding queries that fail to capture the implicit, event-driven nature of real-world needs. Moreover, its reliance on static tool sets overlooks the dynamic retrieval process required to navigate massive tool spaces. To address these challenges, we introduce \textit{FinToolSyn}, a forward synthesis framework designed to generate high-quality financial dialogues. Progressing from persona instruction and atomic tool synthesis to dynamic retrieval dialogue generation, our pipeline constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances, incorporating dynamic retrieval to emulate the noisy candidate sets typical of massive tool spaces. We also establish a dedicated benchmark to evaluate tool-calling capabilities in realistic financial scenarios. Extensive experiments demonstrate that models trained on FinToolSyn achieve a 21.06% improvement, providing a robust foundation for tool learning in financial scenarios.
SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures
PDF ↗As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decisions in real-world scenarios. However, business scenarios often involve complex standard operating procedures (SOPs), and the evaluation of LLM capabilities in such contexts has not been fully explored. To bridge this gap, we propose SOP-Maze, a benchmark constructed from real-world business data and adapted into a collection of 397 instances and 3422 subtasks from 23 complex SOP scenarios. We further categorize SOP tasks into two broad classes: Lateral Root System (LRS), representing wide-option tasks that demand precise selection; and Heart Root System (HRS), which emphasizes deep logical reasoning with complex branches. Extensive experiments reveal that nearly all state-of-the-art models struggle with SOP-Maze. We conduct a comprehensive analysis and identify three key error categories: (i) route blindness: difficulty following procedures; (ii) conversational fragility: inability to handle real dialogue nuances; and (iii) calculation errors: mistakes in time or arithmetic reasoning under complex contexts. The systematic study explores LLM performance across SOP tasks that challenge both breadth and depth, offering new insights for improving model capabilities. We have open-sourced our work on the anonymous link: https://github.com/meituan-longcat/SOP-Maze.
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations
PDF ↗Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid use or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4,709 utterances, including 2,336 help seeker turns, with double-blind labeling reaching Cohen’s kappa of 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 24.0%. In expert review, it averaged 4.62 for evidence supportiveness, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong large language models (LLMs) in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30% and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We release the corpus, annotations, code, and prompts to support research on defensive functioning in language.
As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long-term consistency. However, existing benchmarks primarily focus on casual conversation or task-oriented dialogue, failing to capture “long-term project-oriented” interactions where agents must track evolving goals. To bridge this gap, we introduce RealMem, the first benchmark grounded in realistic project scenarios. RealMem comprises over 2,000 cross-session dialogues across eleven scenarios, utilizing natural user queries for evaluation. We propose a synthesis pipeline that integrates Project Foundation Construction, Multi-Agent Dialogue Generation, and Memory and Schedule Management to simulate the dynamic evolution of memory. Experiments reveal that current memory systems face significant challenges in managing the long-term project states and dynamic context dependencies inherent in real-world projects. Our code and datasets are available at https://anonymous.4open.science/r/realmem-A1E4.
Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learning requires solving the credit assignment problem: determining how individual utterances contribute to multi-turn dialogue outcomes. Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding. We propose SAVOIR (ShApley Value fOr SocIal RL), a novel principled framework grounded in cooperative game theory. Our approach combines two complementary principles: expected utility shifts evaluation from retrospective attribution to prospective valuation, capturing an utterance’s strategic potential for enabling favorable future trajectories; Shapley values ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. Experiments on the SOTOPIA benchmark demonstrate that SAVOIR achieves new state-of-the-art performance across all evaluation settings, with our 7B model matching or exceeding proprietary models including GPT-4o and Claude-3.5-Sonnet. Notably, even large reasoning models consistently underperform, suggesting social intelligence requires qualitatively different capabilities than analytical reasoning.
CogNet-KG: Empowering Tutoring Dialogues with a Cognitively-Structured Knowledge Graph for STEM Learning
PDF ↗Educational knowledge graph (EKG) is a critical component of intelligent tutoring systems that is structured around cognitive principles and provides support for interactive teaching. Most existing EKGs usually rely on simplistic relations, bind with single subjects, and lack integration with explicit learning objectives. In this paper, we introduce CogNet-KG, a novel and cognitively-structured large-scale knowledge graph for STEM learning. CogNet-KG models nearly 500 core concepts across five subjects with various cognitively-grounded relations corresponding to specific learning objectives, thereby encoding a rich cognitive schema for guiding more effective teaching. Based on this structure, we then construct a high-quality tutoring dialogue dataset CogDialogue-QA by leveraging adaptive instructional strategies. Additionally, we train CogTutor-LM, a specialized tutorial LLM that internalizes this structured pedagogical reasoning. Overall evaluation demonstrates that CogTutor-LM generates responses with significantly greater instructional coherence and more appropriate pedagogical guidance compared to baselines, validating the effectiveness of our graph-driven approach to fostering knowledge integration and stimulating students’ thinking. The datasets are publicly available at https://github.com/KCAIED/CogNet-KG.
Bridging Reasoning and Action: Hybrid LLM–RL Framework for Efficient Cross-Domain Task-Oriented Dialogue
PDF ↗Cross-domain task-oriented dialogue requires reasoning over implicit and explicit feasibility constraints while planning long-horizon, multi-turn actions. Large language models (LLMs) can infer such constraints but are unreliable over long horizons, while Reinforcement learning (RL) optimizes long-horizon behavior yet cannot recover constraints from raw dialogue. Naively coupling LLMs with RL is therefore brittle: unverified or unstructured LLM outputs can corrupt state representations and misguide policy learning. Motivated by this, we propose Verified LLM-Knowledge empowered RL (VLK-RL), a hybrid framework that makes LLM-derived constraint reasoning usable for RL. VLK-RL first elicits candidate constraints with an LLM and then verifies them via a dual-role cross-examination procedure to suppress hallucinations and cross-turn inconsistencies. The verified constraints are mapped into ontology-aligned slot–value representations, yielding a structured, constraint-aware state for RL policy optimization. Experiments across multiple benchmarks demonstrate that VLK-RL significantly improves generalization and robustness, outperforming strong single-model baselines on long-horizon tasks.
Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue. Most existing approaches formulate ECPEC as independent pairwise classification, overlooking the distinct semantics of emotion diffusion and cause explanation, and failing to capture globally consistent many-to-many conversational causality. To address these limitations, we revisit ECPEC from a semantic perspective and seek to disentangle emotion-oriented semantics from cause-oriented semantics, mapping them into two complementary representation spaces to better capture their distinct conversational roles. Building on this semantic decoupling, we naturally formulate ECPEC as a global alignment problem between the emotion-side and cause-side representations, and employ optimal transport to enable many-to-many and globally consistent emotion-cause matching. Based on this perspective, we propose a unified framework SCALE that instantiates the above semantic decoupling and alignment principle within a shared conversational structure. Extensive experiments on several benchmark datasets demonstrate that SCALE consistently achieves state-of-the-art performance.
Simulating dementia patients with large language models (LLMs) is challenging due to the need to jointly model cognitive impairment, emotional dynamics, and nonverbal behaviors over long conversations. We present DemMA, an expert-guided dementia dialogue agent for high-fidelity multi-turn patient simulation. DemMA constructs clinically grounded dementia personas by integrating pathology information, personality traits, and subtype-specific memory-status personas informed by clinical experts. To move beyond text-only simulation, DemMA explicitly models nonverbal behaviors, including motion, facial expressions, and vocal cues. We further introduce a Chain-of-Thought distillation framework that trains a single LLM to jointly generate reasoning traces, patient utterances, and aligned behavioral actions within one forward pass, enabling efficient deployment without multi-agent inference.
Theory of Mind (ToM)—an understanding of the mental states of others—is a key aspect of human social intelligence, yet, chatbots and LLM-based social agents do not typically integrate it. In this work, we demonstrate that LLMs that explicitly use ToM get better at dialogue, achieving goals more effectively. After showing that simply prompting models to generate mental states between dialogue turns already provides significant benefit, we further introduce ToMAgent (ToMA), a ToM-focused dialogue agent. ToMA is trained by pairing ToM with dialogue lookahead to produce mental states that are maximally useful for achieving dialogue goals. Experiments on the Sotopia interactive social evaluation benchmark demonstrate the effectiveness of our method over a range of baselines. Extensive analysis shows that ToMA exhibits more strategic, goal-oriented reasoning behaviors, which enable long-horizon adaptation, while maintaining better relationships with their partners. Our results suggest a step forward in integrating ToM for building socially intelligent LLM agents.
Most existing dialogue systems are user-driven, primarily designed to fulfill user requests. However, in many critical real-world scenarios, a conversational agent must proactively extract information to achieve its own objectives rather than merely respond. To address this gap, we introduce Inquisitive Conversational Agents (ICAs) and develop an ICA specifically tailored to U.S. Supreme Court oral arguments. We propose a Dual Hierarchical Reinforcement Learning framework featuring two cooperating RL agents, each with its own policy, to coordinate strategic dialogue management and fine-grained utterance generation. By learning when and how to ask probing questions, the agent emulates judicial questioning patterns and systematically uncovers crucial information to fulfill its legal objectives. Evaluations on a U.S. Supreme Court dataset show our method outperforms single-agent RL baselines in multiple metrics. Although specialized to a single legal domain, it represents an important first step toward broader high-stakes, domain-specific applications. We attached a part of the code as supplementary material. All code will be released upon publication for reproducibility.