Emotional Quotient (EQ) has emerged as a competency for seamless human-AI integration. However, since traditional EQ scales focus on self-healing, directly migrating them to Large Language Models (LLMs) often leads to ignorance of healing others. While EQ metrics specifically designed for LLMs have been proposed, they remain mired in two dilemmas: dimensional deficiency and fragmented testing. Hence, this paper establishes a Quad-in-One architecture for a closed-loop EQ evaluation. First, we propose the PACE Taxonomy to define four dimensions of LLM EQ. Upon this, the Causal-PACE framework is developed to eliminate causal confounding bias triggered by the interactions among EQ dimensions, ensuring a rigorous quantification of composite EQ scores. To operationalize this framework, we implement the PACE-AB, a mutil-agent EQevaluation board system. Finally, we curate the PACE-2700 dataset, featuring 2,700 high-quality instructions, to serve as a comprehensive benchmark for large-scale validation.Experimental results demonstrate that the EQ values derived via the Causal-PACE achieve a high alignment of 89.31% with human preferences, while the automated PACE-AB system maintains a robust consistency of 83.6%. Our data is publicly available at https://anonymous.4open.science/r/PACE-2700-8E52.
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The reasoning capabilities of large language models (LLMs) have improved substantially through increased test-time computation, typically in the form of intermediate tokens known as chain-of-thought (CoT). However, CoT often becomes unnecessarily long, increasing computation costs without improving accuracy and sometimes even degrading performance, a phenomenon known as “overthinking”. We propose a multi-stage efficient reasoning method that combines supervised fine-tuning—via rejection sampling or reasoning trace reformatting—with reinforcement learning using an adaptive length penalty. We introduce a lightweight reward function that penalizes tokens generated after the first correct answer, encouraging the model to perform self-verification only when beneficial. We conduct a holistic evaluation across seven diverse reasoning tasks, analyzing the accuracy–response length trade-off. Our approach reduces response length by an average of 28% for 8B models and 40% for 32B models, while incurring only minor performance drops of 1.6 and 2.5 points, respectively. Despite its conceptual simplicity, it achieves a better trade-off than more complex state-of-the-art efficient reasoning methods, scoring 76.6 on the area under the Overthinking-Adjusted Accuracy curve (\text{AUC}_{\text{OAA}})—5 points above the base model and 2.5 points above the second-best approach.
As Large Language Models (LLMs) scale to support context windows exceeding one million tokens, the linear growth of Key-Value (KV) cache imposes severe memory capacity and bandwidth bottlenecks, constraining the efficiency of long-context inference. Existing compression approaches typically prioritize tokens based on local saliency metrics to decouple prefill computation from decoding memory. However, these methods often rely on local saliency snapshots at a specific layer, thereby systematically discarding tokens that act as global information hubs across the network depth but appear temporarily dormant at the specific layer selected for pruning. To address this limitation, we propose StructKV, a structure-aware KV cache compression framework that introduces three core innovations: First, Global In-Degree Centrality aggregates attention patterns across the network depth to identify global information hubs. Second, Dynamic Pivot Detection utilizes information-theoretic metrics to adaptively locate the optimal layer for compression. Finally, Structural Propagation Decoupling separates the computational budget from the memory storage budget. Experimental results on the LongBench and RULER benchmarks demonstrate that StructKV effectively preserves long-range dependencies and retrieval robustness.
Using LLMs not to predict plans but to formalize an environment into the Planning Domain Definition Language (PDDL) has been shown to improve performance and control. While most existing methodology only applies to fully observable environments, we adapt to the more realistic and challenging partially observable environments without sufficient information to make a complete plan. We propose PDDLego+, a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes. Without fine-tuning, in-context exemplars, or trajectories, PDDLego+ improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches. We also show that the domain knowledge captured after a successful trial can benefit future tasks.
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning
PDF ↗The success of large language models (LLMs) across domains highlights their potential in scientific tasks, with molecular optimization being a promising frontier. Traditionally, this optimization relies on iterative expert feedback to refine molecules toward desired properties, a process well aligned with LLMs’ strengths. **As an experience-driven task, molecular optimization depends critically on the domain feedback and accumulation of historical knowledge. However, none of the existing methods fully leverages such feedback and historical knowledge with reasoning traces and chemical insights.** In this work, we propose F2R: Feedback to Reasoning, a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. Like humans, LLMs can generate imperfect reasoning; F2R is the first framework to use detailed domain feedback to critique and improve this reasoning. This transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. Consequently, F2R shows remarkable performance.
Failure makes the agent stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions
PDF ↗Tool-augmented large language models (LLMs) are typically trained via supervised imitation learning or coarse-grained reinforcement learning, approaches that primarily optimize one-shot tool calls. Existing practices of self-reflection largely rely on heuristic prompting or unidirectional reasoning traces: the model is encouraged to “think more,” rather than to treat error diagnosis and correction as a learnable capability. This makes them fragile in multi-turn interaction settings—once a call fails, the model tends to repeat the same mistake instead of recovering. To address this issue, we propose structured reflection, which transforms the “from error to repair” process into a first-class, controllable, and trainable action. The agent produces a concise yet precise reflection process: specifically, the model diagnoses the error based on evidence from the previous step and then proposes a correct and executable follow-up call. During training, we combine DAPO and GSPO’s objective functions and design a more principled reward mechanism tailored to tool calling, optimizing the stepwise strategy Reflect \to Call \to Final. To evaluate this capability, we introduce Tool-Reflection-Bench, a lightweight benchmark dataset that programmatically verifies structural validity, executability, parameter correctness, and result consistency. Tasks in the benchmark are constructed as miniature trajectories of Erroneous Call \to Reflection \to Corrected Call and are split into disjoint training and testing sets. Experiments on BFCL v3 and Tool-Reflection-Bench show that our method achieves significant improvements in multi-turn tool-call success rates and error recovery, while also reducing redundant calls. These results demonstrate that making reflection explicit and treating it as an optimization objective can substantially enhance the reliability of tool interaction, providing a reproducible pathway for agents to grow stronger by learning from failure. We will release all the code and datasets as open source once the paper is accepted by the community.
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.
Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
PDF ↗Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annotations for three deceptive patterns—experimental errors, logical fallacies, and fabricated claims—each verified by evidence such as national standards and academic literature. We evaluate eight frontier MLLMs across five modality settings. Experimental results demonstrate that Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Furthermore, we investigate social cues that induce false beliefs in videos and find that models are susceptible to biases like authoritative channel IDs.
Going beyond the prediction of numerical scores, recent research in automated essay scoring has increasingly emphasized the generation of high-quality feedback that provides justification and actionable guidance. To mitigate the high cost of expert annotation, prior work has commonly relied on LLM-generated feedback to train essay assessment models. However, such feedback is often incorporated without explicit quality validation, resulting in the propagation of noise in downstream applications. To address this limitation, we propose FeedEval, an LLM-based framework for evaluating LLM-generated essay feedback along three pedagogically grounded dimensions: specificity, helpfulness, and validity. FeedEval employs dimension-specialized LLM evaluators trained on datasets curated in this study to assess multiple feedback candidates and select high-quality feedback for downstream use. Experiments on the ASAP++ benchmark show that FeedEval closely aligns with human expert judgments and that essay scoring models trained with FeedEval-filtered high-quality feedback achieve superior scoring performance. Furthermore, revision experiments using small LLMs show that the high-quality feedback identified by FeedEval leads to more effective essay revisions. We release our code and curated datasets at: https://github.com/BBeeChu/FeedEval.git.
Existing video understanding benchmarks mainly emphasize general visual recognition and reasoning, but do not adequately capture the pedagogical logic embedded in instructional videos. To address this gap, we present PedagogyBench, a multimodal benchmark for instructional video understanding grounded in pedagogical cognition. We introduce a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement, enabling the construction of a dataset organized around a cognitive pyramid with four levels and 20 fine-grained tasks. We further propose the Cognitive Fidelity Score (CFS) to measure the balance of model performance across pedagogical cognitive dimensions. Experiments on 12 multimodal large language models reveal a clear generative gap, where models perform relatively well on discriminative tasks but degrade on higher-order pedagogical diagnosis, often relying on parametric memory rather than grounded visual perception. Project resources are available at https://github.com/Shallcom/PedagogyBench.
Advances in Multimodal Large Language Models (MLLMs) intensify concerns about data safety, making Machine Unlearning (MU), the selective removal of harmful/private information, a critical necessity. However, existing MU benchmarks for MLLMs are limited by a lack of image diversity, coarse-grained unlearning target, and insufficient evaluation scenarios, which fail to capture the complexity of real-world applications. To facilitate the development of MLLMs unlearning and alleviate the aforementioned limitations, we introduce OFFSIDE, a novel benchmark for evaluating misinformation unlearning in MLLMs. This manually curated dataset contains 15.68K records for 80 players, providing a comprehensive framework with four test sets to assess forgetting efficacy, generalization, utility, and robustness. OFFSIDE supports advanced unlearning targets, such as fine-grained unlearning and visual rumor removal. Our extensive evaluation of multiple baselines not only extends key findings from LLM MU to MLLM MU: (1) unlearned rumors can be easily recovered through relearning and (2) all methods are vulnerable to prompt attacks, but also introduces novel insights in the context of MLLM: (1) unimodal methods fail to handle multimodal rumors, (2) unlearning efficacy is primarily driven by catastrophic forgetting statistically, and (3) all methods struggle with visual rumors (rumors embedded in images). These results expose significant vulnerabilities in current approaches, highlighting the need for more robust multimodal unlearning solutions.
Retrieval shapes how language models access and cite knowledge in retrieval-augmented generation (RAG). In historical research, the goal is often to locate the exact record for a specific regnal month, where temporal alignment matters as much as topical relevance. This is especially challenging for Classical Chinese annals: time is encoded in terse, implicit, non-Gregorian reign phrases that are context-dependent, so semantically plausible evidence can still be temporally invalid. We introduce **ChunQiuTR**, a time-keyed retrieval benchmark built from the **Spring and Autumn Annals** and its exegetical tradition. It organizes records by month-level reign keys and includes chrono-near confounders that mimic real retrieval failures. We propose **CTD** (Calendrical Temporal Dual-encoder), a time-aware dual-encoder combining Fourier-based absolute context with relative offset biasing. Experiments show consistent gains over semantic dual-encoder baselines under time-keyed evaluation. We will release ChunQiuTR and code after the anonymity period.
Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies
PDF ↗Simultaneous Machine Translation (SiMT) requires high-quality translations under strict real-time constraints, which traditional policies with only READ/WRITE actions cannot fully address. We extend the action space of SiMT with four adaptive actions: **Sentence_Cut**, **Drop**, **Partial_Summarization** and **Pronominalization**, which enable real-time restructuring, omission, and simplification while preserving semantic fidelity. We adapt these actions in a large language model (LLM) framework and construct training references through action-aware prompting. To evaluate both quality and word-level monotonicity, we further develop a latency-aware TTS pipeline that maps textual outputs to speech with realistic timing. Experiments on the ACL60/60 English-Chinese, English-German and English-Japanese benchmarks show that our framework consistently improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. Notably, combining **Drop** and **Sentence_Cut** leads to consistent improvements in the balance between fluency and latency. These results demonstrate that enriching the action space of LLM-based SiMT provides a promising direction for bridging the gap between human and machine interpretation.
Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning
PDF ↗Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this work, we conduct a systematic study of calibration in two widely used fine-tuning paradigms: supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). We show that while RLVR improves task performance, it produces extremely overconfident models, whereas SFT yields substantially better calibration, even under distribution shift, though with smaller performance gains. Through targeted experiments, we diagnose RLVR’s failure, showing that decision tokens act as extraction steps of the decision in reasoning traces and do not carry confidence information, which prevents reinforcement learning from surfacing calibrated alternatives. Based on this insight, we propose a calibration-aware reinforcement learning formulation that directly adjusts decision-token probabilities. Our method preserves RLVR’s accuracy level while mitigating overconfidence, reducing ECE scores up to 9 points.
Watch Out Your Industrial Copilots: Stealthy Backdoor Attack Against LLM-Based PLC Code Generation
PDF ↗Recently, there is an emerging trend of using Large Language Models (LLMs) to generate Programmable Logic Controller (PLC) code automatically, resulting in commercialized products such as Siemens Industrial Copilots. While such LLM-driven products have the potential to transform the way control engineers program, they may also introduce a new attack surface. In this work, we introduce STBack, the first stealthy backdoor attack framework targeting LLM-based PLC code generation. STBack first incorporates six malicious logic injection patterns specifically designed for PLCs to generate the poisoned code samples, along with a three-stage automated pipeline to refine stealthiness. Then, it injects the backdoor by finetuning an LLM using the prompts with a semantic-integrated trigger and the corresponding malicious PLC code sample pairs. The compromised LLM will generate malicious PLC code when the trigger is identified in the prompts.We evaluate STBack on multiple LLMs, which achieves 82.92% average attack success rate while remaining stealthy, i.e., maintaining over 95% semantic similarity with benign code and bypassing quality validation, making the injected backdoor extremely challenging to detect. We also show that existing defenses are ineffective against our benign-looking trigger mechanism. This work reveals a novel and critical security threat for industrial copilots, calling for more cautious use and dedicated defenses.
RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
PDF ↗A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rather than truly informative samples, resulting in degraded performance. In this paper, we introduce RADS (Reinforcement Domain Adaptive Sampling), a robust sample selection strategy using reinforcement learning (RL) to identify the most informative samples. Experimental evaluations on several real world clinical datasets show our sample selection strategy enhances model transferability while maintaining robust performance under extreme class imbalance compared to traditional methods. Our code is open-sourced on GitHub.
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
PDF ↗Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms. However, existing LLM-based methods often rely on implicit language-level reasoning, resulting in opaque causal assumptions, unverifiable reasoning paths, and fragile predictions under complex interventions, particularly in context-free settings. In this paper, we propose an explicit and auditable causal reasoning framework for context-free intervention-based question answering. Our method formulates causal inference as structured reasoning over an explicit causal graph through four modular stages, rather than implicit end-to-end prediction. A key innovation is a target-aware causal graph construction strategy that treats the target variable as a core constraint during graph expansion, effectively suppressing irrelevant variables, spurious causal relations, and reasoning noise. We further introduce a path-level causal evidence aggregation mechanism that combines multiple causal paths while modeling both reinforcing and counteracting effects, enabling robust decision-making beyond single-chain reasoning. Extensive experiments on three benchmarks demonstrate that our framework consistently outperforms existing LLM-based methods while providing interpretable and auditable causal reasoning traces.
SplitThenMerge: Token-Level Skill-Compositional Sparse Mixture-of-Experts for Complex Domain-Specific Tasks
PDF ↗Large language models have demonstrated strong performance on general-purpose tasks but often fail to satisfy the accuracy requirements of knowledge-intensive domains such as law, medicine, and finance. Complex domain-specific generation is inherently compositional, involving multiple atomic skills such as reasoning, knowledge grounding, and numerical computation that are frequently interleaved at the token level. Existing domain adaptation methods typically train these heterogeneous skills jointly within a single objective, which makes it difficult for models to reliably coordinate multiple skills when solving complex tasks. In this work, we explicitly incorporate atomic skills into domain-specific model training and propose SplitThenMerge, a framework that decomposes domain competence into atomic skills, trains them independently, and composes them dynamically during generation. SplitThenMerge adopts a token-level sparse Mixture-of-Experts architecture to enable fine-grained skill routing and coordination while implementing each skill as a lightweight LoRA expert to achieve parameter-efficient specialization. Experimental results demonstrate that our method consistently achieves superior performance in both legal and medical domains under the same training parameter budget.
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.
LEAF: Towards Lightweight Explainable Hateful Video Detection via Self-Grounding CoT Guided Stage-Wise Distillation
PDF ↗The rapid spread of hateful videos online has sparked growing social concerns, driving research efforts to detect and limit their dissemination. However, existing methods rely on opaque models that offer no insight into their decisions, eroding trust in detection systems. Large Multimodal Models (LMMs) provide a compelling alternative, thanks to their ability to generate free-text explanations for multimodal content. Yet, their high computational demands and pronounced bias toward benign predictions limit their practicality. We introduce LEAF, the first Lightweight, Explainable hAteful video detection Framework. At its core, LEAF distills the "explainability" from LMMs into efficient Smaller Multimodal Models (SMMs) through a controlled, de-biasing process, enabling lightweight yet interpretable Hateful Video Detection (HVD). We achieve this with a novel Self-Grounding Chain-of-Thought mechanism that guides LMMs to generate high-quality, unbiased explanatory supervision signals for videos. These signals then progressively train the SMM via a new Stage-Wise Distillation paradigm, resulting in faithful, human-readable natural language explanations for HVD. Extensive experiments on three video benchmarks demonstrate that LEAF not only outperforms prior methods in detection accuracy but also provides strong explainability — all with a lightweight design.