Emotion
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Move by Move: Measuring and Steering How LLMs Conduct Psychotherapy
2026-08-21 17:32:38Afonso Baldo, Hugo Pitorro, Areti Vassilopoulos, Anabela C. Areias, Maya D'Eon, Fab\'iola Costa, Ricardo Rei, Nuno M. GuerreiroAbstract
Users increasingly turn to large language models for emotional support, yet little is known about how these models actually conduct a psychotherapy interaction. We introduce an ontology of ten therapeutic moves: compact, function-based categories grounded in the MULTI-60 inventory, validated through an annotation campaign with five licensed psychologists, and scaled with a judge-based approach that matches expert agreement. Applying it to real counseling transcripts and model-led sessions, we compare the move distributions between human clinicians and a panel of frontier models. Models over-use inquiry at up to three times the human rate, neglect psychoeducation, and are strongly context-anchored: they carry forward strategies initiated by a human clinician but rarely initiate them themselves. Exposing the ontology as a set of tools roughly halves the mean deviation from the human move distribution and improves turn-level alignment with human therapist by 7-9 percentage points, without any fine-tuning.
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URL
https://arxiv.org/abs/2608.21325
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Affective Context Amplifies Sycophancy in LLM Responses
2026-08-21 15:52:20Jiayi Li, Sanjana Menon, Brett Frischmann, Shomir Wilson, Sarah RajtmajerAbstract
As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates LLM sycophancy in subjective, evaluative interactions, where users share actions or opinions that invite feedback. Drawing on ingratiation theory, we measure sycophancy as the divergence between a model's independent evaluation and its user-facing response, elicited by presenting the same content as either a third-party account or the user's own disclosure. Across seven LLMs and two Reddit datasets (r/AmItheAsshole and r/TrueUnpopularOpinion), we find that this divergence is systematic and strongly one-directional. User-facing responses consistently soften or withhold negative or oppositional judgments. Affective context further amplifies this divergence with negative states, particularly loneliness and distress, producing the largest effects. These findings suggest that affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most, often through evasive sycophancy, in which models retreat toward non-committal responses rather than outright agreement.
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URL
https://arxiv.org/abs/2608.21242
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Recognition-Conditioned Reasoning: A Training-Free Multimodal-LLM Pipeline for Fine-Grained Micro-Action Understanding
2026-08-21 12:10:32Fengshun Wang, Jin'ang Han, Zhigang TuAbstract
Micro-actions are subtle, short, low-amplitude body movements, such as a fidgeting hand or a slight head tilt, that humans perform with little conscious intent yet that reliably leak emotional and psychological state. Understanding them goes beyond assigning a label: a model must also describe which body parts move and reason, faithfully, about why a clip warrants a particular fine-grained category. We present the training-free, prompt-only system that won first place in the fine-grained understanding track (MA-Bench) of the MAC~2026 Micro-Action Challenge, where both fine-tuning and ground-truth supervision are disallowed. Built entirely upon frozen multimodal large language models (MLLMs), the system dynamically routes each of the eight sub-tasks to the MLLM empirically best suited for that task: a discriminative MLLM for closed-ended recognition tasks and a generative MLLM for open-ended description and reasoning tasks. This architecture achieves a statistically significant performance advantage on open-ended tasks, attaining an average score of 2.68 (on a five-point scale) compared to 1.44 for the second-best approach.
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URL
https://arxiv.org/abs/2608.21022
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EmotionDialogCN: A Spontaneous Multimodal Dataset for Mandarin Emotional Dialogue
2026-08-21 09:25:39Yi Zheng, Yifan Xu, Yan Zhou, Hejia Chen, Chunyu Qiang, Xiaoqiang Liu, Xiaohan Li, Shenze Huang, Yue Zhang, Guoying Zhao, Pengfei WanAbstract
Face-to-face audiovisual interaction is central to human communication, conveying rich emotional and social cues. However, existing multimodal dialogue datasets remain limited by inadequate emotion annotations, poor emotional diversity, and small scale. We introduce EmotionDialogCN, a large-scale audiovisual-emotional dataset designed to capture authentic face-to-face communication. It contains 21,880 dialogue sessions performed by 119 professional actors across 20 everyday scenarios, covering 18 emotion categories with over 400 hours of recordings, the largest and most comprehensive dataset of its kind. A novel data collection framework minimizes equipment interference, enabling natural and nuanced emotional expressions. EmotionDialogCN achieves an emotion distribution deviation of 0.64 from real human emotion statistics (versus 5.65 for prior datasets) and consistent subject framing (52-59% frame occupancy). Together, these properties translate into stable unimodal and multimodal performance across acoustic, lexical, and visual modalities, with fusion results further underscoring strong multimodal alignment and cross-modal complementarity.
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URL
https://arxiv.org/abs/2608.20905
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Do SpeechLMs Hear Their Own Opinions? Diagnosing and Mitigating Previous-Belief Contamination in Streaming Emotion Understanding
2026-08-21 06:17:03Haoyue Liu, Zhichao Wang, Ye Chen, Haonan Deng, Xiaoying TangAbstract
Streaming emotion understanding uses historical state while continuously interpreting current audio, often feeding the model's previous prediction back as context. We show that this history conditioning can distort current perception. On a balanced CREMA-D-Stream counterfactual diagnostic, changing only the injected previous emotion label while holding the audio fixed reduces current-audio accuracy from 72.50% to 30.42% and flips 65.69% of predictions. The effect is strongly label-asymmetric, with prior pull ranging from 4.76% to 98.20%, revealing a failure we call previous-belief contamination (PBC). To address PBC, we introduce EmoUpdate, a training-free framework that separates current-audio perception from historical state revision through three components: (1) a prior-blind acoustic firewall that prevents historical state from entering perception; (2) an evidence-shrunk causal belief filter that introduces history only after observation formation and retains label-asymmetric transition structure only when supported by observed evidence; and (3) a closed-form decontamination operator derived from the same counterfactual measurements for serving stacks where firewalling is unavailable. Across four SpeechLMs and two streaming emotion benchmarks, EmoUpdate achieves the best step accuracy and state-balanced accuracy in all eight model--benchmark settings, improving S-BAcc by up to 69.71 points and step accuracy by up to 38.41 points over the strongest controlled baselines.
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URL
https://arxiv.org/abs/2608.20769
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Designing Human-mediated AI Guidance: Ready Together for Personalized Family Emergency Preparedness
2026-08-20 12:17:24Nini Kurashvili, Yana Ivanchenko, Greta Schiavo, Cansu Koyuturk, Dimitri OgnibeneAbstract
Artificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decision support. However, in some contexts, AI-generated information may not be suitable for direct delivery to the final recipient. Instead, it may need to be interpreted, adapted, and communicated by a human who understands the recipient's needs, emotional state, and situational context. Human-AI interaction research has given less attention to situations in which a more knowledgeable human acts as an intermediary between an AI system and a less experienced or less informed recipient. We introduce the human-mediated AI guidance framework and explore it through Ready Together, an AI-supported family emergency preparedness system in which parents mediate AI-generated content for their children. The system is designed to provide personalized guidance and support parents in making emergency preparedness more interactive and understandable through guided activities and family-centered learning. The system design was informed by a qualitative, design-oriented research process involving semi-structured interviews and co-design activities. Findings identified challenges in family emergency preparedness, including difficulty discussing emergencies with children, uncertainty about providing appropriate explanations, and a preference for interactive learning activities. These findings informed the design of an interactive prototype, subsequently evaluated through a pilot study and a heuristic evaluation. Participants responded positively to the personalized recommendations and practical activities. Preliminary findings suggest that human-mediated AI guidance may support context-sensitive family preparedness while preserving parents' responsibility for interpreting, adapting, and communicating AI-generated information.
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URL
https://arxiv.org/abs/2608.19950
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Calming Robot Pitches? Exploring the Influence of Robot Voice Pitch on Children's Stress Levels
2026-08-20 09:27:56Nina G. M. van Roij, Emilia I. Barakova, Briana Isaila, Aoju ChenAbstract
This study examined whether variations in robot speech pitch influence children's stress levels during a robot-guided game. Although lower-pitched voices have been shown to facilitate stress regulation in human communication, it remains unclear whether this effect generalizes to synthetic voices in child-robot interactions. Twenty-seven Dutch children aged 8-12 years were randomly assigned to interact with a Zenbo Junior II robot using either a lower-pitched or a higher-pitched voice. The interaction consisted of an introduction followed by a timed LEGO-building game. Stress levels, measured with an adapted version of CAM-S, increased during the game, confirming the stress-inducing nature of the task. No differences emerged between pitch conditions. These findings suggest that the benefits of lower pitch in reducing stress may not directly translate to child-robot interactions. Possible explanations include children's developing sensitivity to emotional tone, mismatches between the robot's voice and appearance, or the use of fixed pitch changes that sound unnatural, since real speech varies dynamically across multiple dimensions. Future research examining combinations of prosodic cues (beyond pitch alone) could provide further insights and help inform robot voice design for effective stress regulation support for children.
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URL
https://arxiv.org/abs/2608.19826
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Humanoid Musical Robots as Experimental Interfaces for Music-Evoked Emotion
2026-08-20 03:23:36Vincent K. M. Cheung, Jia-Yeu LinAbstract
Advances in technology have led to increasingly sophisticated musical humanoid robots. However, their use has largely been limited to performance and related research in human-robot interaction. In this position paper, we propose a novel perspective: musical humanoid robots as experimental interfaces for investigating music-evoked emotions. We argue that current research is constrained by paradigms relying on pre-recorded auditory stimuli, which fail to capture the multimodal, embodied, and interactive nature of real-world musical experience. Building on existing theories of music cognition and emotion, we identify mechanisms that require controlled manipulation of both acoustic and non-acoustic variables. We show that humanoid robots are well-suited as they enable parametric control of performance variables, reproducibility across trials, and the decoupling and recombination of auditory, visual, and interactive components. We illustrate the technical feasibility of this perspective through a case study of the WAseda Saxophonist Robot 5 (WAS-5), demonstrating reproducible control of acoustic and interaction variables that are prerequisites for future music-emotion experiments. Our work positions musical humanoid robots as a methodological platform that enables future controlled investigations of music-evoked emotions.
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URL
https://arxiv.org/abs/2608.20433
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Do Large Language Models Hallucinate Electric Fata Morganas?
2026-08-19 11:14:06Kristina \v{S}ekrstAbstract
AI hallucinations - that is, outputs which are made up, cannot be verified, or contradict the source material - are generally regarded as an engineering flaw to be dealt with. This paper contends that they also have philosophical significance when it comes to the question of machine consciousness. We examine the known causes of hallucinations in large language models - such as source-target divergence, discrepancies between training and inference, and overfitting - and we present two empirical investigations. In the first, we apply successive generations of the GPT model to ambiguous factual questions under different temperature settings, finding that higher temperatures result in plausible but incorrect answers while lower temperatures lead to factually accurate ones. The sampling parameters that cause a model to seem creative or spontaneous and thus more likely to pass behavioral tests of intelligence are the same ones that increase its hallucination rate. In the second, we look at an encoder-only model that has been trained on encyclopedic data and which answers questions of the same type factually and without embellishment, indicating that hallucinations are due to exposure to subjective and socially diverse training data rather than to the development of any cognitive ability. Using references to Turing, Searle's Chinese Room, the frame problem, and the cybernetic tradition of Wiener and Ashby, we claim that a model's self-reports of emotion or sentience come within the definition of hallucination, and that any future occurrence of machine consciousness might remain epistemically inaccessible since it would be indistinguishable from a sufficiently advanced hallucination.
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URL
https://arxiv.org/abs/2608.18816
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Sounds Uncertain: Exploring the Affective Aspects of Sonification for Uncertainty Visualization
2026-08-19 08:31:09Marcel-Simon Dutt, Sita A. Vriend, Elias Elmquist, Daniel WeiskopfAbstract
Affective visualization can influence how users perceive, interpret, and engage with data by embedding and conveying emotion through visual design. While sound is widely used in media to evoke emotions, little is known about how sonification can support affective visualization. In this work, we investigate how sonification can communicate emotion in uncertainty visualizations through a co-design study. Participants created two sonifications to accompany a visualization: one conveying the affective component of uncertainty and one conveying neutrality. Our findings show that uncertainty was commonly associated with wavy auditory qualities related to an ominous sentiment. On the other hand, neutrality was associated with clear and relaxing auditory qualities. These results provide insights for the design of visualizations that integrate sonification to communicate the affective component of uncertainty.
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URL
https://arxiv.org/abs/2608.18680
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EMODY Flow: Emotion-Aware Audio-Driven Full-Body Motion Generation
2026-08-19 07:47:02Harsh Kumar Agarwal, Xavier Alameda-Pineda, Olivier PerrotinAbstract
Embodied conversational agents require synchronized full-body motion (body gestures and facial expressions) that aligns with speech and emotional state. Omni-modal large language models excel at multimodal understanding but produce only linguistic outputs, leaving a critical gap in embodied response generation. We identify and address a failure of emotion conditioning: like other conditional generators that under-use weak conditioning signals, a flow-matching model given both a rich audio embedding and a discrete emotion label suppresses the emotion, generating near-identical motion regardless of the specified emotion. We present EMODY Flow, a lightweight (around 35M parameters) flow-matching framework that attaches to a frozen Qwen-3 Omni model and reuses its internal Mimi audio-codecs to condition two parallel DiT generators - one for SMPL-X body pose, one for FLAME facial expressions. A training-time auxiliary emotion classifier restores emotion sensitivity by forcing generated motion to be emotion-identifiable. EMODY Flow sets a new state of the art on BEAT2 gesture quality, with FGD 0.302, Beat Correlation 0.853, and Diversity 24.62 - improving over the best prior results by 26%, 5%, and 62% respectively - and transfers to zero-shot facial animation on TFHP without domain-specific fine-tuning. Beyond these quantitative gains, the classifier yields clearly emotion-separated motion, which we demonstrate qualitatively through a multidimensional-scaling analysis of the generated gestures.
Abstract (translated)
URL
https://arxiv.org/abs/2609.16011
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Designing Social Robots for Social-Cognition Training with Autistic Adults
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Social robots have been widely explored as tools for autism intervention, yet this literature has focused predominantly on children and has rarely involved autistic adults as active contributors to design. This creates a mismatch between existing systems and the social-cognitive challenges autistic adults actually face in everyday life, including navigating ambiguous interpersonal contexts, managing conversational timing, and interpreting implied emotional meaning. To address this gap, we conducted an online focus group and co-design session with five autistic adults to explore what a social robot for social-cognition training should do, how it should interact, and under what conditions it would be genuinely useful. The 90-minute session combined open discussion with structured co-design activities on a shared digital whiteboard, and the resulting verbal and visual data were analysed using reflexive thematic analysis. The analysis yielded seven themes that define core design requirements: the robot should function as a scaffold rather than a substitute, prioritise authenticity over comfort, provide personalised and user-controlled feedback, accommodate emotional self-awareness gaps, respect privacy and contextual boundaries, support rehearsal for real-world social situations, and remain configurable in identity, form, and expression. Together, the findings suggest that autistic adults envision the robot not as a companion or live social assistant, but as a private, configurable rehearsal partner designed to support independence over time.
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URL
https://arxiv.org/abs/2608.18488
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TTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs
2026-08-18 23:40:28Muhammad Haseeb Aslam, Alessandro Koerich, Marco Pedersoli, Ali Etemad, Eric GrangerAbstract
Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is inherently multimodal, requiring a joint understanding of facial expressions, vocalizations, language, biosignals, and gestures. However, real-world deployment remains challenging: modalities may be missing or noisy at test time. Partial observations can be viewed as a distribution shift relative to the complete-modality distribution. SOTA TTA methods based on entropy minimization or perplexity reduction do not transfer to autoregressive LVLMs, while retrieval augmented generation (RAG) degrades when the observed modality is weak. Because no ground-truth supervision exists to verify individual updates, adaptation across this stream risks accumulating drift and degrading once the model departs from a reliable solution. An effective solution must therefore adapt to arbitrary missing-modality patterns and remain effective during continual adaptation. We address both jointly with Test-Time Self-Distillation (TTSD), a parameter-efficient framework in which a frozen teacher, trained on complete modalities, guides an adaptive low-rank student via self-distillation, updating only a negligible number of parameters. Stability is built into this same loop through Fisher-Anchored Restoration (FAR), which monitors Fisher information stability to detect convergence versus drift and restores the student toward the teacher's anchor when distributional shifts are identified. Our experiments on MELD, DFEW, and BAH under 0%-50% missing modalities show that this unified adaptation-restoration design consistently outperforms entropy-based adaptation, RAG, and perplexity-based generation over long adaptation horizons, where baselines without restoration progressively degrade while TTSD-FAR remains consistent.
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URL
https://arxiv.org/abs/2608.18386
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'AI Is Turning Too Human': How Teenagers Experience and Negotiate AI in Everyday Life
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Generative AI is rapidly entering adolescents' everyday lives during a critical period of cognitive, social and emotional development. Yet its adoption is outpacing evidence on how adolescents themselves experience, understand and negotiate its expanding role in their lives. We examined AI-related discourse on r/teenagers from January 2023 to July 2026 using validated keyword-based retrieval and a human-in-the-loop, LLM-assisted thematic analysis. AI-related discussion increased substantially over time, and 11,083 analytically coded posts revealed eight interconnected domains of experience. Everyday and social use was most prevalent (36.8 percent), while discourse increasingly shifted toward authenticity, personal control and safety, and future human roles. Across domains, adolescents questioned when AI should support or substitute for human thinking and creativity, how conversational AI changes relationships and perceptions of agency, what can still be considered authentic, who controls personal information and representation, and what opportunities and roles should remain human. These findings position adolescent AI use not simply as technology adoption, but as an emerging negotiation over AI's place and boundaries in everyday life. Supporting this transition will require developmentally appropriate AI literacy, psychological and social support, and AI systems and policies that protect adolescents' agency, privacy, relationships and opportunities for human development.
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URL
https://arxiv.org/abs/2609.27824
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SpeechSense: A Paralinguistic-Focused Dataset for Fine-Grained Speech Sentiment Analysis
2026-08-18 15:51:27Shicheng Ma, Wenqian Cui, Irwin KingAbstract
Recent advances in AI have revolutionized speech processing, yet effective speech understanding requires discerning not just what is said, but how it is said. Speech Sentiment Analysis plays a critical role in decoding these paralinguistic cues for diverse real-world applications such as recruitment and customer service. However, existing Speech Sentiment Analysis research faces two primary limitations. First, dominant approaches rely on text-centric pipelines that cascade Automatic Speech Recognition with text analysis. This process inevitably discards essential acoustic features like prosody and tone, failing to capture attitudinal meanings in acoustically ambiguous utterances. Second, current benchmarks suffer from a mismatch in label granularity, prioritizing basic emotions (e.g., happy, sad) over the nuanced interpersonal stances (e.g., confident, impatient) necessary for social sensitivity. To address these limitations, we propose a novel dataset, SpeechSense, for fine-grained speech sentiment analysis. Specifically, we define a specialized 8-class taxonomy of interpersonal stances detectable primarily through prosodic cues beyond lexical content alone. We then construct a curated dataset based on this taxonomy, built from high-fidelity speech synthesis and rigorous human validation. Comprehensive experiments across multi-modal LLMs, text-only LLMs, and speech encoders demonstrate that models with acoustic access consistently outperform text-only baselines. These results empirically validate the primacy of acoustic cues in detecting subtle speaker attitudes, highlighting the necessity of SpeechSense. Dataset and supplementary materials are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2608.17931
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Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models
2026-08-17 20:26:22Xiutian Zhao, Luqi Sun, Bj\"orn Schuller, Berrak SismanAbstract
Modern multimodal foundation models (MFMs) have made rapid progress on tasks requiring integrated perception across speech, vision, and language, including emotion recognition. However, it remains unclear whether they recognize speech and facial emotion through shared affective functional units or modality-specific pathways. We explore emotion-sensitive neurons (ESNs), sparse decoder neurons selectively associated with emotion categories, in three MFMs: Gemma-4-12B-it, MiniCPM-o-4.5, and Qwen2.5-Omni-7B. Using speech emotion recognition and facial expression recognition as complementary probes, we identify acoustic and visual ESNs. Visual ESNs are causally meaningful: deactivating them selectively impairs recognition of the associated facial emotion, whereas steering their activations selectively enhances recognition of that emotion relative to other emotion categories. Acoustic and visual ESNs further show emotion-matched overlap and similar layer-wise distributions, indicating partial structural alignment between affective representations across speech and faces. Finally, cross-modal interventions reveal bidirectional causal transfer: ESNs identified from one modality produce emotion-specific effects when applied to the other. Our findings provide one of the first cross-modality activation-level analyses of affective functional units in MFMs, suggesting that speech and facial emotion recognition partially converge onto sparse decoder-level components that can be localized and manipulated without training.
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URL
https://arxiv.org/abs/2608.17102
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PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation
2026-08-17 15:34:54Yuji Wang, Yuheng Chen, Teng Hu, Ran Yi, Yijia Hong, Han Feng, Weijian Cao, Chengjie Wang, Lizhuang Ma, Jiangning ZhangAbstract
Video generation is rapidly evolving from single-shot clips to multi-shot narratives, where the human character serves as the core narrative anchor. However, existing benchmarks mainly assess character appearance or individual-shot quality, without measuring whether physical and emotional states remain coherent across cuts. They also rarely provide criterion-specific evaluation methods, although physical continuity, facial dynamics, and cinematic relations require different visual, temporal, and relational evidence. To address these limitations, we introduce PersonaShot, the first person-centric benchmark for narrative continuity in multi-shot video generation. PersonaShot contains approximately 1,000 multi-shot segments and 16 metrics spanning physical continuity, affective dynamics, and cinematic grammar. \textbf{\textit{1)} Narrative Continuity Benchmark:} We evaluate character coherence across three temporal levels: within-shot states, cross-shot transitions, and sequence-level trajectories. \textbf{\textit{2)} Human-Aligned Specialist Evaluators:} We distill reasoning from a large multimodal teacher into lightweight criterion-specific evaluators, each grounded in the visual, temporal, or relational evidence required by its metric, and align them with expert human judgments. \textbf{\textit{3)} Systematic Evaluation and Insights:} Our evaluation reveals distinct capability profiles across state-of-the-art models and a clear gap between perceptual quality and cross-shot narrative continuity. Even visually compelling videos frequently exhibit physical-state resets, abrupt affective shifts, and broken cinematic relations across shots. Human studies further demonstrate strong agreement between our evaluators and expert judgments.
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URL
https://arxiv.org/abs/2608.16717
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Closing the Affective Loop: Multimodal Speaker-Listener Emotion-Dynamics-Aware Empathetic Social Robots
2026-08-17 15:08:38Zi Haur Pang, Casey Kennington, Tatsuya KawaharaAbstract
Empathetic social robots should respond not only to what users say, but also to how their emotions dynamically evolve during interaction. However, existing empathetic dialogue systems are often text-centered and primarily model empathy as a one-way mapping from the user's emotion to the system response, limiting their ability to capture embodied speaker--listener affective exchange. We present AffectLoop, a multimodal speaker-listener emotion-dynamics-aware spoken dialogue system implemented on the Misty II robot. The system tracks the speaker's verbal and facial affective dynamics, estimates the robot listener's own verbal and behavioral affective state, and conditions LLM-based response generation on both affective streams. The robot then generates a short spoken empathetic response together with emotionally congruent embodied behavior, forming a closed speaker--listener affective loop. We evaluate the system in a pilot within-subject study with five participants, comparing it with an otherwise identical utterance-conditioned baseline that omits the speaker- and listener-affective-state inputs. The proposed system received higher overall impression ratings, especially for empathetic response and user satisfaction. Post-hoc log analysis further showed higher speaker-listener affective alignment and stronger valence-based distress recovery. These preliminary results suggest that explicitly modeling both speaker emotional dynamics and listener affective state can improve embodied empathetic interaction.
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URL
https://arxiv.org/abs/2608.16686
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FTA-Mem: Fact-Time-Affect Anchored Memory for Low-Density Long-Term Dialogue
2026-08-17 09:14:31Chang Liu, Shuyi Zhang, Changsheng Ma, Yongfeng Tao, Minqiang Yang, Bin HuAbstract
Long-term emotional-support agents require memory mechanisms for personalized understanding across sessions. However, emotional-support dialogue is often low-density: turns are incomplete, evidence is scattered, and user states evolve over time. Existing memory methods usually rely on fixed units, such as turn-level notes or session summaries, which may lose details or introduce redundant noise. We propose FTA-Mem, a structured memory framework for low-density long-term dialogue. FTA-Mem uses Boundary-preserving Window Segmentation (BWS) to form coherent situation fragments, and constructs Fact-Time-Affect Memory Units (FTA Units) that jointly encode factual content, temporal grounding, and affective context. Retrieved units are then synthesized into structured context for answer generation. Experiments on ES-MemEval and LoCoMo show that FTA-Mem improves overall long-term memory question answering across benchmarks with different information-density characteristics. On ES-MemEval, FTA-Mem achieves 0.3871 F1 and 0.6668 BERTScore. Further analysis shows that situation-level FTA construction better balances evidence preservation and construction cost than coarse session-level or overly fine-grained turn-pair construction, providing an effective granularity trade-off for long-term dialogue memory.
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URL
https://arxiv.org/abs/2608.16303
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MUPA$^{2}$E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment
2026-08-17 01:26:47Stefanos Gkikas, Eric Nichols, Christian Arzate Cruz, Randy GomezAbstract
Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion. This paper presents MUPA\textsuperscript{2}E, a unified perception framework that processes facial video and electroencephalography (EEG) through a single shared asymmetric-attention backbone. Facial video is represented through axis-folded frame tokens, while EEG is processed either as a raw multichannel waveform or projected into the spatial domain for multimodal fusion. The framework is evaluated on the DMER dataset under a stratified subject-independent protocol, comparing unimodal video, unimodal EEG, and fused video--EEG configurations with per-channel and merged EEG projections. Using the original recordings, with shorter trials zero-padded to match the longest duration, merged fusion at stride~$30$ achieves the highest validation performance and a test accuracy of $70.07\%$. Further analysis revealed that recording duration is unevenly distributed across the affective classes, making the padding pattern a potential classification cue. Controlling for this factor by cropping all recordings to a common duration of $20$ seconds yielded a test accuracy of $62.71\%$, providing a stricter duration-controlled assessment of the framework in which differences in recording length are removed as a potential classification cue. These findings demonstrate the feasibility of processing structurally different neural and visual signals within a compact unified architecture while highlighting the importance of controlling duration-related cues in affective datasets.
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URL
https://arxiv.org/abs/2608.15999
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