Emotion
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Semantic-First MixGRPO: Reliable Emotional Image Generation via Temporal Decoupling
Abstract
Controllable image synthesis should modify visual affect without replacing the objects, layout, or relations specified by a prompt. Existing methods address this tension through conditioning architectures or auxiliary losses, while mixed ordinary differential equation (ODE) and stochastic differential equation (SDE) sampling has mainly been used to accelerate group relative policy optimization (GRPO). We instead study stochastic-window position as an attribute-control variable for flow-based image generation. Using FLUX.1-dev with EmotiCrafter-style valence-arousal conditioning, we compare fixed early, middle, and late four-transition SDE windows, progressive MixGRPO, and full-path Flow-GRPO under matched settings. Because step 0 has the highest noise, our semantic-first schedule uses deterministic ODE sampling at steps 0-19 to establish content, then confines stochastic exploration and policy updates to steps 20-23. Across three seeds, it achieves a CLIPScore of $29.662\pm0.276$, cross-affect DINO consistency of $80.1\pm0.9$, and semantic drift of 2.9%, while reducing training cost by 33.5% relative to full-path Flow-GRPO. Full-path stochastic training remains more accurate on valence-arousal targets, revealing a genuine trade-off. Semantic-first nevertheless improves both affect and semantic metrics over progressive scheduling, preserves the principal subject in 95.98% of images at five extreme or neutral targets, and receives the highest semantic-preservation and overall-preference ratings in a 30-participant study. These results identify probability-flow time as a practical graphics control dimension for reliable affective appearance manipulation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33460
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DJustify: Reason-and-Verify DJ Planning for Pop Transitions and Story-Driven Sets
2026-10-24 04:22:09Wei-Han Hsu, Li-Jie Lin, Po-Hsuan Lai, Po-Hsiang Huang, Jeng-Yue Liu, Li Su, Yi-Hsuan YangAbstract
Automatic DJ systems mostly target electronic dance music, whose regular structure makes rule-based mixing relatively easier. Vocal-rich popular music is harder: which track to play next, where to cue it, and which transition to use are tied together, so fixed rules handle them poorly. We present DJustify, a pop AI-DJ that puts an LLM in a reason--propose--verify--repair loop and lets it choose the next track, cue points, and mixing technique together. The LLM samples several transition plans; an external acoustic checker then scores the rendered audio and triggers repair when a check fails. Sampling keeps several candidate transitions per track pair instead of one fixed mix. In blind listening tests our transitions beat Mosaikbox, a reimplemented crossfade, and a cue baseline that reuses the crossfade's cues; that last comparison shows most of the margin comes from cue placement. The same planner also picks and orders a set along an emotion arc: this improves story coherence over a no-story ablation, and within a set story and transition ratings show no measurable gap from a human DJ. The checker mainly adds reliability, not higher average quality: it repairs rare but audible errors such as cut vocals, while the planner stays non-deterministic.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33434
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AdShot: Benchmarking Multimodal Large Language Models for Video Advertisement Clipping
2026-10-24 00:14:26Wen Xie, Om Rastogi, Sai Siddhartha Vivek Dhir Rangoju, Gijs Overgoor, Yakov Bart, Sarah OstadabbasAbstract
Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process. While multimodal large language models (MLLMs) excel at general video understanding, their ability to perform specialized editorial tasks remains unexplored. We introduce AdShot, a benchmark for evaluating MLLMs on shot selection for ad clipping. It contains 823 professionally edited ad pairs (30-sec sources and 15-sec edits) across 194 brands and 17 industries, annotated with shot-level boundaries and content features (action, emotion, information, imagery). We evaluate 13 state-of-the-art video-only MLLMs and 2 audio-visual MLLMs (7B-72B parameters) on shot selection accuracy (Precision, Recall, F1), duration constraints (15s), and content preservation, including an audio ablation. We find: (1) model architecture matters more than scale, with smaller models often matching or exceeding larger ones; performance is strongest for target ads with 6-10 shots; (2) models exhibit a mean absolute duration error of 5.39s, with most tending to overshoot the 15s target, though they can still substantially reduce human editing effort; (3) all models exhibit systematic content biases, favoring information-rich and emotion-focused shots while under-selecting action and imagery. We publicly release our benchmark to facilitate research at the intersection of multimodal AI and computational advertising.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33377
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The As-If Machine: Personalized counterfactuals make distant policy risks feel close
2026-10-23 21:11:11Joshua Ashkinaze, Yubo Zhou, Pengyu Chen, Avi Bhattacharya, Hyebin Park, Yanman Zhang, Stephanie Preston, Eric Gilbert, Ceren BudakAbstract
Life experiences can shape policy attitudes, but we only live one life. Citizens may therefore fail to support policies addressing risks they have never personally experienced. Narratives can make such risks immediate and personal, but personalized stories remain difficult to produce at scale. Here we ask whether AI can make distant scenarios feel concrete by simulating a participant's life as if that scenario occurred. We present the As-If Machine (AIM), a multi-agent system through which people co-write personalized counterfactual narratives---stories about their own lives under different policy futures. In two pre-registered and quota-matched experiments on science funding (N=1,007) and satellite cybersecurity (N=894), we compared AIM with two controls: reading an expert-written vignette on the same topic and co-writing with AIM about an unrelated topic. AIM increased psychological closeness---how close a risk feels to the self---by nearly identical margins in the two domains (0.36 and 0.38 standard deviations (SDs) against the vignette, 0.80 and 0.90 against unrelated writing). AIM increased psychological closeness even among participants with unfavorable views of AI. For policy attitudes, AIM's effects were 0.16 SDs against the vignette and 0.10 SDs (n.s.) against unrelated writing for science funding; the corresponding effects for satellite cybersecurity were 0.54 and 0.67 SDs. Across 23,496 human-AI autocomplete interactions, participants and the system played distinct roles: AIM supplied concrete detail that grounded the scenario, and participants supplied first-person perspective and emotion. Co-writing with the As-If Machine makes distant policy risks feel close.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33301
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Interpretable Singing Emotion Recognition Using Acoustic and Vocal Technique Features
2026-10-23 13:42:34Fotini Kiriakopoulou, Vassilis Lyberatos, Spyridon Kantarelis, Athanasios Voulodimos, Giorgos StamouAbstract
Singing emotion recognition relies predominantly on deep learning models that achieve strong performance but offer limited insight into the vocal characteristics driving emotion, of- ten overlooking the singing voice as a distinct signal. We propose an interpretable singing emotion recognition framework using a compact set of singing-specific descriptors across prosody, spec- tral features, phonetics, and vocal technique. To decode model decisions and link predictions to measurable acoustic phenomena, we employ SHAP and apply GRAD-CAM to qualitatively assess the learned representations of vocal techniques. Evaluations show that singing-specific features reduce cross-singer variability, while vocal technique descriptors provide interpretable insights into singing expression. Furthermore, out-of-domain evaluation on VocalSet reveals interesting associations between emotional expression and ground-truth technical execution. The analysis highlights vibrato and pharyngeal techniques as salient cues of emotional delivery, with SHAP indicating that vibrato is context-dependent on articulation rate. These findings emphasize the value of domain-specific features for interpretable singing emotion analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2610.32629
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Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers
2026-10-23 13:24:45Harvey Mannering, Zhiwu Huang, Adam Pr\"ugel-BennettAbstract
Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space. Existing VA-based image editors are typically training-based and domain-specific, limiting them to VA regions seen during training and making them brittle across datasets and domains. Meanwhile, despite producing high-quality edits, state-of-the-art foundation image editors are not continuous affective controllers: non-linear emotion representations in text encoders makes prompt interpolation poorly calibrated, so accurate and continuous VA control remains unreliable. To fill this gap, we introduce Barycentric Guidance, a training-free framework that turns pretrained foundation image editors into continuous affective controllers over VA, requiring no finetuning, extra data, or model changes. Our method maps any target VA point to simplex-based guidance weights, enabling VA controllability in three aspects: (i) accurate targeting of VA states, (ii) broad coverage across the VA plane, and (iii) continuous control. Across three face datasets, we improve VA target accuracy by 25-29%, expand VA-plane coverage by 28-50%, and increase expression diversity by 29-49%, over the strongest baseline. Unlike prior domain-specific methods, our training-free approach exploits the foundation model's inherent versatility, enabling generalization beyond human faces to animals, artworks, and complex scenes within a single framework. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.16892
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CASE: Confusion-Aware Skill Evolution for Frozen Multimodal Emotion Models
2026-10-23 10:50:03Siyu Wu, Zeyu Liang, Linghao Wang, Qiyun Zheng, Yi Yao, Ling Lo, Hongxia XieAbstract
Multimodal large language models (MLLMs) have substantially advanced Multimodal Emotion Recognition (MER) by enabling fine-grained emotion understanding through reasoning over multimodal evidence. However, they still exhibit systematic confusion between semantically adjacent emotions with highly overlapping multimodal cues. Existing approaches typically address these errors through global parameter adaptation, making confusion correction costly and inflexible. We instead observe that emotion confusion is highly localized and therefore better addressed through reusable pair-specific expertise. Building on this insight, we propose Confusion-Aware Skill Evolution (CASE), the first agent-skill framework for MER. CASE maintains a sparse, self-evolving library of pair-specific EmotionSkills that externalize affective expertise from model parameters, enabling the learned expertise to be continuously refined and readily transferred across diverse frozen MLLM backbones. During inference, CASE dynamically retrieves the appropriate EmotionSkill to reinforce boundary-aware reasoning while keeping the MLLM backbone frozen. Extensive experiments on CA-MER, DFEW, and EMER demonstrate the effectiveness and transferability of CASE across both general-purpose and emotion-specialized MLLMs. Notably, CASE improves the average accuracy of Qwen3-VL-8B on the CA-MER benchmark from 46.80% to 59.13%, while achieving consistent gains across MLLMs.
Abstract (translated)
URL
https://arxiv.org/abs/2610.32481
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Forging Emotions: a deep learning experiment on emotions and art
2026-10-23 04:54:00Amalia FokaAbstract
Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recently been driven by the use of Generative Adversarial Networks (GANs), a deep learning method. The experiment presented in this paper, Forging Emotions, explores the use of visual emotion datasets and the working processes of GANs for visual affect generation, that is, for generating images that can convey or trigger specified emotions. This experiment concludes that the methodology used so far by computer science researchers to build image datasets for describing high-level concepts such as emotions is insufficient and proposes utilizing emotional networks of associations according to psychology research. Forging Emotions also concludes that to generate affect visually, merely corresponding to basic psychology findings, such as bright or dark colours, does not seem adequate. Therefore, research efforts should aim to understand the structure of trained GANs and compositional GANs in order to produce genuinely novel compositions that can convey or trigger emotions through the subject matter of generated images.
Abstract (translated)
URL
https://arxiv.org/abs/2610.32163
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Controlled Evaluation of EEG Foundation Models: Random Initialisation, Band-Power and Validation-Selected Probes
Abstract
EEG foundation models report downstream gains, yet their protocols seldom include three controls. These are the same encoder untrained, spectral features fitted on the same data, and a layer picked without looking at test scores. We therefore evaluate five publicly released EEG foundation models and an in-house JEPA-style model under one protocol with all three controls and several seeds, on five benchmarks and four added task families. The controls change the reading in three ways. First, picking the probed layer by its test score raises the layer-wise peak by an average of +0.034 and reverses the peak-versus-output gap in 9 of 36 cells. Second, with the layer picked on validation, random initialisation matches or beats pretraining for half of the models on sleep and emotion, and no model beats a five-band log-power baseline on emotion or motor imagery. Third, after full fine-tuning only 5 of 30 pairs gain beyond twice their seed spread, and the one clear loss of -0.290 AUROC persists under matched learning rates. From these results we draw four reporting practices, and we release every cell for re-analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2610.32067
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Predicting Music Valence and Arousal via Textual and Multimodal In-Context Learning in Large Language Models
2026-10-22 12:49:14Chi-En Dai, Chih-Pin Tan, Yi-Hsuan YangAbstract
While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature. In this work, we investigate the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction. Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model. We systematically evaluate a text-based ICL variant using lyrics and generated audio captions, alongside a multimodal variant that incorporates raw audio signals. Using the entire MER60 dataset strictly as a held-out test set, our experiments demonstrate substantial performance gains, particularly for valence. Text-based ICL improves the valence $R^2$ from 0.110 to 0.427 with 80 demonstration shots, while the multimodal variant achieves 0.346 with only 10 shots. These findings suggest that training-free ICL offers an effective inference-time strategy for current language models to predict continuous music emotion.
Abstract (translated)
URL
https://arxiv.org/abs/2610.31202
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AGF-Net: A Robust Multimodal Emotion Recognition Model for Asynchronous Audio-Visual Signals Based on Predictive Dynamic Fusion with Negative Learning
2026-10-22 03:16:19Chuan Han Li, Yun Chien ChengAbstract
This study develops an audio-visual emotion recognition system that integrates automatic utterance segmentation with multimodal fusion. Unlike conventional methods that rely on manually annotated utterance boundaries, the proposed system automatically generates candidate segments from continuous dialogue and extracts the corresponding audio and visual features for emotion classification. To address the challenges introduced by automatic segmentation, the proposed AGF-Net integrates Audio-Guided Fusion (AGF), Multimodal Negative Learning (MNL), and Predictive Dynamic Fusion (PDF) to improve cross-modal fusion, modality balance, and robustness to varying modality quality. Experimental results show that AGF-Net achieves 64.92% Accuracy and 65.47% F1-score under clean conditions, 62.13% Accuracy and 63.08% F1-score under moderate noise, and 58.19% Accuracy and 59.41% F1-score under higher noise. Overall, the proposed framework reduces reliance on manually annotated utterance boundaries and maintains competitive performance under automatically segmented and noisy audio-visual inputs.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30651
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EmoFI-TTS: Face-Conditioned Expressive Voice Synthesis with Intensity Modulation
2026-10-21 21:51:23Yejin Jeon, Luel Hagos Beyene, Marie Maltais, Hyounghun Kim, David Ifeoluwa AdelaniAbstract
Controlling emotional intensity remains challenging in expressive text-to-speech synthesis, particularly when emotion must be inferred without explicit labels. We propose EmoFI-TTS, a face-conditioned framework that derives emotion category and intensity from a single facial image while extracting speaker characteristics from reference audio. Modality-specific representations are fused to condition a pretrained speech synthesizer, with cross-speaker training encouraging separation of affect and vocal identity. An intensity-conditioned refinement module enhances the generated mel-spectrogram, while interpolation of learned intensity embeddings enables continuous control without additional training. On speaker-disjoint MEAD evaluation, EmoFI-TTS achieves on average 64.4% and 75.2% objective and subjective emotion intensity accuracies, while retaining low word error rate and high naturalness.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30477
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SIM-UT: A User Simulation based Evaluation Framework for Medical Triage Chatbots
2026-10-21 20:14:16Deepali Mittal, Dheeraj Mundhra, Yulia Sidorova, Tersia de Kock, Oli Munns, Sofie S MeyerAbstract
LLM-based chat agents are increasingly used for complex, multi-turn, multilingual medical triage in low- and middle-income countries (LMICs), yet evaluation methods focus on single-turn, English-only benchmarks that primarily test the chatbots' knowledge. In practice, users are often confused, overwhelmed, or emotionally vulnerable, giving unclear, misspelled single-word answers or long messages in regional dialects, and the context of a conversation can shift rapidly. We propose SIM-UT (Simulation of User Behaviour for Triage), a multi-turn, simulation-based evaluation method that identifies ecologically valid, contextually relevant failure modes of LLM-based triage chat agents. Using SIM-UT, we evaluated three real-world WhatsApp-based health chatbots: a respiratory screening programme in India, a hearing health support programme in South Africa, and a diabetes and lifestyle coaching agent in Kenya. We compared a medically fine-tuned open model (MedGemma) against general-purpose frontier models (ChatGPT and Claude) across 5,000+ multilingual simulated conversation turns grounded in the personas, constraints, and communication patterns of the real user populations, over 2-4 iterative rounds. A key failure mode emerged in both model families: de-escalation risk. The agents initially identified medical emergencies correctly, but when the user minimised the symptoms without introducing new clinical information, they de-escalated, exhibiting clinically dangerous sycophancy, for example agreeing that vaginal bleeding in pregnancy was "not a problem" or that decreased fetal movements at 32 weeks "can be normal". We recommend user simulation-based behavioural evaluation, grounded in real user context and covering adversarial and edge cases, as standard practice for triage chatbots in production, so that safeguards can be added before deployment or in subsequent iterations.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30412
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MERP: A Latent-Bottleneck Framework for Robust Multi-Modal Multi-Label Emotion Recognition
2026-10-21 08:54:49Chuangxin Zhao, Meimei Zhang, Canran Xiao, Jiachen Luo, Jiajun He, Kaijie Chen, Huy Phan, Tomoki Toda, Bi\"orn W. SchullerAbstract
Multimodal multi-label emotion recognition is important for affect-aware systems, but models trained with complete text, visual, and acoustic streams often degrade sharply when some streams are unavailable at deployment. We address this train-test gap by learning a compact representation that preserves emotion-specific evidence from any available subset of modalities. We propose the Multi-modal Emotion Recognition Perceiver (MERP), which maps the joint sequence into a fixed-size latent bank, thereby keeping repeated reasoning independent of raw input length; it extracts label-specific evidence before and after latent reasoning and aligns the two levels through reconstruction. MERP further uses positive and negative class prototypes, a noise-gated feature enhancer with frequency-shaped perturbations during training, and confidence-aware class-wise fusion to strengthen separability, reduce sensitivity to missing inputs, and combine complementary predictors. Under 20 percent test-time random modality drop, MERP achieves micro-F1 scores of 0.563 on CMU-MOSEI and 0.780 on M3ED, exceeding the strongest evaluated baselines by 0.004 and 0.005, respectively; in the severe CMU-MOSEI audio-only setting, it retains 0.473 micro-F1 versus 0.022 for LDDU. These results show that fixed latent reasoning provides a practical, testable route to robust multimodal emotion recognition without requiring missing-modality examples during training. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29430
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Beyond Small Talk: Modeling Disclosure Trajectories in Human-AI Conversation
2026-10-21 08:36:47Keith Kwok, Wanting Ning, Tej Deep Pala, Desmond C. Ong, S. Supraja, Renwen Zhang, Soujanya PoriaAbstract
Recently, we see a rise in conversational agents as a form of social and emotional support, where disclosure will unfold over time. Yet, current evaluations of these agents typically examine isolated conversational states. In analyses of naturally occurring conversations, disclosure history and current disclosure are confounded which tends to make their effects difficult to isolate. We thus introduce Trajectory-based Disclosure Evaluation (TraDE), an evaluation framework grounded in Social Penetration Theory that utilizes predefined disclosure trajectories and evaluates how preceding disclosure history shapes model behavior under matched current disclosure. Through the use of TraDE, we demonstrate across multiple models and relational behavior benchmarks that responses to disclosures matched on depth can differ depending on the preceding trajectory, particularly in whether models accept or challenge the user's framing. We also observed that these differences track accumulated exposure to deeper disclosure rather than any single moment of vulnerability or the order of disclosures. It also tends to persists for several turns after disclosure returns to shallow levels. For several behaviors, we also demonstrate that models diverge most consistently from human supporters in the Emotional Support Conversation corpus (ESConv) not when users open up but rather when they step back in their disclosure. TraDE thus provides a systematic way to expose history-dependent relational behavior that evaluations of isolated conversational states would otherwise miss.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29413
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StyleStream 2.0: Fast and Controllable Streaming Voice Style Conversion
2026-10-20 21:34:02Yisi Liu, Nicholas Lee, Gopala K. AnumanchipalliAbstract
Voice style conversion (VSC) aims to transform a source utterance to match the timbre, accent, and emotion of a target voice while preserving linguistic content. StyleStream 1.0 introduced the first streamable zero-shot VSC system with state-of-the-art conversion quality, but remained limited in two ways: approximately 1s end-to-end latency on consumer hardware, and the lack of a text-based interface for designing or editing target voices. We present StyleStream 2.0, a fast and controllable streaming VSC framework that addresses these limitations. To reduce latency, we train a few-step pixel MeanFlow model and further fine-tune it with autoregressive feedback inspired by Self Forcing, improving robustness under streaming inference. This reduces end-to-end latency to 520~ms on a consumer GPU, a 2x speedup over StyleStream 1.0, while maintaining conversion quality. To enable controllable style manipulation, we remove the mel-spectrogram context and instead route target style information through a compact style embedding. In this embedding space, we train a unified text-conditioned flow matching model that supports both text-based voice design, which maps natural language prompts to voice styles, and instruction-based voice editing, which modifies a specific attribute of a source utterance while preserving the rest. Experiments show that StyleStream 2.0 achieves the strongest target style fidelity on VSC, voice design, and voice editing, while remaining competitive on intelligibility and non-edited attribute preservation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28940
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Beyond a Single Sentiment Score: Topical and Affective Dimensions of Emoji Meaning on Twitter
2026-10-20 15:03:55Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari, Shreya Shah, Gauri PethkarAbstract
Emoji are widely treated as compact affective tokens whose meaning is summarised by a single sentiment score. We argue that this picture is incomplete. From a corpus of 16{,}575 English tweets and a per-emoji metric we call \emph{stability delta} (within-class minus between-class cosine similarity of sentence embeddings), we show that emojis used in tightly clustered topical contexts also exhibit \emph{more} polarised crowd-sourced sentiment, not less. Validated against the Kralj Novak et al.\ (2015) sentiment lexicon, the central correlation is Spearman $\rho = +0.361$ (Holm $p = 0.015$, 95\% bootstrap CI $[+0.10, +0.59]$); against the independent Ferré et al.\ (2025) Spanish 13-emotion ratings, $\rho = +0.509$ ($p = 0.0003$). The result survives permutation testing, BCa bootstrap, leave-one-out jackknife, partial correlation controlling for emoji frequency, and Holm correction across a four-test family. Topical coherence and affective dispersion are distinct dimensions of emoji meaning. As a downstream check we report leakage-free five-fold cross-validation results for sarcasm classification on the Emoji Sarcasm Dataset; all three baselines (a gated multimodal architecture, fine-tuned BERTweet features, and fine-tuned RoBERTa features) score above 0.97 macro F1, indicating the dataset has saturating emoji-label cues. We frame the classifier as supporting evidence rather than the contribution.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28337
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PI-MLLM: Psychophysics-Inspired Multimodal Large Language Model for Oddly Satisfying Video Emotion Analysis
2026-10-20 14:20:39Yuheng Zhang, Jie Zhang, Yuecong Min, Yunwei Zhao, Meichen Liu, Qiankun Li, Shiguang ShanAbstract
Oddly Satisfying Videos (OSVs) can evoke strong viewer responses through repetitive motion, visual tactility, and ASMR-like sounds despite containing few explicit semantic emotion cues. This makes OSVs a challenging setting for Video Emotion Analysis (VEA), since most existing methods focus on semantic video content and under-model the physical sensory dynamics that shape viewer responses. To support Oddly Satisfying Video Emotion Analysis (OSVEA), we construct the Oddly Satisfying Video Dataset (OSVD), comprising 12,000 annotated 5-second clips. Each clip provides viewer-emotion labels under video-only, audio-only, and audio-visual conditions, together with explanations grounded in visual and acoustic sensory evidence. Building on OSVD, we propose PI-MLLM, a psychophysics-inspired multimodal large language model that integrates Modality-Specific Dynamics Alignment (MSDA) and Coalition-Based Audio-Visual Attribution (CAVA). MSDA uses auxiliary prediction objectives to align modality representations with visual motion and acoustic energy-onset dynamics. CAVA evaluates emotion scores across modality coalitions and applies Shapley attribution to identify the dominant modality and determine whether visual and auditory evidence is synergistic or conflicting. Experiments show that PI-MLLM improves both viewer-side emotion prediction and sensory-grounded explanation quality, suggesting the value of physical sensory cues to video emotion analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28264
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Uncovering Audio-Text Alignment in Large Audio-Language Models through Cross-Modal Concept Neurons
2026-10-20 13:27:40Tzu-Quan Lin, Chih-Kai Yang, Yi-Cheng Lin, Hao Tang, Hung-yi LeeAbstract
While the language model backbone is pretrained on text alone, large audio-language models (LALMs) can, to a great extent, recognize acoustic attributes, such as emotion, speaker gender, and spoken language. However, it is unclear how the audio representation is integrated, if at all, with the other text representation before the model produces text. We hypothesize that there is a shared subspace where the audio and text representations are aligned. To test this hypothesis, we identify cross-modal neurons that are responsible for representing both audio and text. We find alignment between audio and text neurons to various degrees across three LALMs and four acoustic attribute types. Causal interventions, steering and deactivating the neurons, confirm that the cross-modal neurons are not just necessary for prediction but can also be manipulated.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28189
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Reading Too Much Into It? Interpretive Preferences and Commitment in LLM Mentalisation under Ambiguity
2026-10-20 12:18:11Stephanie Fong, Yiwen Jiang, Qingyang Xu, Zimu Wang, Yaling Shen, Xiangyu Zhao, Jiahe Liu, Yinghao Zhang, Vincent Lee, Dominic DwyerAbstract
Large Language Models (LLMs) are increasingly used for interpersonal advice, potentially shaping how users interpret ambiguous social encounters. Existing Theory of Mind evaluations typically rely on predefined answers, while uncertainty research overlooks LLMs' preferred explanations and confidence under psychological distress. To address these gaps, we introduce MentalLoom, a novel evaluation of LLM mentalisation comprising 604 ambiguous social episodes, matched depression and social anxiety disclosures, and four-turn dialogues with unchanged event evidence. Across eight LLMs, disclosures initially elicited more negative explanations overall but more positive narrator-directed interpretations. Scripted repeated questioning showed greater emotional support, stronger positive narrator-directed endorsement, and higher certainty without new event evidence. These findings show that user context can reshape interpretive preferences and commitment while the underlying ambiguity remains unresolved.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28105
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