The paper introduces CASE, a Confusion-Aware Skill Evolution framework designed to improve Multimodal Emotion Recognition (MER) by addressing systematic confusion between semantically adjacent emotions. CASE builds a sparse, self‑evolving library of pair‑specific EmotionSkills that externalize affective expertise from frozen multimodal large language model (MLLM) backbones, allowing continuous refinement and transfer across different models. Experiments on CA‑MER, DFEW, and EMER benchmarks show that CASE significantly boosts accuracy, notably raising Qwen3‑VL‑8B’s performance from 46.80% to 59.13%.
arXiv:2607. 12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc.
Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable description generation by jointly modeling video, audio, and language, etc. However, these performance improvements are often accompanied by an increase in model parameter size (e.
arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
The paper proposes an agentic framework for multimodal emotion reasoning that uses frozen models to avoid costly instruction tuning. It employs modality‑specific perception agents, an integration agent, and an MLLM critic to generate emotion predictions with evidence citations, followed by an evidence‑grounded verification stage that identifies errors and applies targeted prompt patches without updating model weights. On the MER2025 dataset, this approach improves F‑score across several large language‑vision models, outperforming static pipelines and matching or exceeding reflection baselines.
arXiv:2607. 21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new level that is complex emotion understanding with advanced video understanding abilities and natural language description.
The paper introduces a new paradigm for multimodal emotion understanding that moves beyond surface-level cue-label associations by incorporating cognitive appraisal reasoning. It presents CogEmo-40K, a large instruction‑tuning dataset that elicits evidence‑grounded reasoning across six appraisal dimensions; CogEmo‑MoE, a compact sparse multimodal large language model with interleaved MoE blocks for appraisal‑specific adaptation; and CogEmo‑Bench, a benchmark featuring an Appraisal Evidence Quality Score (AEQS) to evaluate why emotions arise. Experiments demonstrate that this perception‑to‑appraisal approach improves cross‑domain generalization and aligns model reasoning more closely with human cognitive processes.
arXiv:2609.06188v1 Announce Type: new Abstract: Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic...
arXiv:2609.13240v1 Announce Type: new Abstract: The AffectiveArt Multidimensional Art Emotion Understanding task asks to jointly predict an artwork's fine-grained emotion (12 classes, 1549:1 head-to-...
AffToM is a new benchmark for affective Theory of Mind (ToM) in dynamic multi‑party interactions, comprising 1,922 test pairs and 19,759 training pairs. It evaluates Individual Affective Reasoning (IAR) and Multi‑party Affective Reasoning (MPAR), the latter tracking nested beliefs and emotions as events unfold. The authors introduce an event‑driven multi‑agent generation pipeline and a two‑stage post‑training framework, AffToM‑R1, which boosts MPAR accuracy by 13.38 percentage points over baseline models.
The paper introduces DAN, a training‑free inference‑time framework that improves affective reasoning in multimodal large language models. It combines a Hierarchical Emotional Reasoning Chain (HERC) to better capture fine‑grained visual cues and a Contrastive Discriminative Visual Pruning (CDVP) module to isolate discriminative tokens for semantically similar emotions. Experiments show significant gains, notably a +10.47% improvement on the WebEmo25 benchmark with Qwen3‑VL‑8B‑Instruct.
SpeechEQ is a new benchmark that evaluates the sociolinguistic reasoning of Speech‑Language Models (SLMs) by using a dataset of 2,265 dialogues covering 15 Emotional Quotient (EQ) subscales from EQ‑i 2.0 theory. The framework introduces a multi‑turn evaluation protocol and a Spoken EQ (SEQ) score inspired by human EQ assessments. Experiments reveal that current multimodal models suffer from a text‑reliant shortcut, an alignment‑induced safety trap, and contextual amnesia, limiting their emotional awareness in spoken interaction.
arXiv:2609.22778v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require exper...