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: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-...
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%.
The paper introduces ProAction, a multimodal dataset of 10,000 samples comprising visual, audio, and text inputs across 12 daily-life scenarios, designed to support the Proactive Robot Action Reasoning (ProRobo) problem. It presents a two-stage human-in-the-loop annotation pipeline that incorporates appraisal and Theory-of-Mind considerations to generate cognitively grounded high-level action labels. The authors benchmark multimodal large language models and propose MMC2Act, showing that training on ProAction significantly improves proactive action reasoning compared to general-purpose models.
The paper introduces TRACE, a cognition-oriented framework that models affective episodes as a sequence of Condition, Affect, and Effect stages, incorporating observable cues and cognitive factors like appraisal and regulation. TRACE-Bench evaluates multimodal models on five tasks—grounded affect recognition, regulation decoding, cause reasoning, effect reasoning, and full-chain reconstruction—using 3,746 question-answer pairs from 646 videos. The authors also present TRACER, a structured reasoning method that links each inference to explicit premises, achieving superior performance across all tasks compared to existing baselines.
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:2601. 02854v2 Announce Type: replace Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning.
DREAM (Dynamic Resolution Assignment For Multimodal Multi-Agent Debate) enhances multimodal multi-agent debate by dynamically assigning each agent the optimal visual resolution through a zero-shot probe round that measures uncertainty with Average Normalized Log-Likelihood. It also introduces Uncertainty-Guided Rollback Aggregation to mitigate groupthink by tracking and restoring low-uncertainty answers that are overridden by group pressure. Across six multimodal datasets, DREAM improves the accuracy-token trade-off by 1.5–3.2% over existing baselines without requiring dataset-specific tuning.
arXiv:2606. 12018v1 Announce Type: new Abstract: We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning.
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:2607. 02089v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning.
arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructi...
arXiv:2608. 05381v1 Announce Type: new Abstract: Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cross-modal reasoning.