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-...
By Jian Li, Fanfan Ji, Jinxiang Lai, Ying Tai, Jian Yang, Xiao-Tong Yuan, Chengjie Wang, Yabiao Wang
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%.
By Siyu Wu, Zeyu Liang, Linghao Wang, Qiyun Zheng, Yi Yao, Ling Lo, Hongxia Xie
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.
By Zhihao Gu, Kechao Zhu, Yuanfeng Wu, Mohan Liu, Ankit Kumar Shaw, ChenDong Hong, Xuanyu Chen, Dengchen Mei, Xu Tianyi, Lin Wang
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.
By Hao Li, Jinye Zhang, Bobo Li, Mong-Li Lee, Wynne Hsu, Zheng Wang, Hao Fei, Min Zhang
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.
By Liang-Yuan Wu, Zih-Ching Chen, Tongshuang Wu, Chao-Han Huck Yang, Hua Shen
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.
By Ao Li, Jinghui Zhang, Luyu Li, Yuxiang Duan, Lang Gao, Mingcai Chen, Weijun Qin, Shaopeng Li, Fengxian Ji, Ning Liu, Lizhen Cui, Xiuying Chen, Yuntao Du