arXiv AI July 9, 2026 By Yiyang Fang, Wenke Huang, Pei Fu, Yihao Yang, Kehua Su, Zhenbo Luo, Jian Luan, Mang Ye

EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models

arXiv:2602. 23802v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture the complexity and subjectivity of human emotions.

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arXiv AI

Jul 24

EmoAgent-R1: Towards Multimodal Emotion Understanding with Reinforcement Learning-based Dynamic Agent Specialization

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.

By Lihuang Fang, Yuchen Zou, kebin Jin, Jinghui Qin

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Aug 12

Rationale-Guided Learning for Multimodal Emotion Recognition

arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.

By Sujung Oh, Jung Uk Kim, Sangmin Lee

multimodal benchmarks

Hugging Face Trending Papers

Jul 23

MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement

Affective Image Content Analysis (AICA) aims to recognize and understand emotions elicited by visual content, representing an indispensable step toward Artificial General Intelligence (AGI). However, despite the rapid progress of Multimodal Large Language Models (MLLMs), systematic evaluation of their visual emotional intelligence remains largely absent from recent model releases.

llms multimodal benchmarks

arXiv Machine Learning

Jul 2

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).

By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jul 14

Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning

arXiv:2607. 10678v1 Announce Type: new Abstract: Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states.

By Qing Lin, Mengmi Zhang

fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 21

DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning

arXiv:2609.21675v1 Announce Type: new Abstract: Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural...

By Wan Xu, Yuanfan Guo, Kevin Han, LaLa Chen, Wangmeng Zuo

llms reinforcement-learning multimodal benchmarks safety

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EmoAgent-R1: Towards Multimodal Emotion Understanding with Reinforcement Learning-based Dynamic Agent Specialization

Rationale-Guided Learning for Multimodal Emotion Recognition

MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning

DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning