#mllm2 articles

2 articles

AIBullisharXiv – CS AI · 6h ago11

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

Researchers have developed EMO-R3, a new framework that enhances emotional reasoning capabilities in Multimodal Large Language Models through reflective reinforcement learning. The approach introduces structured emotional thinking and reflective rewards to improve interpretability and emotional intelligence in visual understanding tasks.

AIBullisharXiv – CS AI · 6h ago3

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Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

Researchers introduce UMPIRE, a new training-free framework for quantifying uncertainty in Multimodal Large Language Models (MLLMs) across various input and output modalities. The system measures incoherence-adjusted semantic volume of model responses to better detect errors and improve reliability without requiring external tools or additional computational overhead.