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MLLMs often fail at multimodal emotion recognition due to premature commitment to data priors, but a new architecture, HyDRA, uses reinforcement learning to synthesize evidence-grounded rationales and significantly outperforms strong baselines, especially in ambiguous scenarios.
Protein search agents can now leverage multi-dimensional reward-based RL and multimodal inputs (protein sequence + text) to produce higher quality reports for complex protein analysis tasks.
