Researchers propose agentic framework for emotion reasoning with evidence checks
A team of researchers introduced an agentic framework for multimodal emotion reasoning that uses evidence-grounded verification to improve accuracy without retraining models. Their approach splits tasks into modality-specific agents—visual, acoustic, and lexical—that analyze inputs and post observations to a shared board. An integration agent combines these with retrieved examples to generate…
Key points
- Framework uses separate agents for visual, acoustic, and lexical inputs to analyze multimodal emotion reasoning
- Evidence-grounded verification stage checks cited evidence and claim support before final output
- Method improves F-score by 0.6–2.2 points on MER2025 dataset across four MLLMs
On the MER2025 dataset, the method outperformed frozen static pipelines across four multimodal large language models (MLLMs) ranging from 2B to 38B parameters. Improvements ranged from 0.6 to 2.2 points on F-score, including a gain from 52.3 to 54.5 on Qwen2.5-VL-7B. The work aims to reduce computational costs of instruction tuning while maintaining or exceeding performance of undifferentiated reflection baselines.
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