An Agentic Approach with Evidence-Grounded Verification for Multimodal Emotion Reasoning in Conversation

LLM-based multimodal emotion reasoning systems perform well on standard benchmarks, but improving them through instruction tuning can be computationally expensive. Agentic pipelines built from frozen models avoid this cost, but introduce a different challenge: when a prediction is wrong, the system