A new arXiv paper proposes an intelligent agent framework for traceable multimodal emotion reasoning, supporting evidence verification and prompt repair without weight updates.
On October 26, 2026, arXiv published a preprint paper (ID 2610.31685v1) that proposes a traceable multimodal emotion‑reasoning intelligent agent architecture based on frozen large models. By leveraging modality‑specific perception agents, an integration agent, an MLLM critic, and an evidence‑verification stage, the framework achieves error attribution and adaptive optimization of the prompt space, improving the F‑score for emotion recognition across several open‑source multimodal large models (with 2B–38B parameters) by 0.6–2.2 points on the MER2025 dataset.
Why it matters：This study addresses the trade-off between high instruction fine-tuning costs and the non‑debuggable pipeline of intelligent agents in current multimodal emotion‑reasoning systems by proposing the first end-to-end interpretable framework that integrates evidence citation, certainty verification, failure attribution, and targeted prompt repair. All improvements are implemented without modifying model weights and have been empirically validated across four open-source MLLMs of varying scales, demonstrating both methodological innovation and practical engineering utility.