ESC: Emotional Self-Correction for Reliable Vision-Language Models

Abstract

Vision-language models (VLMs) have achieved strong per-formance across diverse multimodal tasks, yet they remain vulnerable tounreliable reasoning. Existing self-correction methods mitigate these is-sues but typically rely on post-training or carefully engineered feedback,incurring high computational cost. In this work, we revisit this challengethrough the lens of emotional cues, asking whether they can activate la-tent self-correction behaviors in VLMs without additional training. Wefind that emotional signals serve as an effective trigger for self-correction, encouraging more cautious and reflective reason-ing. Motivated by this finding, we propose ESC (Emotional Self-Correction), a training-free self-correction framework. ESC introducesan external verifier that detects potentially incorrect initial responsesand injects emotional feedback to encourage model to reflect, and pro-duce a better revised response without additional training. Extensiveexperiments across safety, hallucination, vision-centric perception, andmultimodal reasoning benchmarks show that ESC consistently improvesreliability while preserving overall model utility. These results suggestthat emotion can function not only as an ability to be recognized, butalso as a practical control signal for scalable self-correction in VLMs. Wetherefore believe that ESC provides a strong foundation for anew reliable human-like, emotion-integrated research direction.Our project is publicly available at https://genai4e.github.io/ESC/.