ResearchResearch paperLarge Language Models · Multimodal Models1 source · Oct 14, 2026

FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation

To address these challenges, we propose FACES, a fine-grained gated semantic alignment framework for MESC.

In recent years, with advances in multimodal interaction technologies and the growing demand for intelligent emotional support, Multimodal Emotional Support Conversation (MESC) has emerged as an important task.
However, existing systems still suffer from shallow responses and inappropriate support strategies due to weak semantic feedback, cross-dimension reward compensation, and coarse reasoning supervision.
Specifically, FACES integrates multi-source rewards to construct multidimensional semantic and functional alignment signals, employs a non-compensatory gated fusion mechanism to reduce cross-dimension reward offset, and leverages structured Chain-of-Thought (CoT) anchors to perform token-level credit assignment.
Results on three external datasets further support transferability.

[1]FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 11:54 AM
To address these challenges, we propose FACES, a fine-grained gated semantic alignment framework for MESC.
In recent years, with advances in multimodal interaction technologies and the growing demand for intelligent emotional support, Multimodal Emotional Support Conversation (MESC) has emerged as an important task.

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