Interpreting and Enhancing Emotional Circuits in Large Vision-Language Models via Cross-Modal Information Flow

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A steering-vector-based causal attribution framework is introduced to interpret and enhance emotional circuits in Large Vision-Language Models (LVLMs). This research constructs a specialized dataset to demystify the three-stage "Adapt-Aggregate-Execute" emotional mechanism. A crucial discovery is a functional decoupling: visual emotional cues are aggregated in middle layers via sentiment-specific attention heads, but are then translated into narrative generation in deep layers through emotion-general pathways. Guided by these insights, the authors developed VEENA, a training-free, surgical inference-time intervention framework comprising Visual Emotion Enhancement (VEE) and Emotional Neuron Augmentation (ENA). Experiments on the MER-UniBench benchmark demonstrate that VEENA significantly improves performance (average +6.7% hit rate on Qwen3-VL-4B-Instruct, from 58.1% to 64.8%), effectively mitigating emotional hallucinations and corroborating the causal fidelity of the discovered circuits.

Key takeaway

For Machine Learning Engineers developing empathetic LVLMs, understanding the "Adapt-Aggregate-Execute" emotional circuits is crucial. You should consider implementing training-free inference-time interventions like VEENA, which significantly improves emotion understanding and reduces hallucinations by jointly regulating attention flow and amplifying emotional neuron activation. This approach, effective on ~3% of parameters, offers a precise way to enhance emotional intelligence without compromising general vision-language capabilities or requiring costly retraining.

Key insights

LVLMs process emotions through a "Adapt-Aggregate-Execute" mechanism with functionally decoupled sentiment-specific aggregation and emotion-general execution.

Principles

• Emotional circuits in LVLMs exhibit a three-stage "Adapt-Aggregate-Execute" flow.
• Sentiment-specific attention heads aggregate emotional cues in middle layers.
• Emotion-general pathways translate cues into narratives in deep layers.

Method

A steering-vector-based causal attribution framework uses contrastive visual counterfactuals and a Latent Restoration Metric to identify critical layers, attention heads, and MLP neurons.

In practice

• Use VEENA's VEE to strengthen attention flow for visual emotional cues.
• Apply VEENA's ENA to amplify emotion-related semantic knowledge in MLPs.
• Intervene on ~3% of parameters (Top-10 heads, Top-30 neurons) for efficacy.

Topics

• Large Vision-Language Models
• Emotional Intelligence
• Mechanistic Interpretability
• Causal Attribution Framework
• Emotional Hallucinations
• Inference-time Intervention

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.