Emotional intelligence in large language models is fragmented across perception, cognition, and interaction

2026-05-23

AI Summary

This study addresses the tendency of current large language models to conflate superficial politeness with deep emotional reasoning, often failing to distinguish between perceptual, cognitive, and interactive dimensions of emotional intelligence. Building upon the Mayer-Salovey-Caruso four-branch ability model, the authors propose FACET—a psychometric evaluation framework comprising 480 expert-designed items—to systematically assess model capabilities in emotion perception, facilitation, understanding, and management. The research reveals, for the first time, that emotional intelligence in large models is fragmented, identifying three distinct performance profiles: cognition-dominant, interaction-dominant, and context-dependent. A consistent bottleneck emerges in recognizing hidden emotions. While state-of-the-art models excel at emotion recognition and social reasoning, they struggle with effective interpersonal interaction, suggesting that current reinforcement learning from human feedback (RLHF) may induce only “stochastic empathy” rather than integrated emotional reasoning.

Abstract

As large language models (LLMs) are increasingly integrated into emotionally sensitive domains, the structural integrity of their emotional intelligence (EI) becomes a critical frontier for safety and alignment. Current benchmarks often conflate superficial politeness with deep affective reasoning, failing to distinguish between perceptual accuracy and interactive efficacy. Here, we introduce FACET (Functional Affective Competence and Empathy Test), a psychometrically grounded framework comprising 480 expert-crafted items. Unlike previous metrics, FACET is theoretically anchored in the Mayer-Salovey-Caruso four-branch ability model, operationalizing EI through perception, facilitation, understanding, and management of emotions. Through an evaluation of nine frontier models (including GPT-5, Claude-Sonnet-4), we demonstrate that emotional intelligence is not a monolithic capability but is fragmented across cognitive and interactive dimensions. While frontier models demonstrate robust proficiency in objective emotion recognition and social reasoning, this does not consistently translate to interactive success. We categorize these discrepancies into three distinct performance profiles: cognitive-dominant, interactive-dominant, and context-dependent. These typologies indicate that emotional skills do not scale uniformly with general intelligence or model size; rather, they are shaped by specific alignment paradigms. Notably, we identify hidden emotion recognition as a universal performance bottleneck across all architectures. Our results suggest that current RLHF processes may optimize for "stochastic empathy", a statistical mimicry of emotional syntax, at the expense of integrated affective reasoning. These findings challenge the assumption of linear emotional scaling and provide a rigorous roadmap for developing socially aware agents capable of genuine clinical resonance.

Problem

• emotional intelligence
• large language models
• affective reasoning
• interactive efficacy
• alignment

Innovation

• emotional intelligence
• large language models
• FACET
• affective reasoning
• RLHF