On Emotion-Sensitive Decision Making of Small Language Model Agents

• URL: http://arxiv.org/abs/2604.06562v1
• Date: Wed, 08 Apr 2026 01:24:13 GMT
• Title: On Emotion-Sensitive Decision Making of Small Language Model Agents
• Abstract summary: We study emotion-sensitive decision making by combining representation-level emotion induction with a structured game-theoretic evaluation.<n>Emotional states are induced using activation steering derived from crowd-validated, real-world emotion-eliciting texts.<n>We show that emotional perturbations systematically affect strategic choices, but the resulting behaviors are often unstable and not fully aligned with human expectations.
• Score: 23.213662701638658
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a causal factor influencing behavior. We study emotion-sensitive decision making by combining representation-level emotion induction with a structured game-theoretic evaluation. Emotional states are induced using activation steering derived from crowd-validated, real-world emotion-eliciting texts, enabling controlled and transferable interventions beyond prompt-based methods. We introduce a benchmark built around canonical decision templates that span cooperative and competitive incentives under both complete and incomplete information. These templates are instantiated using strategic scenarios from \textsc{Diplomacy}, \textsc{StarCraft II}, and diverse real-world personas. Experiments across multiple model families in various architecture and modalities, show that emotional perturbations systematically affect strategic choices, but the resulting behaviors are often unstable and not fully aligned with human expectations. Finally, we outline an approach to improve robustness to emotion-driven perturbations.

Related papers

• EmoTrace: An Emotion Trajectory-Centered Framework for Psychological Support Dialogue Generation [10.967647558354725]
  EmoTrace is a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories.<n>We construct seekers' cognitive profile and introduce a seeker module with emotional schemas and an associated activation mechanism, a counselor module, and an emotional trajectory control module.<n> Experimental results demonstrate that the proposed method outperforms existing approaches in terms of emotional richness and empathy quality.
  arXiv  Detail & Related papers  (2026-07-26T13:30:04Z)
• A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
  This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-08T14:07:30Z)
• Detecting Emotional Dynamic Trajectories: An Evaluation Framework for Emotional Support in Language Models [6.810484095299127]
  Emotional support is a core capability in human-AI interaction, with applications including psychological counseling, role play, and companionship.<n>Existing evaluations of large language models (LLMs) often rely on short, static dialogues and fail to capture the dynamic and long-term nature of emotional support.<n>Our framework constructs a large-scale benchmark consisting of 328 emotional contexts and 1,152 disturbance events, simulating realistic emotional shifts under evolving dialogue scenarios.
  arXiv  Detail & Related papers  (2025-11-12T05:47:28Z)
• From Rational Answers to Emotional Resonance: The Role of Controllable Emotion Generation in Language Models [16.350658746140788]
  Large language models (LLMs) struggle to express emotions in a consistent, controllable, and contextually appropriate manner.<n>We propose a controllable emotion generation framework based on Emotion Vectors (EVs)<n>Our method enables fine-grained, continuous modulation of emotional tone without any additional training or architectural modification.
  arXiv  Detail & Related papers  (2025-02-06T13:38:57Z)
• EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics [12.105216351739422]
  EmoDynamiX models the discourse dynamics between user fine-grained emotions and system strategies using a heterogeneous graph for better performance and transparency.<n> Experimental results on two ESC datasets show EmoDynamiX outperforms previous state-of-the-art methods with a significant margin.
  arXiv  Detail & Related papers  (2024-08-16T14:54:41Z)
• Self-Emotion Blended Dialogue Generation in Social Simulation Agents [18.781929161272853]
  This study explores how self-emotion affects the agents' behaviors in dialogue strategies and decision-making within a large language model (LLM)-driven simulation framework. The results show that incorporating self-emotion helps agents exhibit more human-like dialogue strategies. In a virtual simulation environment where agents have discussions on multiple topics, we show that self-emotion of agents can significantly influence the decision-making process of the agents.
  arXiv  Detail & Related papers  (2024-08-03T02:11:48Z)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)
• Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction [55.47134146639492]
  This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post. Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
  arXiv  Detail & Related papers  (2021-06-06T06:26:15Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.