arXiv AI
Jun 26
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs
arXiv:2606. 26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.
Do Large Language Models Have Emotions?
arXiv:2606. 14742v1 Announce Type: cross Abstract: Do LLMs have emotions?
Related stories
arXiv AI
Jul 22
Semantic Primes as Explanans for Emotion in Large Language Models
arXiv:2607. 18691v1 Announce Type: new Abstract: Progresses have been made on understanding emotion mechanisms of large language models (LLMs).
arXiv AI
6d ago
TEMPER: Testing Emotional Perturbation in Quantitative Reasoning
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
arXiv AI
Jun 12
Emergence of Hierarchical Emotion Organization in Large Language Models
arXiv:2507. 10599v2 Announce Type: replace-cross Abstract: As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment.
arXiv Machine Learning
Jun 9
Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework
arXiv:2606. 07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect.
arXiv AI
Jul 15
Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
arXiv:2607. 12631v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modulate their decision-making becomes critical.
Hugging Face Trending Papers
Aug 10
Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution
Skill-based LLM agents select reusable procedures from an external library to solve complex tasks, yet their routing decisions rely entirely on text-level signals such as task descriptions, verbal reflections, and experience-derived rules, while the model's own internal representational state remains unobserved. Recent interpretability work has shown that LLMs maintain linear emotion representations that causally influence behavior; however, these representations have been exploited only for post-hoc analysis or direct output steering, and have not been used to inform agent-level decision-making.
arXiv AI
Jul 29
How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation
arXiv:2607. 25140v1 Announce Type: new Abstract: This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and appraise one another.
arXiv AI
Jul 22
Associative Emotional Learning in Convolutional Neural Networks
arXiv:2607. 19327v1 Announce Type: new Abstract: Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli.
arXiv AI
Jul 22
Why Do Vision Language Models Struggle To Recognize Human Emotions?
arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
arXiv AI
Aug 11
Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution
arXiv:2608. 09248v1 Announce Type: new Abstract: Skill-based LLM agents select reusable procedures from an external library to solve complex tasks, yet their routing decisions rely entirely on text-level signals such as task descriptions, verbal reflections, and experience-derived rules, while the model's own internal representational state remains unobserved.
arXiv AI
Jun 2
Bayesian Spectral Emotion Transition Discovery from Multi-Annotator Disagreement
arXiv:2606. 01906v1 Announce Type: new Abstract: Emotions evolve through the dynamics of conversation, and understanding their transition structure is foundational to applications ranging from mental-health screening to dialogue systems.