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.
The paper introduces PanicCognitivePath (PCP), a model that predicts the timing of panic emotional arousal by integrating appraisal emotion theory into a Belief‑Desire‑Emotion‑Intention (BDEI) framework. PCP uses a Psychological Safety Distance (PSD) model to fuse physical, social, cognitive, and informational signals into a unified risk metric, and confines large language models to a single parameter‑estimation step to reduce hallucinations. Experiments on Hurricane Sandy data show PCP improves individual prediction accuracy by 10.68% and reduces peak count error to 7.07%.
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.
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:2608. 07480v1 Announce Type: new Abstract: Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction.
arXiv:2606. 18259v1 Announce Type: cross Abstract: AI agents that plan, retain memory across sessions, invoke external tools and act with partial autonomy are transforming human--AI collaboration.
arXiv:2607. 07824v1 Announce Type: cross Abstract: Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling.
The article introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, along with representational selection mechanisms that govern adaptive responses. The model binds exteroceptive and interoceptive signals, bodily-motivational context, and memory traces into associative structures called semblions, enabling motivated learning that incorporates need thresholds, goal generation, and regulatory constraints.
arXiv:2608. 06425v1 Announce Type: cross Abstract: Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outputs, and affective meaning is context-dependent, requiring conflicting cues to be reconciled rather than mapped directly to labels.
arXiv:2606. 05411v1 Announce Type: new Abstract: Motivational architectures in cognitive AI have largely been designed for physical agents regulating bodily needs.
The paper introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems, describing how the system learns to maintain homeostasis via motivated learning—a reinforcement‑learning variant driven by internal motivations. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, and details how exteroceptive and interoceptive signals, bodily context, and memory traces form associative structures called semblions that compete for processing and reconstruction. The model incorporates need thresholds, goal dynamics, bodily state, resource constraints, and action uncertainty, and posits global affect as a central control signal modulating learning rate, representational valence, and exploration‑exploitation balance.
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.