On Emotion-Sensitive Decision Making of Small Language Model Agents
Researchers introduce a framework for studying how emotional states affect decision-making in small language models (SLMs) used as autonomous agents. Using activation steering techniques grounded in real-world emotion-eliciting texts, they benchmark SLMs across game-theoretic scenarios and find that emotional perturbations systematically influence strategic choices, though behaviors often remain unstable and misaligned with human patterns.