AI RESEARCH
Revealing Behavioral Plasticity in Large Language Models: A Token-Conditional Perspective
arXiv CS.LG
•
ArXi:2603.08398v1 Announce Type: cross In this work, we reveal that Large Language Models (LLMs) possess intrinsic behavioral plasticity-akin to chameleons adapting their coloration to environmental cues-that can be exposed through token-conditional generation and stabilized via reinforcement learning. Specifically, by conditioning generation on carefully selected token prefixes sampled from responses exhibiting desired behaviors, LLMs seamlessly adapt their behavioral modes at inference time (e.g., switching from step-by-step reasoning to direct answering) without re.