Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind

User interactions with language models vary due to static properties of the user (trait) and the specific context of the interaction (state).However, existing persona datasets (like Per-sonaChat, PANDORA etc.) capture only trait, and ignore the impact of state.We introduce Chameleon, a dataset of 5,001 contextual psychological profiles from 1,667 Reddit users, each measured across multiple contexts.Using the Chameleon dataset, we present three key findings.First, inspired by Latent State-Trait theory, we decompose variance and find that 74% is within-person(state) while only 26% is between-person (trait).Second, we find that LLMs are state-blind: they focus on trait only, and produce similar responses regardless of state.Third, we find that reward models react to user state, but inconsistently: different models favor or penalize the same users in opposite directions.We release Chameleon to support research on affective computing, personalized dialogue, and RLHF alignment.