Tracing the Persona Circuit: How Large Language Models Encode and Express Character Traits

摘要

Large Language Models (LLMs) demonstrate remarkable potential in role-playing tasks but frequently suffer from personality decay—termed "Out-of-Character" (OOC) behavior—during prolonged interactions. While heuristic strategies exist to align model behaviors, the internal computational dynamics driving personality expression remain opaque. A fundamental barrier to decoding these mechanisms is a *metric gap*: while standard causal attribution paradigms target atomic, single-token outcomes, personality manifests as a holistic, multi-token behavioral tendency. We bridge this gap via the *Latent Persona Vector*, a differentiable proxy enabling the first fine-grained causal tracing of personality circuits. This metric reveals a structured "Preparation-Establishment-Expression" dynamic and identifies the mechanistic root of OOC behavior not as knowledge erasure, but as *generic prior dominance*. Specifically, we find that intrinsic assistant priors suppress emergent persona intents during the critical "Establishment" phase. Guided by this diagnosis, we propose surgically recalibrating the signal magnitude in fewer than 5% of attention heads. This targeted intervention effectively counteracts prior suppression, significantly restoring character consistency while preserving general reasoning capabilities.