Post-Thinking in NPC Dialogue: A Paradigm for Reflective Character Models

Abstract

Immersive and believable NPC dialogue requires characters that feel intentional. They remember specific information about themselves, follow through on their goals, and stay true to their personalities across long conversations. We introduce Post-Thinking, a technique that maintains a rolling reflection trace across chat turns. After each response, effectively in the dead time between LLM queries, the model generates a trace reflecting its current goals, emotional state, and narrative intentions. This trace is kept in context and conditions the next response, directing the conversation while designed to add zero perceivable latency to the end user. Critically, each trace is generated with the previous n reflection traces still visible in context, allowing the character’s inner state to compound and evolve naturally throughout extended conversations. As a preliminary study, we synthetically annotated conversations from seed datasets and interviewed human experts to assess their quality in preparation for fine-tuning. We expect the reflection generation pass to help actively ground the character as it encourages the model to explicitly surface aspects of the character definition most relevant to the current moment, counteracting the prompt drift that typically degrades consistency over long exchanges.