MindCompletion: Learning Latent Human Intent from Behavioral Continuations

Language models are increasingly deployed as proactive writing assistants, suggesting continuations as the writer drafts. Effective writing assistance demands more than fluency: the assistant must anticipate what the writer intends to say next, a behavioral judgment that standard language model training is not designed to capture. To address this, we propose process-continuation learning, a paradigm that uses naturally-occurring expert writing traces as behavioral reward for reinforcement learning, operationalized through a structured user agent without per-instance annotation. To stabilize optimization, we schedule a curriculum reward progressing from local coherence to semantic alignment, and construct context-variant trajectories covering prefix-only and history-conditioned settings. The trained policy doubles acceptance rate on a co-writing benchmark, reducing the writer's coordination burden, and matches strong closed-source assistants. These gains further generalize beyond interactive writing, with emergent context-aware reasoning and consistent improvements on mathematical reasoning and instruction following, reflecting a stronger capacity to anticipate user intent.