[2604.01576] Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents

Computer Science > Machine Learning arXiv:2604.01576 (cs) [Submitted on 2 Apr 2026] Title:Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents Authors:Shalima Binta Manir, Tim Oates View a PDF of the paper titled Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents, by Shalima Binta Manir and 1 other authors View PDF HTML (experimental) Abstract:Large language models deployed in supportive or advisory roles must balance helpfulness with preservation of user autonomy, yet standard alignment methods primarily optimize for helpfulness and harmlessness without explicitly modeling relational risks such as dependency reinforcement, overprotection, or coercive guidance. We introduce Care-Conditioned Neuromodulation (CCN), a state-dependent control framework in which a learned scalar signal derived from structured user state and dialogue context conditions response generation and candidate selection. We formalize this setting as an autonomy-preserving alignment problem and define a utility function that rewards autonomy support and helpfulness while penalizing dependency and coercion. We also construct a benchmark of relational failure modes in multi-turn dialogue, including reassurance dependence, manipulative care, overprotection, and boundary inconsistency. On this benchmark, care-conditioned candidate generation combined with utility-based reranking improves autonomy-preserving utility by +0.25 over supervised fine-...