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• Standard alignment methods for large language models primarily optimize for helpfulness and harmlessness without explicitly modeling relational risks like dependency reinforcement or coercive guidance.limitation 95%
• Care-Conditioned Neuromodulation (CCN) is a state-dependent control framework where a learned scalar signal conditions response generation and candidate selection.method 90%
• The paper formalizes the setting as an autonomy-preserving alignment problem and defines a utility function that rewards autonomy support and helpfulness while penalizing dependency and coercion.technical 90%
• The paper constructs a benchmark of relational failure modes in multi-turn dialogue, including reassurance dependence, manipulative care, overprotection, and boundary inconsistency.technical 90%
• On the constructed benchmark, care-conditioned candidate generation with utility-based reranking improves autonomy-preserving utility by +0.25 over supervised fine-tuning and +0.07 over preference optimization baselines.result 85%
• The proposed method maintains comparable supportiveness to baselines while improving autonomy-preserving utility.result 80%
• Pilot human evaluation and zero-shot transfer to real emotional-support conversations show directional agreement with automated metrics.result 75%
• State-dependent control combined with utility-based selection is a practical approach to multi-objective alignment in autonomy-sensitive dialogue.technical 70%

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