Academic

Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents

arXiv:2604.01576v1 Announce Type: new 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-co

S
Shalima Binta Manir, Tim Oates · · 1 min read · 1 views
#cs.LG

arXiv:2604.01576v1 Announce Type: new 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-tuning and +0.07 over preference optimization baselines while maintaining comparable supportiveness. Pilot human evaluation and zero-shot transfer to real emotional-support conversations show directional agreement with automated metrics. These results suggest that state-dependent control combined with utility-based selection is a practical approach to multi-objective alignment in autonomy-sensitive dialogue.

Executive Summary

This article introduces Care-Conditioned Neuromodulation (CCN), a state-dependent control framework for supportive agents to balance helpfulness with user autonomy. CCN learns a scalar signal from user state and dialogue context to condition response generation and selection. The authors formalize this setting as an autonomy-preserving alignment problem, defining a utility function that rewards autonomy support and helpfulness while penalizing dependency and coercion. Experimental results show improved autonomy-preserving utility and comparable supportiveness compared to baseline methods. Human evaluation and zero-shot transfer to real conversations confirm the effectiveness of CCN. This approach has significant implications for developing autonomy-sensitive dialogue systems that prioritize both helpfulness and user agency.

Key Points

  • ▸ Care-Conditioned Neuromodulation (CCN) introduces a state-dependent control framework for autonomy-preserving supportive dialogue agents.
  • ▸ CCN learns a scalar signal from user state and dialogue context to condition response generation and selection.
  • ▸ The authors formalize the CCN setting as an autonomy-preserving alignment problem with a utility function that balances helpfulness and autonomy support.

Merits

Strength

The article provides a comprehensive formalization of the autonomy-preserving alignment problem, addressing the limitations of standard alignment methods.

Strength

The experimental results demonstrate the effectiveness of CCN in improving autonomy-preserving utility and supportiveness.

Demerits

Limitation

The article assumes a specific dialogue context and user state structure, which may not generalize to all scenarios.

Expert Commentary

While the article makes significant contributions to the field of dialogue systems, its experimental results rely on a narrow benchmark of relational failure modes. Future research should aim to extend the scope of these experiments to more diverse and realistic scenarios. Additionally, further investigation into the generalizability of the CCN framework to different dialogue contexts and user state structures is warranted. Nevertheless, the study provides valuable insights into the development of autonomy-sensitive dialogue systems and highlights the importance of prioritizing user agency in AI design.

Recommendations

  • ✓ Future research should aim to extend the scope of the experimental results to more diverse and realistic scenarios.
  • ✓ Investigation into the generalizability of the CCN framework to different dialogue contexts and user state structures is necessary.

Sources

Original: arXiv - cs.LG

Related Articles