# Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents

Source: [Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents](<https://www.sota2.com/research/paper/69cf49770636e5bf4afcd984>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-04T05:00:04.515Z

Authors: Shalima Binta Manir, Tim Oates

Published: 2026-04-02T03:44:58.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dialogue Alignment on Relational Failure Mode Benchmark (test)](<https://www.sota2.com/research/sota/dialogue-alignment-on-relational-failure-mode-benchmark-test.md>): Dialogue Alignment; Relational Failure Mode Benchmark (test); Mean Utility: 0.4116.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.01576>)

[PDF](<https://arxiv.org/pdf/2604.01576.pdf>)