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arXiv · 2610.36143

Program-Level Adversarial Co-Adaptive Self-Play for Robotic Security

Leo Benaharon·Chi Zhang·Wei Zheng·Weifan Zhang·Yifan Wei
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Abstract

Coding agents can adapt robotic software by revising executable programs, but successful responses to an evolving opponent may not remain effective. We present a framework for program-level adversarial co-adaptive self-play in which coding agents generate attacks and defenses from tested opponent code and mission feedback, without model fine-tuning. Environment-specific interfaces support external programs for autonomous driving and SDK-level defenses for humanoid locomotion. Two eight-generation case studies reveal concrete software responses: a driving defender introduces direct-actuation recovery, while a humanoid defender revises incoming-state validation. These responses provide local task recovery but do not establish sustained robustness. The framework makes such adaptation and limitations observable through executable artifacts and task-level evidence.

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Leo Benaharon, Chi Zhang, Wei Zheng, Weifan Zhang, Yifan Wei. 2026-10-26. Program-Level Adversarial Co-Adaptive Self-Play for Robotic Security. https://arxiv.org/abs/2610.36143

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