CourtGuard: A Model-Agnostic Framework for Zero-Shot Policy Adaptation in LLM Safety
arXiv:2602.22557v1 Announce Type: new Abstract: Current safety mechanisms for Large Language Models (LLMs) rely heavily on static, fine-tuned classifiers that suffer from adaptation rigidity, the inability to enforce new governance rules without expensive retraining. To address this, we introduce CourtGuard, a retrieval-augmented multi-agent framework that reimagines safety evaluation as Evidentiary Debate. By orchestrating an adversarial debate grounded in external policy documents, CourtGuard achieves state-of-the-art performance across 7 safety benchmarks, outperforming dedicated policy-following baselines without fine-tuning. Beyond standard metrics, we highlight two critical capabilities: (1) Zero-Shot Adaptability, where our framework successfully generalized to an out-of-domain Wikipedia Vandalism task (achieving 90\% accuracy) by swapping the reference policy; and (2) Automated Data Curation and Auditing, where we leveraged CourtGuard to curate and audit nine novel datasets of
arXiv:2602.22557v1 Announce Type: new Abstract: Current safety mechanisms for Large Language Models (LLMs) rely heavily on static, fine-tuned classifiers that suffer from adaptation rigidity, the inability to enforce new governance rules without expensive retraining. To address this, we introduce CourtGuard, a retrieval-augmented multi-agent framework that reimagines safety evaluation as Evidentiary Debate. By orchestrating an adversarial debate grounded in external policy documents, CourtGuard achieves state-of-the-art performance across 7 safety benchmarks, outperforming dedicated policy-following baselines without fine-tuning. Beyond standard metrics, we highlight two critical capabilities: (1) Zero-Shot Adaptability, where our framework successfully generalized to an out-of-domain Wikipedia Vandalism task (achieving 90\% accuracy) by swapping the reference policy; and (2) Automated Data Curation and Auditing, where we leveraged CourtGuard to curate and audit nine novel datasets of sophisticated adversarial attacks. Our results demonstrate that decoupling safety logic from model weights offers a robust, interpretable, and adaptable path for meeting current and future regulatory requirements in AI governance.
Executive Summary
This article presents CourtGuard, a novel framework for zero-shot policy adaptation in Large Language Model (LLM) safety. By reimagining safety evaluation as 'Evidentiary Debate', CourtGuard achieves state-of-the-art performance across 7 safety benchmarks without fine-tuning. The framework's ability to adapt to new policies and datasets without retraining offers a promising solution to the challenges of AI governance. The authors demonstrate CourtGuard's potential through two critical capabilities: zero-shot adaptability and automated data curation and auditing. These results suggest that decoupling safety logic from model weights could be a robust and adaptable path forward for meeting regulatory requirements.
Key Points
- ▸ CourtGuard is a model-agnostic framework for zero-shot policy adaptation in LLM safety.
- ▸ The framework reimagines safety evaluation as 'Evidentiary Debate', achieving state-of-the-art performance without fine-tuning.
- ▸ CourtGuard demonstrates zero-shot adaptability and automated data curation and auditing capabilities.
Merits
Strength
The framework's ability to adapt to new policies and datasets without retraining offers a promising solution to the challenges of AI governance.
Demerits
Limitation
The article does not provide a detailed explanation of the framework's potential vulnerabilities or attack surfaces.
Expert Commentary
The article presents a compelling case for the potential of CourtGuard as a solution to the challenges of LLM safety. However, further research is needed to fully explore the framework's capabilities and limitations. Additionally, the article's focus on zero-shot adaptability and automated data curation and auditing raises important questions about the role of human oversight in AI governance. As the field continues to evolve, it will be essential to balance the potential benefits of frameworks like CourtGuard with the need for robust and transparent decision-making processes.
Recommendations
- ✓ Future research should focus on developing more robust and scalable frameworks for AI safety evaluation.
- ✓ Policymakers should consider the implications of decoupling safety logic from model weights and develop regulatory frameworks that prioritize transparency and accountability.
