Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment
arXiv:2606.02322v1 Announce Type: new Abstract: In dynamic environments, large language models need to keep adapting to new tasks, but continual learning often suffers from forgetting, limited transfer, and vulnerability to adversarial perturbations. To address this, we present AdvCL, which repurposes adversarial perturbations as a geometric control signal for stable continual adaptation. AdvCL combines three plug-in modules: Intra-Smooth promotes local smoothness via small adversarial perturbations; Proto-Clip uses similarity clipping to prevent excessive alignment to current task prototype;
The increasing deployment of large language models in dynamic real-world environments necessitates robust continual learning strategies that can mitigate issues like catastrophic forgetting and adversarial vulnerabilities, driving innovation in this space.
This research outlines a novel approach to make AI models more stable, adaptive, and resilient against adversarial attacks, which are critical for reliable and secure AI deployment at scale.
The proposed AdvCL method offers a new paradigm for continual learning, repurposing adversarial perturbations as a beneficial 'geometric control signal' rather than solely a threat, potentially leading to more robust and actively aligning AI systems.
- · AI model developers
- · Cybersecurity researchers
- · Industries deploying AI in dynamic environments
- · Users of AI systems
- · Adversarial attack developers
AI systems will become more resilient to sudden changes in data distribution and active adversarial attacks.
Improved adversarial robustness could accelerate the adoption of autonomous AI agents in sensitive applications.
Enhanced self-correction and adaptation capabilities might reduce the need for constant human oversight in complex AI deployments, potentially accelerating the development of advanced AI agents.
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Read at arXiv cs.LG