Position: Capability Control Should be a Separate Goal From Alignment
This addresses the problem of controlling model capabilities to prevent misuse in AI systems, but it is a position paper with incremental conceptual framing.
The paper argues that capability control, which imposes hard limits on permissible model behaviors, should be treated as a separate goal from alignment, and proposes a defense-in-depth approach across data, learning, and system layers to address misuse and failures in foundation models.
Foundation models are trained on broad data distributions, yielding generalist capabilities that enable many downstream applications but also expand the space of potential misuse and failures. This position paper argues that capability control -- imposing restrictions on permissible model behavior -- should be treated as a distinct goal from alignment. While alignment is often context and preference-driven, capability control aims to impose hard operational limits on permissible behaviors, including under adversarial elicitation. We organize capability control mechanisms across the model lifecycle into three layers: (i) data-based control of the training distribution, (ii) learning-based control via weight- or representation-level interventions, and (iii) system-based control via post-deployment guardrails over inputs, outputs, and actions. Because each layer has characteristic failure modes when used in isolation, we advocate for a defense-in-depth approach that composes complementary controls across the full stack. We further outline key open challenges in achieving such control, including the dual-use nature of knowledge and compositional generalization.