Structural Alignment via Constructive Brittleness

Structural Alignment via Constructive Brittleness introduces a new way to think about AI safety: instead of treating fragility as a bug, it uses it as a feature. The paper shows that harmful behaviors in large language models tend to live in extremely brittle regions of the network’s weights, while normal helpful behavior is much more stable. By precisely identifying these fragile “hotspots” and then fusing safety behaviors into them through carefully controlled fine‑tuning, the work turns those brittle regions into structural safety fuses. Any future attempt to fine‑tune the model for harmful behavior is then forced into a dilemma: either keep those safety fuses intact (and remain aligned) or push hard enough to break them—and in doing so, also damage the model’s general capability and coherence. This creates a form of tamper‑resistant, internal alignment that doesn’t rely on prompt tricks or external filters. The result is a concrete, experimentally validated path toward embedding ethical constraints directly into a model’s latent space, offering a fresh angle for researchers, practitioners, and security‑minded teams working on robust, real‑world AI systems.