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This paper challenges the 'Locate-then-Update' paradigm in LLM post-training by demonstrating that static mechanistic localization is insufficient due to the dynamic evolution of neural circuits during fine-tuning. It introduces new metrics to analyze circuit stability and proposes the need for predictive frameworks in mechanistic localization.
ASGuard is a mechanistically-informed defense framework that mitigates jailbreaking attacks on LLMs by identifying vulnerable attention heads through circuit analysis and applying targeted activation scaling and fine-tuning to improve refusal behavior robustness while preserving model capabilities.
OpenAI researchers present methods for training sparse neural networks that are easier to interpret by forcing most weights to zero, enabling the discovery of small, disentangled circuits that can explain model behavior while maintaining performance. This work aims to advance mechanistic interpretability as a complement to post-hoc analysis of dense networks and support AI safety goals.