RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs

Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable to adversarial misuse. While prior work has shown that safety-relevant features are encoded in structured representations within the model's activation space, how these representations change during fine-tuning and why alignment degrades remains poorly understood. In this work, we investigate the representation-level mechanisms underlying alignment

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ethics.ai (3 May 2026), “RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs,” evidence record 5045, https://ethics.ai/record/5045 (originally published by arXiv).

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