Geometric fragility score from the safety subspace
Collect hidden states on harmful and harmless prompts, recover the low-rank safety subspace per layer, and compress its geometry into one score that ranks aligned checkpoints by how easily benign fine-tuning will erase refusal, so the fragi
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Collect hidden states on harmful and harmless prompts, recover the low-rank safety subspace per layer, and compress its geometry into one score that ranks aligned checkpoints by how easily benign fine-tuning will erase refusal, so the fragi Filed by the drafting stage from Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations (2606.22676) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Direction-ablation causal test on four models (non-overlapping 95% CIs), linear-probe accuracy at least 0.90 for the subspace, and a LoRA-fragility experiment where the score's top pick retained the most refusal; twenty-one models across six recipes.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Code
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Sources
Whether a technique works is a claim. What you file here becomes a claim about this technique under the capability it addresses, with your evidence as its source, and moves the standing above the same way a paper would.