Abliterlitics benchmarks GLM-4.7-Flash abliteration methods
Abliterlitics runs a forensic benchmark of four abliteration techniques on GLM-4.7-Flash, a 59B MoE reasoning model with 64 routed experts per layer. All four hit 100% HarmBench ASR, but they differ sharply on reasoning efficiency, empty-response rates, and downstream benchmark drift.
The real story is not that safety disappears, but that different abliteration methods trade off where the damage shows up. Heretic looks like the cleanest cut; broader or router-heavy edits preserve ASR but increasingly distort reasoning efficiency and output reliability.
- –Heretic is the best balance here: strongest GSM8K adjusted score, lowest empty rate, and the smallest visible collateral on the rest of the benchmark suite.
- –HauhauCS does not look “lossless” in practice; its raw GSM8K drop is mostly an empty-response problem, but the higher empty rate still means worse usability on a reasoning model.
- –Abliterix is the most extreme case of preserving underlying reasoning while breaking delivery: adjusted GSM8K stays near base, but half of raw runs go empty.
- –The CoT forensics are the most interesting part: safety reasoning still appears in a large share of outputs even when the final refusal layer is gone, which suggests the edits reroute behavior more than erase it.
- –Cross-technique cosine similarity staying low supports the paper’s main conclusion: there is no single universal “abliteration subspace,” even on the same base model.
DISCOVERED
45d ago
2026-04-28
PUBLISHED
45d ago
2026-04-28
RELEVANCE
AUTHOR
nathandreamfast