PRISM: A Geometric Risk Bound for Decomposing Drift into Scale, Shape, and Head
A single base LLM now comes with dozens of post-training variants, quantized, LoRA-adapted, or distilled, and each has to be checked before release. Existing evaluations provide only a partial picture: benchmark scores and likelihood screens say that a variant has degraded, similarity scores such as CKA and SVCCA say how its features moved, and nothing connects the two. We connect them with one structural fact and one design choice: the prediction head is linear, so feature geometry reaches the loss, and we compare the two feature sets through an orthogonal map, which leaves the geometry being measured unchanged. From these we derive PRISM, a closed-form upper bound on the cross-entropy r...
| Authors | Chieh-Yen Lin, Shao-Hua Sun |
|---|---|
| Primary category | cs.CL |
| Published | 12 May 2026 |
| Last updated at source | 29 September 2026 |
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