Exact Finite Attention Responses From RoPE Derivatives
We derive exact local responses for attention interventions, allowing candidate edits to be scored from a cached baseline and one backward pass. The starting point is the RoPE derivative $\partial_p z(p) = A z(p)$: its integral gives the finite positional displacement, which we carry through the softmax without linearising either rotation or normalisation. The resulting predictions achieve 95.36--96.52% sign accuracy across 92,160 executed positional edits on 768 held-out prompt sets, reducing answer-margin MAE by 73.6--82.5% against the positional Jacobian and by 36.2--50.9% against zero. For simultaneous key and value edits, the same divided-difference calculus isolates the interaction ...
| Authors | Julie Huang, Maggie Chlon, Gregory Gutin et al. |
|---|---|
| Primary category | stat.ML |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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