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stat.ML2026
Exact Finite Attention Responses From RoPE Derivatives
Julie Huang, Maggie Chlon, Gregory Gutin +1
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 deri…
stat.ML2026
RoPE attention is an exact forward-pass gradient step with softmax intact
Julie Huang, Maggie Chlon, Leon Chlon
We derive an exact gradient-step representation of the RoPE-softmax forward pass. For every deterministic RoPE-softmax attention head with arbitrary affine projection weights, we c…
stat.ML2026
Attention Deficits in Language Models: Causal Explanations for Procedural Hallucinations
Ahmed Karim, Fatima Sheaib, Zein Khamis +3
Large language models can follow complex procedures yet fail at a seemingly trivial final step: reporting a value they themselves computed moments earlier. We study this phenomenon…