Qualcomm AI uses entropy and CKA to pick layers, beating EAGLE3 defaults in speculative decoding
Qualcomm-AI-Research · hf · 2026-10-12
Qualcomm AI Research and Stanford present a principled framework for selecting multi-layer hidden states in speculative decoding.
- Existing methods rely on fixed, position-based layer choices (e.g., EAGLE3's default [2, N//2, N-3]); optimal selection is a combinatorial problem.
- The framework uses two complementary criteria: informativeness (matrix-entropy analysis) and diversity (centered kernel alignment), plus token-entropy filtering to drop low-predictive-entropy hidden states.
- Layer-wise analysis reveals distinct informational and geometric compartments within decoder representations.
- Selected layer sets outperform fixed baselines in EAGLE3-style speculative decoding while explaining why some defaults remain competitive.
The paper offers a practical recipe for layer selection extendable to related applications.
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