mlsanityData to 14 Oct 2026

Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth

arXiv 2610.20773 ↗First posted 14 Oct 2026cs.LG stat.MLcited by 0

On the map: Efficient LLM inference and serving → Early exit and edge inference

Abstract (arXiv)

Calibrating a conformal prediction set at each early exit does not preserve coverage once a data-dependent exit is selected. We construct one terminal path score from prefix maxima, calibrate a single split-conformal quantile, and return nested prefix sets that all contain the valid terminal set. The guarantee is selection-safe without an alpha/L correction and holds simultaneously for every post-calibration label-free stopping policy that preserves terminal-set containment. We also bound the efficiency cost of the path maximum by the empirical tail of the early-over-terminal score excess. Across eight encoder task-backbone cells, pathwise selection attains macro coverage 0.901 versus 0.814 for naive layerwise selection, with average set size 1.41 and latency fraction 0.71. On CIFAR-100 with a 24-exit ViT-L/16 (n_cal=500), first-eligible stopping at budget b=4 attains coverage 0.902, set size 2.76, and latency fraction 0.57, and the same calibration supports deployment-time budget changes without refitting. In a joint exit-count/calibration-size sweep at n=100, pathwise set size moves only from 3.02 to 3.11 as exits increase from 4 to 24, while Bonferroni degenerates to the full label space for 12 or more exits. Paired comparison against MinSE valid selection at (n,L)=(100,24) gives a set-size difference of 0.80 [0.47, 1.13]. Full-vocabulary decoder experiments remain a negative boundary, with sets of 7,162-9,620 tokens. Pathwise calibration is most useful when candidate exits are many relative to calibration size, when budgets change after calibration, and when shallow heads return actionable sets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving. First posted on arXiv 14 Oct 2026.

Builds on

No reference list yet.

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization
  2. 2026SAFE-KD: Risk-Controlled Early-Exit Distillation for Vision Backbones
  3. 2024Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
  4. 2026VGAS: Variance-Reduced Guidance and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion
  5. 2026Available Guardrails: Certifying Selective Prediction across ML Systems
  6. 2026Robust Conformalized Selection with Noisy Responses
  7. 2026Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance
  8. 2026SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise