Why Training-Free Token Reduction Collapses: The Inherent Instability of Pairwise Scoring Signals

• URL: http://arxiv.org/abs/2604.16745v1
• Date: Fri, 17 Apr 2026 23:26:27 GMT
• Title: Why Training-Free Token Reduction Collapses: The Inherent Instability of Pairwise Scoring Signals
• Abstract summary: Training-free token reduction methods for Vision Transformers employ different scoring mechanisms, yet they share a closely matched cliff-like collapse at high compression.<n>We develop a diagnostic framework with two tools, ranking consistency $_s$ and off-diagonal correlation $_textoff$, that decomposes the collapse into (1)a signal-agnostic error amplifier inherent to layer-wise reduction.<n>We construct CATIS as a constructive validation: unary signals raise the trigger threshold, triage suppresses the gain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a closely matched cliff-like collapse at high compression. This paper explains \emph{why}. We develop a diagnostic framework with two tools, ranking consistency $ρ_s$ and off-diagonal correlation $ρ_\text{off}$, that decomposes the collapse into (1)a signal-agnostic error amplifier inherent to layer-wise reduction, predicting convex Pareto curves and $r_{\text{crit}} \propto 1/L$; and (2)shared reliance on \emph{pairwise} similarity signals whose ranking consistency degrades from $ρ_s{=}0.88$ to $0.27$ in deep layers. Pairwise rankings are inherently unstable ($O(N_p^2)$ joint perturbations) while unary signals enjoy greater stability ($O(N_p)$ perturbations, CLT). From three design principles derived from this diagnosis, we construct CATIS as a constructive validation: unary signals raise the trigger threshold, triage suppresses the gain. On ViT-Large at 63% FLOPs reduction, CATIS retains 96.9% of vanilla accuracy (81.0%) on ImageNet-1K where all baselines collapse to 43--65%.

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