Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models

• URL: http://arxiv.org/abs/2607.08991v1
• Date: Thu, 09 Jul 2026 23:40:36 GMT
• Title: Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models
• Abstract summary: Efficient inference in Large Language Models (LLMs) requires deciding where computation can be reduced while preserving model quality.<n>We study this problem through multilayer perceptron (MLP) activation sparsification and token-level conditional routing.
• Score: 2.593510481959727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient inference in Large Language Models (LLMs) requires deciding where computation can be reduced while preserving model quality. We study this problem through multilayer perceptron (MLP) activation sparsification and token-level conditional routing. We first propose Sensitivity-Aware Thresholding for Sparsity (SATS), a threshold calibration method to choose layerwise gate thresholds using a local MLP output sensitivity proxy rather than calibrating thresholds directly from activation percentiles. While SATS retains the existing mechanism of sparsifying MLP activations by thresholding gate activations, it replaces percentile-based calibration with a sensitivity-aware selection rule. We then introduce a lightweight token routing framework that dynamically selects between a base path and a modified path on a per-token basis, rather than applying the modified computation uniformly to all tokens. We evaluate both methods on multiple recent open-weight LLMs. Our results show that SATS improves over the threshold-based sparsification baseline at matched actual sparsity and that token routing yields a more favorable quality-throughput trade-off than static activation modification baselines. Overall, our results suggest that improved threshold calibration and token routing can improve the quality-throughput trade-off in LLMs.

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