GLASS: Test-Time Acceleration for LLMs via Global-Local Neural Importance Aggregation

• URL: http://arxiv.org/abs/2508.14302v1
• Date: Tue, 19 Aug 2025 22:50:20 GMT
• Title: GLASS: Test-Time Acceleration for LLMs via Global-Local Neural Importance Aggregation
• Authors: Amirmohsen Sattarifard, Sepehr Lavasani, Ehsan Imani, Kunlin Zhang, Hanlin Xu, Fengyu Sun, Negar Hassanpour, Chao Gao,
• Abstract summary: We introduce A/I-GLASS: Activation- and Impact-based Global-Local neural importance aggregation for feed-forward network SparSification.<n> Empirical results across multiple Large Language Models (LLMs) and benchmarks demonstrate that GLASS significantly outperforms prior training-free methods.
• Score: 12.921040231832082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying Large Language Models (LLMs) on edge hardware demands aggressive, prompt-aware dynamic pruning to reduce computation without degrading quality. Static or predictor-based schemes either lock in a single sparsity pattern or incur extra runtime overhead, and recent zero-shot methods that rely on statistics from a single prompt fail on short prompt and/or long generation scenarios. We introduce A/I-GLASS: Activation- and Impact-based Global-Local neural importance Aggregation for feed-forward network SparSification, two training-free methods that dynamically select FFN units using a rank-aggregation of prompt local and model-intrinsic global neuron statistics. Empirical results across multiple LLMs and benchmarks demonstrate that GLASS significantly outperforms prior training-free methods, particularly in challenging long-form generation scenarios, without relying on auxiliary predictors or adding any inference overhead.

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