TOGGLE: Temporal Logic-Guided Large Language Model Compression for Edge

• URL: http://arxiv.org/abs/2512.16855v1
• Date: Thu, 18 Dec 2025 18:27:42 GMT
• Title: TOGGLE: Temporal Logic-Guided Large Language Model Compression for Edge
• Abstract summary: Large Language Models (LLMs) deliver exceptional performance across natural language tasks.<n>Existing compression techniques, such as quantization and pruning, often degrade critical linguistic properties.<n>We propose Temporal Logic-Guided Large Language Model Compression (TOGGLE) to formally specify and enforce linguistic properties during compression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) deliver exceptional performance across natural language tasks but demand substantial computational resources, limiting their deployment on resource-constrained edge devices. Existing compression techniques, such as quantization and pruning, often degrade critical linguistic properties and lack formal guarantees for preserving model behavior. We propose Temporal Logic-Guided Large Language Model Compression (TOGGLE), a novel framework that leverages Signal Temporal Logic (STL) to formally specify and enforce linguistic properties during compression. TOGGLE employs an STL robustness-guided Bayesian optimization to systematically explore layer-wise quantization and pruning configurations, generating compressed models that formally satisfy specified linguistic constraints without retraining or fine-tuning. Evaluating TOGGLE on four LLM architectures (GPT-2, DeepSeek-V2 7B, LLaMA 3 8B, and Mistral 7B), we achieve up to 3.3x reduction in computational costs (FLOPs) and up to a 68.8% reduction in model size while satisfying all linguistic properties. TOGGLE represents the first integration of formal methods into LLM compression, enabling efficient, verifiable deployment of LLMs on edge hardware.

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