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arXiv CS AI

techLeft

9h ago

Reasoning-Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deploymenttranslating…

arXiv:2609.05512v1 Announce Type: new 
Abstract: Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all components, risking damage to critical reasoning circuits. We present a…

Factuality

90/100

Political Bias

Left

Ownership

Cornell University

Country

USA

Region

North America

Language

English

Keywords#Reasoning#Reasoning Circuits#Compression#Deployment#Energy-Efficient

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Reasoning-Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deployment | Pulse Aggregator