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Output-Space Allocation Costs for Calibration-Guided LLM Compression: An Empiric

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1 SIGNALFIRST DETECTED 30 June 2026
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30 Jun
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5 May
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs

arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder

REPORT · WHAT · ArXiv CS.AIOPEN STORY
12 Aug
Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs

arXiv:2608.08506v1 Announce Type: new Abstract: Training-free low-rank compression frameworks have been gaining prominence for LLM compression given their effectiveness in

REPORT · WHAT · ArXiv CS.AIOPEN STORY
7 May
FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression

arXiv:2605.04084v1 Announce Type: cross Abstract: Compressing large language models (LLMs) for deployment on commodity GPUs remains challenging: conventional scalar quantization

REPORT · WHAT · ArXiv CS.AIOPEN STORY
9 Apr
HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference

arXiv:2604.05887v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is

REPORT · WHAT · ArXiv CS.AIOPEN STORY
25 Apr
Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations

arXiv:2604.21018v1 Announce Type: new Abstract: While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute

REPORT · WHERE · ArXiv CS.AIOPEN STORY
26 Mar
DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression

arXiv:2603.22324v1 Announce Type: cross Abstract: We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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