Aleksandr Beznosikov
2 indexed papers
Recent (6 mo)
2With code
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226
Top categories
AI×2ML×2
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Research Timeline
2026
HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization
HARP introduces a novel, adaptive, learnable orthogonal processor that significantly improves the robustness and accuracy of extreme low-bit LLM quantization compared to fixed methods.
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
The paper analyzes the failure modes of aggressive 2-bit quantization in large reasoning models, proposing lightweight controls like FP16 planning and loop rescue to restore accuracy and achieve practical end-to-end speedup.
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Papers
cs.AIcs.LGRecentJun 1, 2026
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
The paper analyzes the failure modes of aggressive 2-bit quantization in large reasoning models, proposing lightweight controls like FP16 planning and loop rescue to restore accuracy and achieve pract…
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cs.LGcs.AIRecentMay 28, 2026