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cs.LG2026
FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy
Qingyao Yang, Runming Yang, He Xiao +7
While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlooks the necessity of specializ…
cs.LG2025
PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
He Xiao, Runming Yang, Qingyao Yang +6
Post-training quantization (PTQ) of large language models (LLMs) to extremely low bit-widths remains challenging due to the fundamental trade-off between computational efficiency a…
cs.LG2025
Quantization Meets Reasoning: Exploring and Mitigating Degradation of Low-Bit LLMs in Mathematical Reasoning
Zhen Li, Yupeng Su, Songmiao Wang +8
Low-bit post-training quantization (PTQ) is a practical route to deploy reasoning-capable LLMs under tight memory and latency budgets, yet it can markedly impair mathematical reaso…