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20232026
most citedThe Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

37 citations · 114 across the 14 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding

Shuang Liang, Hao Mark Chen, Zhiwen Mo +4

Speculative decoding verifies a tree of draft tokens in one target-model forward pass. For a mixture-of-experts (MoE) target, however, parallel verification can activate the union…

cs.LG2025

FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning

Hao Mark Chen, Zhiwen Mo, Guanxi Lu +4

Recent advances in reasoning Large Language Models (LLMs) are driving the emergence of agentic AI systems. Edge deployment of LLM agents near end users is increasingly necessary to…

cs.LG2025

SeerAttention-R: Sparse Attention Adaptation for Long Reasoning

Yizhao Gao, Shuming Guo, Shijie Cao +12

We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the…

cs.LG2025

TileLang: A Composable Tiled Programming Model for AI Systems

Lei Wang, Yu Cheng, Yining Shi +8

Modern AI workloads rely heavily on optimized computing kernels for both training and inference. These AI kernels follow well-defined data-flow patterns, such as moving tiles betwe…

cs.LG2025★ 1 cited

WaferLLM: Large Language Model Inference at Wafer Scale

Congjie He, Yeqi Huang, Pei Mu +5

Emerging AI accelerators increasingly adopt wafer-scale manufacturing technologies, integrating hundreds of thousands of AI cores in a mesh architecture with large distributed on-c…