Hugging Face Trending Papers August 6, 2026

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

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Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs.

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arXiv AI

Aug 7

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

arXiv:2608. 05926v1 Announce Type: cross Abstract: Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks.

By Guanqiao Qu, Shuo Chen, Qian Chen, Kin K. Leung, Xianhao Chen

llms efficiency

arXiv AI

1d ago

S2-MoE: Enabling Efficient Self-Speculative Decoding for Mixture-of-Experts on Edge Devices

arXiv:2608. 15018v1 Announce Type: new Abstract: Deploying large language models (LLMs) for inference on edge devices is challenging due to severe memory and bandwidth constraints.

By Haochen Huang, Shengxuan Qiu, Meng Li

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arXiv Machine Learning

Aug 4

Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO

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By Ngoc Hung Nguyen, Bjorn Landfeldt

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arXiv AI

5d ago

SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference

arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.

By Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal

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arXiv AI

Jun 11

Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

arXiv:2509. 23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.

By Mingyi Luo, Ruichen Zhang, Xiangwang Hou, Jun Du, Chunxiao Jiang, Yong Ren, Shiwen Mao

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arXiv Machine Learning

Jun 4

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arXiv:2603. 16867v2 Announce Type: replace Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment.

By Yelysei Bondarenko, Thomas Hehn, Rob Hesselink, Romain Lepert, Fabio Valerio Massoli, Evgeny Mironov, Leyla Mirvakhabova, Tribhuvanesh Orekondy, Spyridon Stasis, Andrey Kuzmin, Anna Kuzina, Markus Nagel, Ankita Nayak, Corrado Rainone, Ork de Rooij, Paul N Whatmough, Arash Behboodi, Babak Ehteshami Bejnordi

llms reinforcement-learning fine-tuning efficiency benchmarks

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