arXiv AI June 29, 2026 By Yiheng Tao, Yihe Zhang, Matthew Dearing, Xin Wang, Yuping Fan, Michael E. Papka, Zhiling Lan

Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank

arXiv:2510. 03243v3 Announce Type: replace-cross Abstract: Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with the rise of reasoning-capable LLMs whose generation lengths are highly variable.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 18

Beyond Prediction: Tail-Aware Scheduling for LLM Inference

arXiv:2606. 18431v1 Announce Type: new Abstract: LLM serving exhibits extreme length variability, making size-based scheduling difficult in practice.

By Yueying Li, Yuanfan Chen, Jiayang Chen, Esha Choukse, Haoran Qiu, G. Edward Suh, Rodrigo Fonseca, Ziv Scully, Udit Gupta

llms

arXiv AI

Jun 30

LLM Serving Optimization with Variable Prefill and Decode Lengths

arXiv:2508. 06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths.

By Meixuan Wang, Yinyu Ye, Zijie Zhou

llms nlp

arXiv Machine Learning

1d ago

Multi-Bin Batching for Increasing LLM Inference Throughput

arXiv:2412. 04504v2 Announce Type: replace-cross Abstract: As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become increasingly critical.

By Ozgur Guldogan, Jackson Kunde, Kangwook Lee, Ramtin Pedarsani

llms efficiency

arXiv Machine Learning

1d ago

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

arXiv:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.

By Anders Vestrum, Arya Raeesi, Hanna Roed

llms

arXiv AI

1d ago

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

arXiv:2608. 15592v1 Announce Type: new Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput.

By Feiyang Ren, Shengtao Wen, Lingbing Guo, Yu Tian, Yuanning Cui, Xiang Chen

llms reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 2

Message Passing Enables Efficient Reasoning

arXiv:2607. 01077v1 Announce Type: cross Abstract: While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is a computational bottleneck.

By Xuecheng Liu, Daman Arora, Gokul Swamy, Andrea Zanette

llms nlp fine-tuning

Related stories

Beyond Prediction: Tail-Aware Scheduling for LLM Inference

LLM Serving Optimization with Variable Prefill and Decode Lengths

Multi-Bin Batching for Increasing LLM Inference Throughput

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

Message Passing Enables Efficient Reasoning