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A brief history of just-in-time (2003)

Lobsters Hottest ↗ · 2026-11-01

A survey paper examining forty years of just-in-time compilation research from 1960 to 2000, covering the motivation, design constraints, and a classification scheme for JIT systems.

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#systems

WAKMan: Workload Aware KVCache Manager

arXiv cs.AI ↗ · 2026-10-28 Cached

WAKMan is a workload-aware KV cache manager for LLM servers that automatically selects its eviction policy from live traffic, improving hit rate over vLLM's LRU by 5 percentage points on average across mixed single-turn, multi-turn, and agentic request workloads.

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#systems

TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving

Hugging Face Daily Papers ↗ · 2026-10-27 Cached

TAO 提出一个工具感知的联合调度框架,用于基于 LLM 的智能体服务,通过动态规划在容量约束下选择和放置会话,相比最优基线最高降低 26.8% 延迟、36.3% TTFT 并提升 29.5% 解码吞吐。

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#systems

OpenBSD 8.0

Lobsters Hottest ↗ · 2026-10-20 Cached

OpenBSD 8.0, the 61st OpenBSD release, was announced on October 20, 2026 with platform improvements for amd64, arm64, riscv64 and sparc64, including larger kernel virtual address space, hibernate support, RISC-V vector/Advanced Interrupt Architecture support, and a switch to the LLVM toolchain on sparc64.

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#systems

Efficient CPU-GPU collaborative MoE inference via Adaptive Expert Assignment

arXiv cs.AI ↗ · 2026-10-19 Cached

The paper presents Apex, a CPU-GPU collaborative MoE inference system that adaptively assigns GPU-missed experts to either CPU or GPU based on token load, improving prefill speed by up to 51.9% and decode throughput by up to 39.5% over the strongest baseline.

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#systems

On-Policy Residual Learning for Diffusion Language Models

arXiv cs.LG ↗ · 2026-10-16 Cached

该论文提出ORL(On-policy Residual Learning)框架,通过熵收缩目标与异步残差捕获等系统级协同设计,使扩散语言模型(DLM)在相近质量下将吞吐量提升至约6.9×,在单请求解码速度上超越自回归模型的推测解码方案。

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#systems

Towards Training and Inference of Graph Neural Networks in Graph Databases

arXiv cs.LG ↗ · 2026-10-15 Cached

该论文研究在图数据库中直接进行图神经网络的训练与推理,展示使用Cypher执行采样、特征检索和完整前向传播的可行性,优化后训练速度提升约2.5倍,推理速度比冷加载图到内存快10倍以上。

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#systems

Deploying RDMA for Large-Scale Inference and Training Workloads

arXiv cs.LG ↗ · 2026-10-12 Cached

ByteDance and Fudan University researchers present ByteExpress, a software communication layer over RDMA for large-scale parameter-server inference and training, reporting up to 74% lower P99 push latency and significant CPU savings versus TCP in production deployments.

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#systems

B-Trees Are Back: Engineering Fast and Pageable Node Layouts

Lobsters Hottest ↗ · 2026-10-09 Cached

The research paper "B-Trees Are Back: Engineering Fast and Pageable Node Layouts" explores how to design faster, pageable B-tree node layouts to improve database index performance on modern hardware.

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#systems

MemMux: Runtime Verification and Honest Resource Attribution for Fleets of Parallel Coding Agents

arXiv cs.AI ↗ · 2026-10-08 Cached

MemMux is a local runtime that treats resource governance for fleets of parallel coding agents as runtime verification, providing per-agent memory attribution, complete reclamation, escaped-process visibility, and bounded footprint. Benchmarks show it keeps a 7.5 GiB budget under zero swap and detects all injected escaped processes, with an open-sourced engine and benchmark.

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#systems

TomasuLLM: Out-of-Order Speculative Execution for LLM Agents

arXiv cs.CL ↗ · 2026-10-01 Cached

TomasuLLM 是一个将乱序推测执行思想引入 LLM Agent 的运行时,让工具调用在隔离的写时复制沙箱中乱序执行并按序提交验证,从而在 SWE-bench Verified、Terminal-Bench 2.0 和 SWE-Marathon 上分别提升 1.31x、1.35x 和 1.27x 的性能,且在 4,010 条提交验证记录中零误报。

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#systems

SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs

Hugging Face Daily Papers ↗ · 2026-09-28 Cached

SlideDP presents an asynchronous data-parallel runtime for host-resident layer streaming that fine-tunes LLMs beyond GPU memory across multiple GPUs, achieving 1.46–2.64x throughput over SlideFormer, MegaTrain, and ZeRO-Offload and even exceeding FSDP2 peak throughput while fine-tuning Qwen2.5-72B on four RTX 4090s.

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#systems

@songhan_mit: Welcome to submit to MLSys 2027. I am thrilled to serve as a PC Co-Chair this year alongside Rashmi Vinayak. We are act…

X AI KOLs Timeline ↗ · 2026-09-23 Cached

The MLSys 2027 call for papers is announced, with Song Han serving as PC Co-Chair, inviting research and industrial track submissions on machine learning and systems.

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#systems

Are AI agents creating more complexity than they remove?

Reddit r/AI_Agents ↗ · 2026-08-18

The article questions whether the proliferation of AI agents is leading to increased complexity in management and maintenance, potentially outweighing their benefits.

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#systems

@Krishnasagrawal: Andrej Karpathy’s 1-hour Stanford lecture on AI engineering is one of the best explanations I’ve seen of how AI systems…

X AI KOLs Timeline ↗ · 2026-08-15 Cached

Andrej Karpathy's Stanford lecture offers a concise explanation of AI engineering, focusing on building systems around models with context, memory, tools, and feedback loops.

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#systems

InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk

arXiv cs.AI ↗ · 2026-08-13 Cached

InfraBench is a benchmark suite for evaluating AI agents on realistic infrastructure management tasks across the full stack, lifecycle, and risk levels. Experiments show that even the strongest agent configurations achieve only 40-88% effective scores and struggle with consistency and safety.

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#systems

Ask HN: Are systems ready for the first negative leap second?

Hacker News Top ↗ · 2026-07-06 Cached

Discussion on Hacker News about whether computer systems are prepared for the first negative leap second, with comments debating the risks and alternatives like leap minutes.

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#systems

@chessMan786: Memory Segmentation Cheatsheet

X AI KOLs Timeline ↗ · 2026-07-04 Cached

A cheatsheet covering memory segmentation concepts, likely useful for students and developers.

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#systems

@aiwith3232: Various solid, textbook-like resources on the basics of LLMs!! So grateful... https://arxiv.org/pdf/2606.24937

X AI KOLs Timeline ↗ · 2026-07-02 Cached

A paper titled 'The Hitchhiker's Guide to Agentic AI: From Foundations to Systems' provides comprehensive textbook-like resources on the basics of LLMs and agentic AI.

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#systems

@vivekgalatage: What if you got a list of books across a broad spectrum of CS domains? And what if it is access to Brendan Gregg's cura…

X AI KOLs Timeline ↗ · 2026-06-28 Cached

A tweet sharing Brendan Gregg's curated list of recommended books covering systems performance, language performance, database performance, and other computer science domains.

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