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Artificial Intelligence / Machine Learning · 5

Automation / Agentic Systems · 5

Research Papers · 4

Embedded Systems · 5

Computer Systems · 5

Developer Tools / Open Source · 5

Cloud / Infrastructure · 5

Archived section

Artificial Intelligence / Machine Learning · 5

Artificial Intelligence / Machine Learning
arXiv7/20/2026
Recently

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis

Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data. A growing landscape of pathology foundation models now spans diverse data sources, architectures, and downstream applications. However, most pretrained models operate only at the image-tile level, use restrictive licenses, and remain computationally expensive, limiting large-scale slide-level clinical and research use. Authors: Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv7/20/2026
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EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database

Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical. However, conventional compressors often struggle to preserve fine structural details at high compression ratios (CRs), and implicit neural representations (INRs) require costly per-volume optimization and produce models with fixed CRs. To respond, we present EVOLVE, an autoencoder (AE)-based volume-compression framework that targets high CRs for offline compression, with three key contributions. Authors: Kaiyuan Tang, Maizhe Yang, Chaoli Wang.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical.

Primary paperarxivcs.GR
Original
Artificial Intelligence / Machine Learning
Apple Machine Learning Research7/19/2026
Recently

Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling

Token serves as the fundamental unit of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches lack fine-grained length modeling, operating primarily at the coarse-grained sequence level. In this paper, we introduce the Length Value Model (LenVM), a token-level framework that models the remaining generation length at each decoding step.

Why it matters

Read this for the official technical update in Artificial Intelligence / Machine Learning: Token serves as the fundamental unit of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance.

Official source
Original
Artificial Intelligence / Machine Learning
NVIDIA Technical Blog7/20/2026
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NVIDIA NVLink: The Scale-Up Network for AI Factories

The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute... The demand for AI continues to accelerate.

Why it matters

Read this for the official technical update in Artificial Intelligence / Machine Learning: Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute...

Official source
Original
Artificial Intelligence / Machine Learning
MIT Technology Review7/20/2026
Recently

AI is more likely than humans to form biases when hiring

The next time you apply for a job, AI may screen your résumé before any human sees it. But there's good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data.

Why it matters

Read this for the concrete reporting in Artificial Intelligence / Machine Learning: The next time you apply for a job, AI may screen your résumé before any human sees it.

Trusted sourceArtificial intelligenceAI
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Archived section

Automation / Agentic Systems · 5

Automation / Agentic Systems
arXiv7/20/2026
Recently

FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications

Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism. Existing serving systems and auto-parallelism compilers commit to limited transformations and fixed workload assumptions, so achieving high performance on a new application requires hand-crafting an efficient implementation. We present FlashRT, an agent harness that guides coding agents to lift simple developer-written reference implementations into optimized multi-GPU deployments that flexibly weigh target metrics like latency and throughput. Authors: Krish Agarwal, Zhuoming Chen, Yanyuan Qin.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism.

Primary paperarxivcs.LG
Original
Automation / Agentic Systems
arXiv7/20/2026
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LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications

Large language models (LLMs) and agentic AI systems have evolved from natural language tasks to using external tools to plan, retrieve, and act in technical domains. In smart grids, recent work applies agentic schemes to forecasting, optimization, and control, wrapping trusted solvers behind language interfaces and orchestrating multi-step workflows. The literature lacks a unified approach to designing and evaluating such systems. Authors: Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Large language models (LLMs) and agentic AI systems have evolved from natural language tasks to using external tools to plan, retrieve, and act in technical domains.

Primary paperarxiveess.SY
Original
Automation / Agentic Systems
arXiv7/20/2026
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HyMCache: A KV Cache Framework for Multi-Turn LLM Serving with CXL-Hybrid Memory

Long-context, multi-turn, and agentic LLM workloads increasingly reuse previously processed context, making KV-cache reuse essential for reducing redundant computation. However, this reuse shifts the bottleneck to the memory tier that stores and serves reusable KV states at cluster scale. GPU HBM and host DRAM are too costly to scale to TB-scale shared context capacity, motivating remote tiers built from lower-cost, higher-capacity media. Authors: Hakbeom Jang, Inho Song, Sam H. Noh.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Long-context, multi-turn, and agentic LLM workloads increasingly reuse previously processed context, making KV-cache reuse essential for reducing redundant computation.

Primary paperarxivcs.DC
Original
Automation / Agentic Systems
arXiv7/20/2026
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SGA: Plug&Play Geometric Verification for Educational Video Synthesis

Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim. However, ensuring spatial correctness and visual legibility remains challenging, as existing frameworks emphasize pedagogical content while overlooking geometric occlusions. We propose the Symbolic Geometric Agent (SGA), a plug-and-play module for code-centric animation pipelines that intercepts LLM-generated code, performs partial execution to extract symbolic scene graphs, and applies targeted refinement when spatial conflicts are detected. Authors: Lopez Jhon, Hinojosa Carlos, Ghanem Bernard.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim.

Primary paperarxivcs.AI
Original
Automation / Agentic Systems
arXiv7/20/2026
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SWE-Pruner Pro: The Coder LLM Already Knows What to Prune

Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attaching a separate code classifier, we find the agent itself encodes internal representations indicating the relevance of code context when reading tool output. Based on this finding, we propose SWE-Pruner Pro, which prunes tool outputs directly inside the agent. Authors: Yuhang Wang, Yuling Shi, Shaoqiu Zhang.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Pruning long context for coding agents has been a vital technology for efficient context management.

Primary paperarxivcs.CL
Original

Archived section

Research Papers · 4

Research Papers
arXiv7/20/2026
Recently

CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum chemistry, combinatorial optimization, and quantum machine learning. Since real-world VQA deployments routinely require circuits that exceed available hardware capacity, quantum circuit cutting has become an indispensable execution strategy, and pre-trained parameters are increasingly distributed through public repositories, introducing supply-chain security risks that have received little attention. Prior quantum backdoor attacks either introduce detectable circuit modifications or depend on device-specific noise, and none consider circuit cutting as an attack surface. Authors: Ahatesham Bhuiyan, Hoang Ngo, Cheng Chu.

Why it matters

Read this for the paper's specific claim in Research Papers: Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum chemistry, combinatorial optimization, and quantum machine learning.

Primary paperarxivquant-ph
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Research Papers
arXiv7/20/2026
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GARAGE: Characterizing the Automation Boundary in LLM-based Attack Graph Generation

While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances. To bridge this gap, we introduce GARAGE, a RAG-powered framework that converts fragmented CTI into an actionable, domain-specific knowledge base for automated attack graph generation. 1 and Auto-ISAC ATM-compliant knowledge base. Authors: Daekwon Pi, Sangho Lee, Young Hun Lee.

Why it matters

Read this for the paper's specific claim in Research Papers: While modern vehicle security depends on effective Cyber Threat Intelligence (CTI) synthesis, current automated tools struggle with unstructured data and automotive-specific architectural nuances.

Primary paperarxivcs.CR
Original
Research Papers
arXiv7/20/2026
Recently

Hardware Robustness of Sample-Based Quantum Diagonalization

Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples. Although SQD is considered robust to noisy samples and imperfect classical inputs, its robustness across practical deployment choices has not been systematically analyzed. As a result, shot budgets, qubit layouts, noise mitigation strategies, and the coupled-cluster singles and doubles (CCSD) amplitudes that initialize the ansatz are often chosen without clear empirical guidance. Authors: Ahatesham Bhuiyan, Cheng Chu, Qian Lou.

Why it matters

Read this for the paper's specific claim in Research Papers: Sample-based Quantum Diagonalization (SQD) is a hybrid quantum-classical method that replaces variational optimization with a self-consistent recovery loop over QPU samples.

Primary paperarxivquant-ph
Original
Research Papers
Apple Machine Learning Research7/19/2026
Recently

RayRoPE: Projective Ray Positional Encoding for Multi-View Attention

We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can be adaptive to the geometry of the underlying scene. We find that prior (absolute or relative) encoding schemes for multi-view attention do not meet the above desiderata, and present RayRoPE to address this gap.

Why it matters

Read this for the official technical update in Research Papers: We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can be adaptive to the geometry of the underlying scene.

Official source
Original

Archived section

Embedded Systems · 5

Embedded Systems
NVIDIA Technical Blog7/20/2026
Recently

Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps

Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and... Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and services they already use. Many of these workflows already depend on OpenUSD scenes, simulation-ready (SimReady) assets, Blender-based workflows, CAD pipelines, or domain-specific app stacks.

Why it matters

Read this for the official technical update in Embedded Systems: Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and...

Official source
Original
Embedded Systems
arXiv7/20/2026
Recently

FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation

Vision-language-action (VLA) models have achieved impressive generalization in robotic manipulation, and recent memory-augmented VLAs have relaxed the Markovian assumption by conditioning on past images or language summaries., pushing a button multiple times with small movements. We propose FM-VLA, a VLA model with force-based memory, enabling temporal context reasoning for non-Markovian, contact-rich manipulation. Authors: Ruicheng Li, Qixiu Li, Ruichun Ma.

Why it matters

Read this for the paper's specific claim in Embedded Systems: , pushing a button multiple times with small movements.

Primary paperarxivcs.RO
Original
Embedded Systems
IEEE Spectrum7/20/2026
Recently

SEM-Guided Low-kV FIB Finishing for Leading-Edge Semiconductor Failure Analysis

Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication. This delivers better resolution, better SNR, larger usable FOV, and shorter acquisition times. Learn how uninterrupted FIB milling will reduce damage and rework, accelerate time to TEM, and increase first pass success—so your FA, yield, and materials teams make faster, confident data driven decisions.

Why it matters

Read this for the concrete reporting in Embedded Systems: Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication.

Trusted sourceType-webinarSemiconductors
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Embedded Systems
Hackaday7/20/2026
Recently

Sail Virtually Aboard the "Itanic" with IA-64 Emulator

Intel's Itanium architecture was an interesting experiment, but it has gone down in history as one of the chip giant's bigger flops, so much so that it earned the name …read more

Why it matters

Read this for the engineering context in Embedded Systems: Intel's Itanium architecture was an interesting experiment, but it has gone down in history as one of the chip giant's bigger flops, so much so that it earned the name …read more

Trusted sourceSoftware Developmentemulator
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Embedded Systems
Raspberry Pi News7/21/2026
Recently

AI Projects with Raspberry Pi — out now!

This latest book from Raspberry Pi Press is a hands-on guide to building real-world AI applications on Raspberry Pi. The post AI Projects with Raspberry Pi — out now! appeared first on Raspberry Pi.

Why it matters

Read this for the official technical update in Embedded Systems: This latest book from Raspberry Pi Press is a hands-on guide to building real-world AI applications on Raspberry Pi.

Official sourcePublicationsRaspberry Pi AI
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Archived section

Computer Systems · 5

Computer Systems
arXiv7/20/2026
Recently

uSTM: A Lightweight and Efficient STM Supporting General Types and Deferred Aborts

Software Transactional Memory (STM) systems allow developers to more easily exploit multicore architectures by wrapping arbitrary sequential code in transactions that are executed concurrently. In recent years, the performance of STM systems has approached that of hand-tuned data structures through techniques that avoid unnecessary aborts and exploit the semantics of underlying data structures. Despite achieving excellent performance, most STM systems do not fully address the concerns they targeted in the first place: safety, usability, and generality. Authors: Zachary Kent, Guy Blelloch, André Costa.

Why it matters

Read this for the paper's specific claim in Computer Systems: Software Transactional Memory (STM) systems allow developers to more easily exploit multicore architectures by wrapping arbitrary sequential code in transactions that are executed concurrently.

Primary paperarxivcs.DC
Original
Computer Systems
arXiv7/20/2026
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RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning

Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory (CiM) capabilities, while its inherently stochastic write behavior provides a natural source of randomness that can be leveraged for differential privacy (DP) protection. Yet how to transform this device-level randomness-typically viewed as detrimental to accuracy-into a principled randomized mechanism while preserving model utility remains underexplored. Authors: Kwunhang Wong, Jichang Yang, Karl M. H. Lai.

Why it matters

Read this for the paper's specific claim in Computer Systems: Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns.

Primary paperarxivcs.CR
Original
Computer Systems
arXiv7/20/2026
Recently

Empowering On-Device Model Adaptation with an Edge AI Inference Accelerator

On-device model adaptation is essential to enable lifelong personalization on resource-constrained hardware, but compute, power, and memory limitations of such devices make end-to-end backpropagation impractical for modern deep neural networks. This work proposes a heterogeneous adaptation pipeline that repurposes a commercial edge AI inference accelerator, Hailo-8L, for frozen-backbone feature extraction during on-device training. The computational graph is partitioned so that the pre-trained backbone is quantized to INT8 and run on the accelerator, while only a lightweight FP32 classification head is fine-tuned on the host CPU, enabling frequent, energy-efficient in-field updates with most weights remaining fixed. Authors: Mateusz Piechocki, Alessandro Capotondi, Marek Kraft.

Why it matters

Read this for the paper's specific claim in Computer Systems: On-device model adaptation is essential to enable lifelong personalization on resource-constrained hardware, but compute, power, and memory limitations of such devices make end-to-end backpropagation impractical for modern deep neural networks.

Primary paperarxivcs.LG
Original
Computer Systems
arXiv7/20/2026
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Hardware Mechanisms to Dynamically Throttle AI Performance

As more capable AI models are increasingly integrated into critical computer systems, the lack of control over AI intent motivates safety mechanisms. Existing software safeguards impose only behavioral constraints that can potentially be bypassed by sufficiently intelligent models. While hardware-level safety enforcement has been recognized as an essential last line of defense, few mechanisms have been proposed beyond policy regulations on unauthorized accesses or coarse full-chip shutdown. Authors: Haiyue Ma, Lauren Malek, Joseph Forzani.

Why it matters

Read this for the paper's specific claim in Computer Systems: As more capable AI models are increasingly integrated into critical computer systems, the lack of control over AI intent motivates safety mechanisms.

Primary paperarxivcs.AR
Original
Computer Systems
LWN.net7/20/2026
Recently

Merging famfs?

The famfs filesystem, which is meant to provide shared access to huge memory-resident files on CXL and other devices, returned to the Linux Storage, Filesystem, Memory Management, and BPF Summit (LSFMM+BPF) in 2026. It was first discussed at LSFMM+BPF 2024 and a new implementation was described at the 2025 gathering, but it still has not made its way into the kernel; LWN looked at a discussion about merging famfs back in April 2026.

Why it matters

Read this for the concrete reporting in Computer Systems: The famfs filesystem, which is meant to provide shared access to huge memory-resident files on CXL and other devices, returned to the Linux Storage, Filesystem, Memory Management, and BPF Summit (LSFMM+BPF) in 2026.

Trusted source
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Archived section

Developer Tools / Open Source · 5

Developer Tools / Open Source
arXiv7/20/2026
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VEHBench: A Stage-Local Diagnostic Benchmark for LLM-Assisted Vibration Energy Harvester Design

Battery-free Internet of Things (IoT) requires iterative design of vibration energy harvesters (VEHs) under coupled physical constraints, while LLMs are emerging as interface layers for engineering workflows. However, existing engineering benchmarks primarily assess final artifact validity, offering limited insights into how LLMs behave across different stages of coupled physical design. We introduce VEHBench, an engineering-native diagnostic benchmark for LLM-assisted VEH design, featuring 763 literature-grounded tasks scored by an analytical physical oracle. Authors: Depeng Su, Yuyu Luo, Guobiao Hu.

Why it matters

Read this for the paper's specific claim in Developer Tools / Open Source: Battery-free Internet of Things (IoT) requires iterative design of vibration energy harvesters (VEHs) under coupled physical constraints, while LLMs are emerging as interface layers for engineering workflows.

Primary paperarxivcs.CL
Original
Developer Tools / Open Source
arXiv7/20/2026
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Testing Retrieval-Augmented Generation Systems with Chunk Coverage

Retrieval-Augmented Generation (RAG)-based systems\footnoteFor brevity, RAG-based systems are referred to as RAG systems throughout this paper. are increasingly deployed in high-stakes settings where correct behaviour depends not only on the language model but also on the retrieval component that selects external documents at inference time. While existing RAG evaluation metrics assess retrieval and generation quality on a per-query basis, typically relying on query-level test oracles such as reference answers or relevance annotations, they provide limited insight into whether a test suite adequately exercises the retrieval behaviour of the system as a whole. Authors: Jinhan Kim, Samuele Pasini, Paolo Tonella.

Why it matters

Read this for the paper's specific claim in Developer Tools / Open Source: Retrieval-Augmented Generation (RAG)-based systems\footnoteFor brevity, RAG-based systems are referred to as RAG systems throughout this paper.

Primary paperarxivcs.SE
Original
Developer Tools / Open Source
Hackster.io7/20/2026
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Telling Time Through Literature

Most clocks tell the time with numbers, but GitHub user kapoorankush's clock tells time with stories. Instead of displaying "10:26," for example, it might show a passage from a novel reading, "It was twenty-six minutes past ten," pulling from a library of thousands of carefully selected quotes that each correspond to a specific minute of the day. 5-inch Waveshare e-paper display to create a literary clock designed for everyday use.

Why it matters

Read this for the engineering context in Developer Tools / Open Source: Most clocks tell the time with numbers, but GitHub user kapoorankush's clock tells time with stories.

Trusted source
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Developer Tools / Open Source
GitHub Blog7/20/2026
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$100 million for open source: A milestone built by the community

Celebrating 100 million contributed by the community to the people who build and sustain open source every day. The post100 million for open source: A milestone built by the community appeared first on The GitHub Blog.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Celebrating 100 million contributed by the community to the people who build and sustain open source every day.

Official sourceMaintainersOpen Source
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Developer Tools / Open Source
Vercel Blog7/19/2026
Recently

Configure where run state lives in Vercel Workflows

Vercel Workflows A run keeps its home region for its lifetime, so for agents built on Workflows, the whole loop stays near the user: an agent serving someone in Sydney executes, checkpoints its progress, and streams output from Sydney. During a regional incident, workflow traffic fails over to the next closest region. 33 To pin a run to a specific region, pass the option to:regionstart() Existing workflows pick up regional placement on their next run, with no migration or code changes.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Vercel Workflows A run keeps its home region for its lifetime, so for agents built on Workflows, the whole loop stays near the user: an agent serving someone in Sydney executes, checkpoints its progress, and streams output from Sydney.

Official source
Original

Archived section

Cloud / Infrastructure · 5

Cloud / Infrastructure
CNCF Blog7/21/2026
Recently

Platform engineering for the agentic enterprise: Managing applications, resources, and AI agents

Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era. As organizations embraced Kubernetes, microservices, GitOps, and distributed architectures, the complexity of building and operating software grew beyond...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Platform engineering is evolving Platform engineering has become one of the defining disciplines of the cloud native era.

Official sourceBlog
Original
Cloud / Infrastructure
arXiv7/20/2026
Recently

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability. Modern LCSs continuously generate telemetry logs from distributed cloud services, producing high-dimensional multivariate time series that capture system operations. Detecting anomalies in this context is difficult due to extreme dimensionality, complex dependencies among distributed components, and severe sparsity from intermittently active services. Authors: Thu T. H. Doan, Mohammad Saiful Islam, Andriy Miranskyy.

Why it matters

Read this for the paper's specific claim in Cloud / Infrastructure: With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability.

Primary paperarxivcs.NI
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Cloud / Infrastructure
CNCF Blog7/20/2026
Recently

Why goodput matters more than throughput for LLM serving

When we benchmark an LLM serving setup, the number almost everyone reaches for first is throughput: how many requests per second the system can push through. It is easy to measure, easy to compare, and it...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: When we benchmark an LLM serving setup, the number almost everyone reaches for first is throughput: how many requests per second the system can push through.

Official sourceBlog
Original
Cloud / Infrastructure
AWS Blog7/20/2026
Recently

AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)

Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders.

Official sourceAmazon BedrockAmazon Cognito
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Cloud / Infrastructure
CNCF Blog7/21/2026
Recently

Why your agent needs access to your documentation

What 1,192 agent conversations taught us about knowledge base search A few months ago we shipped an agent inside our own product. It lives in our web app and lets users ask questions about their deployment,...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: What 1,192 agent conversations taught us about knowledge base search A few months ago we shipped an agent inside our own product.

Official sourceBlog
Original