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Artificial Intelligence / Machine Learning · 5

Automation / Agentic Systems · 5

Research Papers · 2

Embedded Systems · 5

Computer Systems · 5

Developer Tools / Open Source · 4

Cloud / Infrastructure · 3

Archived section

Artificial Intelligence / Machine Learning · 5

Artificial Intelligence / Machine Learning
arXiv10/9/2026
Recently

Revisiting the Task of Building Damage Assessment by Satellite Imaging

Natural disasters such as earthquakes, tropical storms, and floods cause extensive damage to buildings, and rapid, reliable damage maps are essential to guide emergency response. Automating this task, referred to as building damage assessment (BDA), from satellite imagery, has become an active research topic at the intersection of remote sensing, computer vision, and machine learning, particularly since supervised deep learning methods were shown to outperform earlier handcrafted approaches. Yet, despite a decade of intense activity and the release of several large-scale datasets, the field lacks a shared foundation. Authors: Anthony Cioppa, Virgile Blua, Sébastien Piérard.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Natural disasters such as earthquakes, tropical storms, and floods cause extensive damage to buildings, and rapid, reliable damage maps are essential to guide emergency response.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
arXiv10/9/2026
Recently

QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code

Large language models (LLMs) have shown strong results in classical code generation and are increasingly used to write the unit tests that safeguard that code. Whether they can do the same for quantum software is less clear, because quantum programs are probabilistic, depend on numerical tolerances, and are judged by domain-specific checkers, so a well-formed test can still disagree with the benchmark's verdict. Existing quantum benchmarks measure whether a model produces a correct solution, but not whether its tests can distinguish correct implementations from faulty ones. Authors: Meriem Terki, Nouhaila Innan, Minghao Shao.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Large language models (LLMs) have shown strong results in classical code generation and are increasingly used to write the unit tests that safeguard that code.

Primary paperarxivcs.LG
Original
Artificial Intelligence / Machine Learning
arXiv10/9/2026
Recently

RSI-Anything: Unlocking Recursive Self-Improvement with World Models

Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model predictions may become unreliable as policies evolve. We introduce RSI-Anything, a domain-general framework built on JEPA-Anything that uses predictive world models to guide action and learning-data selection. Observed feedback drives policy or workflow improvement and conditional world-model adaptation, while component and compatibility checks govern the retention of updated policy-model pairs. Authors: Taoyong Cui, Pinxian Zeng, Wanghan Xu.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model predictions may become unreliable as policies evolve.

Primary paperarxivcs.CL
Original
Artificial Intelligence / Machine Learning
arXiv10/9/2026
Recently

InstantFusion: A Shared Latent Interface for Heterogeneous Image Generators

Diffusion/flow models have emerged as the de facto paradigms for image generation, giving rise to a diverse family of modern generators with increasingly heterogeneous architectures and representations. Despite their remarkable generation capabilities, the cross-model alignment of latent representations and the consistency of their underlying denoising dynamics remain underexplored. In this work, we uncover an intriguing cross-model compatibility: independently trained generators exhibit similar latent spatial organization, while directly handing off intermediate states between unaligned models can still preserve coarse visual content. Authors: Xuanlang Dai, Jiazi Bu, Yujie Zhou.

Why it matters

Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Diffusion/flow models have emerged as the de facto paradigms for image generation, giving rise to a diverse family of modern generators with increasingly heterogeneous architectures and representations.

Primary paperarxivcs.CV
Original
Artificial Intelligence / Machine Learning
IEEE Spectrum10/9/2026
Recently

Master AI Chip Principles With New IEEE Design Program

" The article examines the rapid growth of edge AI and the challenges it creates, including resource constraints, model architecture limitations, and network demands across edge-AI deployments. The acceleration is driven by a fundamental shift in how modern AI models are built and scaled. As the models have become much larger and more complex, they are computationally more demanding because they contain more parameters and require more calculations.

Why it matters

Read this for the concrete reporting in Artificial Intelligence / Machine Learning: " The article examines the rapid growth of edge AI and the challenges it creates, including resource constraints, model architecture limitations, and network demands across edge-AI deployments.

Trusted sourceIeee-products-and-servicesArtificial-intelligence
Original

Archived section

Automation / Agentic Systems · 5

Automation / Agentic Systems
arXiv10/9/2026
Recently

Towards Physically Grounded Articulated 3D Assets: A Multi-Agent Pipeline and Benchmark

In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how they move and function. Inspired by the literature on procedural and agentic generation, we present Artisan, a system that extends the latest method Articraft by introducing a multi-agent pipeline with physical simulation feedback, enabling comprehensive attributes beyond geometry, appearance, and physically plausible articulation, such as collision-free motion, stability under gravity, and accurate mass and material properties. Furthermore, we construct a unified benchmark spanning four key aspects: spatial fidelity, physical plausibility, downstream task performance, and overall quality. Authors: Xudong Cai, Ruijie Zhu, Haoliang Shang.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how they move and function.

Primary paperarxivcs.CV
Original
Automation / Agentic Systems
arXiv10/9/2026
Recently

BT-Agent: Deploying Onboard Robot Agents with Behavior Trees and Multi-Fidelity Evaluation

Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory. We propose BT-Agent, an onboard architecture combining LLM-driven task decisions with predefined behavior-tree (BT) skills. These skills enforce execution conditions and return structured failure feedback. Authors: Fabio Hübel, Adam Lilja, Yue Yin.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Deploying language-model agents onboard mobile robots requires task reasoning and responsive execution within limited compute and memory.

Primary paperarxivcs.RO
Original
Automation / Agentic Systems
arXiv10/9/2026
Recently

MC-EvoBench: Auditing Experience-Driven Self-Improvement in Minecraft Agents

A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish that a reusable capability was acquired; evaluation must separate capability change from carried-over resources, unequal source exposure, and early execution failures. We construct MC-EvoBench as a controlled Minecraft benchmark with a fixed catalog of 19 registered evolution chains, including 12 primary evaluation chains and seven calibration or probe chains, together with three graph-aligned paired probe families for transfer. Authors: Jingwei Song, Zhisheng Chen, Bingfan Zeng.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks.

Primary paperarxivcs.RO
Original
Automation / Agentic Systems
arXiv10/9/2026
Recently

Returning to Development States: When Full Sandbox Snapshots Help Coding Agents

Coding agents often make useful progress before they fail, but retry-from-scratch discards the machine state created along the way. This makes failure recovery an interesting search problem: if intermediate environment states preserve meaningful progress, restoring and branching from them could outperform repeatedly restarting from the initial state. We implement Go-Explore-style search over full Daytona snapshots and find that higher and more efficient solve rates depend mainly on progress that sits outside the repository, such as databases, services, and other runtime state. Authors: Daniel Thi Graviet, Tanmay Shah.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: Coding agents often make useful progress before they fail, but retry-from-scratch discards the machine state created along the way.

Primary paperarxivcs.LG
Original
Automation / Agentic Systems
arXiv10/9/2026
Recently

RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics

As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore tool availability during execution. To address this gap, we propose RATED (Reliability-Aware Tool Retrieval via Execution Dynamics), a retrieval framework that combines robust semantic matching with dynamic reliability adaptation. Authors: Weiliang Chen, Song Liao.

Why it matters

Read this for the paper's specific claim in Automation / Agentic Systems: As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior.

Primary paperarxivcs.AI
Original

Archived section

Research Papers · 2

Research Papers
arXiv10/9/2026
Recently

Nimbus Studio reproduces reference BCI decoding pipelines: a validation study on public benchmarks across protocols and paradigms

Nimbus Studio is a visual pipeline platform for brain-computer interface (BCI) experiments: researchers assemble preprocessing, feature extraction, decoding, and evaluation over public benchmark datasets or their own recordings, with cross-validated evaluation protocols (including leave-one-subject-out), BIDS export, and bring-your-own Python model classes. Whether its numbers can be trusted is a fair question for any such platform. We answer it with a parity study: six standard BCI decoding pipelines (CSP+LDA, Riemannian MDM, tangent-space logistic regression through the platform's bring-your-own-code node, P300 xDAWN+LDA, and the deep ShallowConvNet and EEGNet) were evaluated on three public datasets (motor-imagery BNCI-2014-001/004; P300 BNCI-2014-009) under two protocols (per-subject session holdout and pooled leave-one-subject-out cross-validation via the platform's planned-CV driver), once through Nimbus Studio's execution engine and once through independent, hand-written reference implementations built directly on MNE-Python, scikit-learn, pyriemann, and braindecode. Authors: Sergey Musienko.

Why it matters

Read this for the paper's specific claim in Research Papers: Nimbus Studio is a visual pipeline platform for brain-computer interface (BCI) experiments: researchers assemble preprocessing, feature extraction, decoding, and evaluation over public benchmark datasets or their own recordings, with cross-validated evaluation protocols (including leave-one-subject-out), BIDS export, and bring-your-own Python model classes.

Primary paperarxivcs.HC
Original
Research Papers
arXiv10/9/2026
Recently

Decision-Focused Learning for Cyber-Physical Risk-Aware Power Grid Operations

Power grid cyber-physical security requires intrusion detection methods that account for the operational consequences of detection errors. However, the existing intrusion detection approaches primarily focus on the predictive performance of learning algorithms, while downstream operational costs are often optimized independently, which can lead to inferior operational performance. To help alleviate this problem, we propose a novel optimization-learning cyber-physical-risk-informed framework that communicates iteratively to minimize power grid operational costs. Authors: Ayesha Siddiqua Dina, Ebisa D. Wollega, Afzal Hossain Sohag.

Why it matters

Read this for the paper's specific claim in Research Papers: Power grid cyber-physical security requires intrusion detection methods that account for the operational consequences of detection errors.

Primary paperarxivcs.CR
Original

Archived section

Embedded Systems · 5

Embedded Systems
arXiv10/9/2026
Recently

Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection

Light Detection and Ranging (LiDAR) is critical for autonomous perception, yet its performance is known to degrade in adverse weather. While existing literature models snow as atmospheric noise that degrades performance, our granular evaluation on the WADS-3D and CADC datasets reveals a counter-intuitive phenomenon: detection accuracy consistently improves as snow intensity increases. Through ablation study and meta-information analysis, we reconcile this by confirming that while falling snow acts as noise, its negative impact is outweighed by a dominant, concurrent factor: significantly increased object point count. Authors: Yiming Yang, Jeremy P. Bos.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Light Detection and Ranging (LiDAR) is critical for autonomous perception, yet its performance is known to degrade in adverse weather.

Primary paperarxivcs.CV
Original
Embedded Systems
IEEE Spectrum10/12/2026
Recently

Learning to Play Like a Pro: Adaptive Motion Planning and Tracking for Humanoid Robots

This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why adaptive, speed-aware control is essential for closing the gap between simulation and real-world deployment. What you will learn about: Why combining a professional athlete's motion style with reliable task performance remains an open challenge in humanoid ball-sports robotics, and how separating a high-level planner from a low-level tracker addresses this trade-off. How motion data extracted from broadcast video and motion-capture recordings can be reconstructed, retargeted, and physically corrected into reference motions a humanoid robot can safely track.

Why it matters

Read this for the concrete reporting in Embedded Systems: This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why adaptive, speed-aware control is essential for closing the gap between simulation and real-world deployment.

Trusted sourceType-whitepaperHumanoid-robots
Original
Embedded Systems
Hackster.io10/12/2026
Recently

Valve Releases Hardware Files, Including a KiCad Example, for Steam Frame Hardware and Accessories

Valve has made good on its promise to make its next-generation Steam Frame virtual reality (VR) headset accessible to those looking to build or mod the device, releasing initial details on using its hardware expansion port along with "stripped down CAD" files. "Information provided within this repository is for informational purposes only," Valve warns those interested in using the design files it has released to expand their Steam Frame with mods and accessories. "The reference material is provided on an AS-IS basis.

Why it matters

Read this for the engineering context in Embedded Systems: Valve has made good on its promise to make its next-generation Steam Frame virtual reality (VR) headset accessible to those looking to build or mod the device, releasing initial details on using its hardware expansion port along with "stripped down CAD" files.

Trusted source
Original
Embedded Systems
arXiv10/9/2026
Recently

HorizonNav: Geometry-Guided Video Diffusion for Anticipatory 3D Robot Navigation

We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a geometrically aligned representation of plausible surrounding 3D space. Given the initial observation and a textual description of the reconstruction objective, a vision-language model, guided by designed instructions, interprets the scene and constructs structured prompts for a video diffusion model. The resulting video sequence provides plausible future observations of the environment, which are subsequently processed using monocular ORB-SLAM3 for camera pose estimation and Video Depth Anything for dense depth prediction. Authors: Hajira Amjad, Valerii Serpiva, Viacheslav Liubimov.

Why it matters

Read this for the paper's specific claim in Embedded Systems: We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a geometrically aligned representation of plausible surrounding 3D space.

Primary paperarxivcs.RO
Original
Embedded Systems
arXiv10/9/2026
Recently

Noise Space Learning for VLA Adaptation

Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints. Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed. Building on prior noise-steering methods, we formulate NSL as an interface for VLA adaptation that accommodates different learning algorithms and instantiate it with PPO, SAC, and AWR. Authors: Junjie Lu, Chuheng Zhang, Yucen Wang.

Why it matters

Read this for the paper's specific claim in Embedded Systems: Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints.

Primary paperarxivcs.RO
Original

Archived section

Computer Systems · 5

Computer Systems
arXiv10/9/2026
Recently

Scheduling in Cloud,Fog, and Edge Computing: A Comparative Analysis and Decision Framework for Technique Selection

The increasing heterogeneity and dynamism of cloud, fog, and edge computing make scheduling a challenging problem involving diverse resource capabilities, workload characteristics, latency and energy constraints, and competing optimization objectives. Although numerous scheduling methods have been proposed, existing reviews often provide fragmented coverage of computing environments, scheduling granularities, or technique families, offering limited guidance on selecting an appropriate approach for specific requirements. This review presents a comparative analysis of task, job, and workflow scheduling across the cloud, fog, and edge paradigms, covering heuristic, metaheuristic, learning-based, and hybrid techniques. Authors: Rana Ghazali, Douglas G. Down.

Why it matters

Read this for the paper's specific claim in Computer Systems: The increasing heterogeneity and dynamism of cloud, fog, and edge computing make scheduling a challenging problem involving diverse resource capabilities, workload characteristics, latency and energy constraints, and competing optimization objectives.

Primary paperarxivcs.DC
Original
Computer Systems
arXiv10/9/2026
Recently

GPU-resident Execution for Timing-predictable Sensor Processing Systems

Real-time embedded systems increasingly place GPU-accelerated processing of high-bandwidth sensor streams inside closed-loop systems, but the CPU (the host) remains the primary orchestrator of each iteration. The host observes sensor inputs, submits CUDA work, waits for completion, and commits actuation. This host-mediated path exposes the closed-loop end-to-end latency to I/O interrupts, host scheduler decisions, and CUDA API overheads even after common host-side mitigations such as GPU-memory RDMA or CPU core isolation. Authors: Soham Sinha, Rahul Choudhury, Shekhar Dwivedi.

Why it matters

Read this for the paper's specific claim in Computer Systems: Real-time embedded systems increasingly place GPU-accelerated processing of high-bandwidth sensor streams inside closed-loop systems, but the CPU (the host) remains the primary orchestrator of each iteration.

Primary paperarxivcs.DC
Original
Computer Systems
Hackaday10/12/2026
Recently

Backup Tapes Come Back to the Homelab

It's one of the great mysteries of computing, how somehow in the Year Of OLlama 2026, tape remains the god of cheap storage.

Why it matters

Read this for the engineering context in Computer Systems: It's one of the great mysteries of computing, how somehow in the Year Of OLlama 2026, tape remains the god of cheap storage.

Trusted sourcecomputer hacksdata storage
Original
Computer Systems
LWN.net10/11/2026
Recently

Kernel prepatch 7.3-rc7

3-rc7 kernel prepatch is out for testing. According to Linus: "As always, I would have preferred to see a smaller rc, but it's not like anything in here looks like a reason to delay the final release. " One notable change that slipped into the mainline this week is the deprecation of the ext4 data=journal mode; it will be removed entirely at the beginning of 2028.

Why it matters

Read this for the concrete reporting in Computer Systems: According to Linus: "As always, I would have preferred to see a smaller rc, but it's not like anything in here looks like a reason to delay the final release.

Trusted source
Original
Computer Systems
arXiv10/9/2026
Recently

A Hardware-Native Trapped-Ion Hybrid Qudit-Oscillator Instruction Set

Scaling trapped-ion processors by adding ions remains challenging. A complementary route exploits the multilevel internal structure of each ion to increase the computational Hilbert space per ion. We introduce a hardware-native hybrid qudit-oscillator instruction set for a general d-level internal register coupled to bosonic modes and prove that three of its native operation families suffice for universality of one qudit coupled to several modes. Authors: Masoud Hakimi Heris, Yuan Liu, Frank Mueller.

Why it matters

Read this for the paper's specific claim in Computer Systems: Scaling trapped-ion processors by adding ions remains challenging.

Primary paperarxivquant-ph
Original

Archived section

Developer Tools / Open Source · 4

Developer Tools / Open Source
Vercel Blog10/9/2026
Recently

Deployment Storage billing begins for existing Pro teams

10 per GB-month. Check your email for details about when billing will start for your team. Deployment Storage Deployments older than 30 days will be deleted starting October 23, unless you opt out in your retention settings before then.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: Check your email for details about when billing will start for your team.

Official source
Original
Developer Tools / Open Source
GitHub Blog10/9/2026
Recently

Hack the World: Why hackathons are still the best place to learn to build

The barriers to building software have collapsed. Today, anyone can build. Hackathons are a great place to start learning.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: The barriers to building software have collapsed.

Official sourceCareer growthDeveloper skills
Original
Developer Tools / Open Source
Vercel Blog10/9/2026
Recently

Agents can now buy domains with the Vercel CLI

Your agent can now use the to buy a domain. vercel domains searchvercel domains checkvercel domains pricevercel domains buy The purchase decision itself stays with you by design. Run non-interactively, returns a structured, machine-readable error and the suggested next commands, so the agent hands the confirmation back to you, prohibiting it from spending money without your approval.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: vercel domains searchvercel domains checkvercel domains pricevercel domains buy The purchase decision itself stays with you by design.

Official source
Original
Developer Tools / Open Source
Vercel Blog10/9/2026
Recently

New Pro teams now default to 30-day deployment retention

New Pro teams now default to a 30-day deployment retention period. Coding agents have accelerated development, increasing deployment frequency and the amount of stored deployment history. Shorter retention keeps recent history available while limiting older history and its storage costs.

Why it matters

Read this for the official technical update in Developer Tools / Open Source: New Pro teams now default to a 30-day deployment retention period.

Official source
Original

Archived section

Cloud / Infrastructure · 3

Cloud / Infrastructure
AWS Compute Blog10/9/2026
Recently

Scaling RL rollouts with Firecracker on Amazon EC2 metal

Reinforcement learning at scale runs thousands of isolated rollout sandboxes in parallel, and packing more of them onto each host lowers your cost per training step. This post shows how to tune Firecracker microVM density on Amazon EC2 metal, and introduces labsweep, a harness for mapping your own latency and density curve.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: Reinforcement learning at scale runs thousands of isolated rollout sandboxes in parallel, and packing more of them onto each host lowers your cost per training step.

Official sourceAmazon EC2Expert (400)
Original
Cloud / Infrastructure
Cloudflare Blog10/9/2026
Recently

Introducing Clef-omni with full multimodality, plus a faster Clef and a cheaper Clef-flash

We are expanding the Clef decision model family with Clef-omni, natively processing audio, video, images, and text in a single pipeline. 0x.

Why it matters

Read this for the official technical update in Cloud / Infrastructure: We are expanding the Clef decision model family with Clef-omni, natively processing audio, video, images, and text in a single pipeline.

Official sourceAIDevelopers
Original
Cloud / Infrastructure
CNCF Blog10/9/2026
Recently

Join Platform Engineering Day at KubeCon + CloudNativeCon North America 2026

As organizations accelerate their adoption of AI and cloud native technologies, platform teams are being asked to solve a growing set of challenges. How can developers access new capabilities such as AI safely and efficiently? How...

Why it matters

Read this for the official technical update in Cloud / Infrastructure: As organizations accelerate their adoption of AI and cloud native technologies, platform teams are being asked to solve a growing set of challenges.

Official sourceBlog
Original