# laboratory

Published articles for laboratory.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## COMBIO: Automated Discovery of a Compact Composite Biomarker from Routine Laboratory Panels

DevFeed: [COMBIO: Automated Discovery of a Compact Composite Biomarker from Routine Laboratory Panels](<https://devfeed.tech/articles/combio-automated-discovery-of-a-compact-composite-biomarker-from-routine-laboratory-panels-123356.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.09102>)

Author: Tomer D. Meirman, Lior S. Rokach, Bracha Shapira, Seffi Cohen, Noa Dagan

Published: 2026-11-09T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Occam's razor machine learning](<https://devfeed.tech/topics/occam-s-razor-machine-learning.md>), [Hyperparameter optimization](<https://devfeed.tech/topics/hyperparameter-optimization.md>), [Combinatorial optimization](<https://devfeed.tech/topics/combinatorial-optimization.md>)

Tags: [automated](<https://devfeed.tech/tags/automated.md>), [classification](<https://devfeed.tech/tags/classification.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [database](<https://devfeed.tech/tags/database.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [discovery](<https://devfeed.tech/tags/discovery.md>), [disease](<https://devfeed.tech/tags/disease.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [external](<https://devfeed.tech/tags/external.md>), [form](<https://devfeed.tech/tags/form.md>), [gradient](<https://devfeed.tech/tags/gradient.md>), [hospital](<https://devfeed.tech/tags/hospital.md>), [information](<https://devfeed.tech/tags/information.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [learning](<https://devfeed.tech/tags/learning.md>), [logistic-regression](<https://devfeed.tech/tags/logistic-regression.md>), [machine](<https://devfeed.tech/tags/machine.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [measurements](<https://devfeed.tech/tags/measurements.md>), [routine](<https://devfeed.tech/tags/routine.md>)

### AI overview

COMBIO turns five candidate routine laboratory measurements into a compact biomarker: their product raised to integer powers from −2 to 2. Shapley values from a gradient-boosted reference model rank the measurements and guide exponent signs; an exhaustive search selects the formula on inner validation data before reserved-test evaluation. Across six tasks, its test-set AUROC differed by at most 0.03 from logistic regression using the same five measurements. For hospital mortality, AUROC was 0.698 in the external eICU database; a version using widely available measurements reached 0.786 in a source-database cohort covering 92% of admissions. The formula offers compactness rather than better accuracy, and requires local validation and recalibration before clinical use.

### Source excerpt

arXiv:2611.09102v1 Announce Type: new Abstract: Machine-learning models based on routine laboratory panels can predict outcomes but can be difficult to interpret and operationalize. We present COMBIO, an automated pipeline that distils such a model into a compact closed-form biomarker. The composite is a product of laboratory measurements raised to integer powers from minus two to two. The sign of each exponent indicates whether higher values of the corresponding measurement increase or decrease the composite. Shapley additive explanation values from the gradient-boosted reference model rank five candidate measurements and guide eligible exponent signs. An exhaustive search over small integer exponents selects the formula on an inner validation set, before evaluation on the reserved test set. We evaluated COMBIO across six tasks: three National Health and Nutrition Examination Survey outcomes, liver-disease classification in the Indian Liver Patient Dataset, and Medical Information Mart for Intensive Care IV in-hospital mortality and sepsis case-finding, with external evaluation in the eICU Collaborative Research Database for the two hospital tasks. Across the six test sets, COMBIO's area under the receiver operating characteristic curve differed by at most 0.03 from logistic regression using the five candidate laboratory measurements. The hospital mortality composite had an area under the curve of 0.698 in the external database. A version restricted to widely available measurements in the source hospital database achieved an area under the curve of 0.786 in a cohort covering 92% of admissions. The multiplicative representation provides compactness rather than additional accuracy, and local validation and recalibration are required before clinical use.

## SteelAgent: LLM-Orchestrated, Physics-Informed Steel Property Prediction with Audited Generalization and Calibrated Uncertainty

DevFeed: [SteelAgent: LLM-Orchestrated, Physics-Informed Steel Property Prediction with Audited Generalization and Calibrated Uncertainty](<https://devfeed.tech/articles/steelagent-llm-orchestrated-physics-informed-steel-property-prediction-with-audited-generalization-and-calibrated-uncertainty-120540.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.06953>)

Author: Aleksandr Volkov, Roman Sultimov, Mikhail Kuzin, Yury Maximov

Published: 2026-11-06T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Calibrated uncertainty](<https://devfeed.tech/topics/calibrated-uncertainty.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>)

Tags: [boosting](<https://devfeed.tech/tags/boosting.md>), [calibrated-uncertainty](<https://devfeed.tech/tags/calibrated-uncertainty.md>), [compliance](<https://devfeed.tech/tags/compliance.md>), [cond-mat-mtrl-sci](<https://devfeed.tech/tags/cond-mat-mtrl-sci.md>), [coverage](<https://devfeed.tech/tags/coverage.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [errors](<https://devfeed.tech/tags/errors.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [features](<https://devfeed.tech/tags/features.md>), [inverse-design](<https://devfeed.tech/tags/inverse-design.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [language-model](<https://devfeed.tech/tags/language-model.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [physics](<https://devfeed.tech/tags/physics.md>), [physics-informed](<https://devfeed.tech/tags/physics-informed.md>), [prediction](<https://devfeed.tech/tags/prediction.md>), [property](<https://devfeed.tech/tags/property.md>), [regression](<https://devfeed.tech/tags/regression.md>), [steel](<https://devfeed.tech/tags/steel.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [uncertainty](<https://devfeed.tech/tags/uncertainty.md>)

### AI overview

SteelAgent combines metallurgical descriptors and machine-learning models with a language-model interface for steel property prediction, uncertainty reporting, and inverse design. In SteelBench tensile-strength evaluations, gradient-boosting performance depended sharply on the held-out population: pooled out-of-fold R² dropped from 0.859 on a random split to 0.113 when steel families were held out. Nominal 90% prediction intervals achieved 91.0% empirical coverage under random splitting but 37.6% on held-out sources. Physics-derived features also had mixed effects, raising R² by 0.2534 in a 17-family holdout analysis while lowering it by 0.358 in a separate four-grade laboratory analysis. The authors report group-level errors and a synthetic regression experiment on calibration deterioration, and argue that property estimates or compliance scores should be accompanied by the evaluation population. These findings are specific to the reported evaluations.

### Source excerpt

arXiv:2611.06953v1 Announce Type: new Abstract: Steel property models can perform well on random test splits even when they extrapolate poorly to unfamiliar alloys. SteelAgent couples metallurgical descriptors and machine-learning models with a language-model interface for property prediction, uncertainty reporting and inverse design. We examine how the choice of held-out unit changes the conclusions drawn from SteelBench tensile-strength evaluations. For gradient boosting, pooled out-of-fold $R^2$ fell from 0.859 on a random split to 0.113 when steel families were held out. Empirical coverage of nominal 90% prediction intervals ranged from 91.0% under random splitting to 37.6% on held-out sources. The effect of physics-derived features depended on the evaluation. They increased gradient-boosting $R^2$ by 0.2534 in a 17-family holdout analysis and decreased it by 0.358 in a separate four-grade laboratory analysis. We report group-level errors and a synthetic regression experiment illustrating how calibration can deteriorate on held-out groups. These results motivate reporting the evaluation population alongside a property estimate or compliance score.

## Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence

DevFeed: [Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence](<https://devfeed.tech/articles/emergent-transfer-of-a-physics-foundation-model-from-simulation-to-laboratory-turbulence-118231.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2606.01470>)

Author: Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux, Michael McCabe, Alberto Bietti, Kyunghyun Cho, Cristiana Diaconu, Irina Espejo Morales, David Fouhey, Siavash Golkar, Tom Hehir, Shirley Ho, Jake

Published: 2026-11-05T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [AI Foundation Models](<https://devfeed.tech/topics/ai-foundation-models.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Simulation](<https://devfeed.tech/topics/simulation.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [few-shot](<https://devfeed.tech/topics/few-shot.md>)

Tags: [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [dns](<https://devfeed.tech/tags/dns.md>), [fluid](<https://devfeed.tech/tags/fluid.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [physics-flu-dyn](<https://devfeed.tech/tags/physics-flu-dyn.md>), [physics-foundation-models](<https://devfeed.tech/tags/physics-foundation-models.md>), [simulation](<https://devfeed.tech/tags/simulation.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

Walrus, a continuum-dynamics foundation model, was fine-tuned on three or fewer direct numerical simulation (DNS) realizations of Rayleigh-Taylor instability (RTI). In long rollouts it recovered key RTI physics; applied zero-shot to sliding-barrier laboratory data, it predicted mixing growth in the experimental range (α ~= 0.06-0.07) rather than the idealized DNS range (~= 0.02), without training on experimental samples. The authors interpret this as data-driven evidence that initial conditions play a crucial role in the longstanding simulation-experiment gap, whose origin remains debated. The model also generalized zero-shot to stable stratification, absent from training, and correctly predicted slower mixing-layer growth.

### Source excerpt

arXiv:2606.01470v2 Announce Type: replace-cross Abstract: Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Rayleigh-Taylor instability (RTI), a ubiquitous and demanding fluid instability seen from tabletop flows to supernova explosions, in which small perturbations at a density interface grow into chaotic, multiscale mixing as a lighter fluid accelerates into a heavier one. Standard ML models struggle with RTI, and despite over a century of theoretical, numerical, and experimental work, it carries an unresolved discrepancy between simulation and experiment: the late-time mixing growth rate, $\alpha$, measured in most laboratory experiments ($\sim$ 0.06-0.07), is roughly three times the value from idealized direct numerical simulations (DNS, $\sim$ 0.02). The gap's origin remains debated. These properties make RTI a stringent test for a question that matters well beyond RTI: can foundation models trained only on simulations generalise to sparse, messy, and noisy laboratory settings? We finetune Walrus, a foundation model for continuum dynamics, on three or fewer DNS realizations and recover key RTI physics over long rollouts. Applied zero-shot to sliding-barrier laboratory data, the finetuned model leaves the DNS-like regime and enters the observed growth band, having never seen a single experimental sample. These results provide independent, data-driven evidence that initial conditions play a crucial role in the longstanding sim-experiment gap in $\alpha$. The model also generalises zero-shot to stable stratification, a buoyancy regime absent from training, correctly slowing mixing-layer growth. Together, our results show that foundation models can generalise well beyond their training data, predicting laboratory behavior and unseen physical regimes, opening new ways to probe longstanding simulation-experiment gaps.

## What was Zenith Data Systems

DevFeed: [What was Zenith Data Systems](<https://devfeed.tech/articles/what-was-zenith-data-systems-112628.md>)

Original publisher: [Read original article](<https://blog.adafruit.com/2026/11/02/what-was-zenith-data-systems/>)

Author: Anne Barela

Published: 2026-11-02T20:23:53Z

Content type: article

Language: en

Sources: [Adafruit Industries](<https://devfeed.tech/sources/adafruit-industries-makers-hackers-artists-designers-and-engineers.md>)

Topics: [systems](<https://devfeed.tech/topics/systems.md>), [communications](<https://devfeed.tech/topics/communications.md>)

Tags: [aircraft](<https://devfeed.tech/tags/aircraft.md>), [analog](<https://devfeed.tech/tags/analog.md>), [army](<https://devfeed.tech/tags/army.md>), [computers](<https://devfeed.tech/tags/computers.md>), [cp-m](<https://devfeed.tech/tags/cp-m.md>), [dec](<https://devfeed.tech/tags/dec.md>), [electronics](<https://devfeed.tech/tags/electronics.md>), [ham-radio](<https://devfeed.tech/tags/ham-radio.md>), [heathkit](<https://devfeed.tech/tags/heathkit.md>), [history](<https://devfeed.tech/tags/history.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [oscilloscope](<https://devfeed.tech/tags/oscilloscope.md>), [vintage](<https://devfeed.tech/tags/vintage.md>), [vintage-computing](<https://devfeed.tech/tags/vintage-computing.md>), [wireless](<https://devfeed.tech/tags/wireless.md>), [z-100](<https://devfeed.tech/tags/z-100.md>), [zenith](<https://devfeed.tech/tags/zenith.md>), [zenith-data-systems](<https://devfeed.tech/tags/zenith-data-systems.md>)

### AI overview

Zenith Data Systems grew from Zenith Radio's 1979 purchase of Heath, whose electronics business developed from postwar surplus CRTs and Heathkit's instructional kits. Heath's computers ranged from the 1956 analog ES-400 to trainer and kit systems using Motorola 6800, Intel 8080A, DEC LSI-11, and Z80 processors. The Z-100, released in 1982, combined 8088 and 8085 processors, supporting MS-DOS or CP/M, and started at $3,999. The Air Force, Army, Navy, and GSA each purchased more than 5,000 units. The excerpt also traces Zenith's roots to two ham radio operators who founded the Chicago Radio Laboratory in 1918.

### Source excerpt

Bradford Morgan White traces the roots of Zenith Data Systems at Abort Retry Fail, starting with two ham radio operators. Ralph Mathews and Karl Hassel met over wireless, served together in the Navy, and founded the Chicago Radio Laboratory in 1918. The company reincorporated as Zenith in 1923, named after Mathews' call sign 9ZN. The [...]

## Deep Principle Releases Hydrogen-1.0: A 753B Open-Weight Model for Scientific Discovery

DevFeed: [Deep Principle Releases Hydrogen-1.0: A 753B Open-Weight Model for Scientific Discovery](<https://devfeed.tech/articles/deep-principle-releases-hydrogen-1-0-a-753b-open-weight-model-for-scientific-discovery-113062.md>)

Original publisher: [Read original article](<https://www.marktechpost.com/2026/11/02/deep-principle-hydrogen-1-0-open-weight-scientific-model/>)

Author: Asif Razzaq

Published: 2026-11-02T18:41:41Z

Content type: release

Language: en

Sources: [MarkTechPost](<https://devfeed.tech/sources/marktechpost.md>)

Topics: [AI for science](<https://devfeed.tech/topics/ai-for-science.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-discovery](<https://devfeed.tech/tags/agent-discovery.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai-shorts](<https://devfeed.tech/tags/ai-shorts.md>), [applications](<https://devfeed.tech/tags/applications.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [chemistry](<https://devfeed.tech/tags/chemistry.md>), [discovery](<https://devfeed.tech/tags/discovery.md>), [editors-pick](<https://devfeed.tech/tags/editors-pick.md>), [for-developers](<https://devfeed.tech/tags/for-developers.md>), [for-devs](<https://devfeed.tech/tags/for-devs.md>), [glm-5-3](<https://devfeed.tech/tags/glm-5-3.md>), [hydrogen](<https://devfeed.tech/tags/hydrogen.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [language-model](<https://devfeed.tech/tags/language-model.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [license](<https://devfeed.tech/tags/license.md>), [life-sciences](<https://devfeed.tech/tags/life-sciences.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [materials-science](<https://devfeed.tech/tags/materials-science.md>), [model](<https://devfeed.tech/tags/model.md>), [new-releases](<https://devfeed.tech/tags/new-releases.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-weights](<https://devfeed.tech/tags/open-weights.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [records](<https://devfeed.tech/tags/records.md>), [science](<https://devfeed.tech/tags/science.md>), [staff](<https://devfeed.tech/tags/staff.md>), [tech-news](<https://devfeed.tech/tags/tech-news.md>), [technology](<https://devfeed.tech/tags/technology.md>), [z-ai](<https://devfeed.tech/tags/z-ai.md>)

### AI overview

Hydrogen-1.0 is a 753B-parameter, MIT-licensed scientific model made by post-training Z.ai's GLM-5.3 with domain data, laboratory records and discovery trajectories. Deep Principle reports 38.3% on FrontierScience-Research versus GPT-6-Astra's 33.3%, and 93.0% on GPQA-Diamond, roughly matching Kimi-K3. Results vary by task: Hydrogen-1.0 leads GPT-6-Astra on scientific computing and forward reaction prediction, while trailing on NMR structure elucidation. Demonstrations include molecular simulation, reaction-network growth, NMR candidate ranking and experiment-protocol generation. The excerpt says serving was tested with SGLang across eight GPUs; NVFP4 and MXFP4 builds target specified NVIDIA and AMD accelerators. Full training datasets and recipes are not released, and the reported benchmark results are attributed to Deep Principle.

### Source excerpt

Deep Principle has released Hydrogen-1.0, an open-weight scientific model for chemistry, materials science and life sciences. It is built by post-training Z.ai's GLM-5.3 on domain data, laboratory records and agent discovery trajectories. For developers, it offers open weights that report a FrontierScience-Research win over GPT-6-Astra. The weights ship under the MIT License. TL;DR What is [...] The post Deep Principle Releases Hydrogen-1.0: A 753B Open-Weight Model for Scientific Discovery appeared first on MarkTechPost.

## Self-Building Labs

DevFeed: [Self-Building Labs](<https://devfeed.tech/articles/self-building-labs-111939.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.23826>)

Author: Jiaru Bai, Yuchi Zhao, Han Hao, Elia Savino, Miroslav Bogdanovic, Derk H. W. ten Klooster, Simone Pilon, Timothy No\\"el, Yang Cao, Varinia Bernales, Al\\'an Aspuru-Guzik

Published: 2026-11-02T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [AI for science](<https://devfeed.tech/topics/ai-for-science.md>), [Loop Engineering](<https://devfeed.tech/topics/loop-engineering.md>), [lerobot](<https://devfeed.tech/topics/lerobot.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [automation](<https://devfeed.tech/tags/automation.md>), [building](<https://devfeed.tech/tags/building.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-ma](<https://devfeed.tech/tags/cs-ma.md>), [cs-ro](<https://devfeed.tech/tags/cs-ro.md>), [designs](<https://devfeed.tech/tags/designs.md>), [digital-twin](<https://devfeed.tech/tags/digital-twin.md>), [discovery](<https://devfeed.tech/tags/discovery.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [execution](<https://devfeed.tech/tags/execution.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [flexibility](<https://devfeed.tech/tags/flexibility.md>), [flexible](<https://devfeed.tech/tags/flexible.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [labs](<https://devfeed.tech/tags/labs.md>), [layout](<https://devfeed.tech/tags/layout.md>), [objectives](<https://devfeed.tech/tags/objectives.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [programming](<https://devfeed.tech/tags/programming.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [real-world](<https://devfeed.tech/tags/real-world.md>)

### AI overview

Self-building laboratories (SBLs) aim to remove the manual engineering bottleneck that limits existing self-driving labs to procedures they were built to run. Given a high-level scientific goal, SBL starts from an empty room or an existing lab and builds missing capabilities, including the layout, digital twin, robot skills, and experimental workflows. The authors report operation across different robots and setups, from a single workstation to multi-robot laboratories, and validate the end-to-end system with a real-world quantum-dot synthesis experiment. The stated goal is to shift researchers from configuring automation to specifying scientific objectives.

### Source excerpt

arXiv:2610.23826v2 Announce Type: replace Abstract: Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still requires extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable. Project website: https://self-building-labs.github.io/

## Cognitive Inclusivity in AI Coding Assistants: Evidence from GitHub Copilot

DevFeed: [Cognitive Inclusivity in AI Coding Assistants: Evidence from GitHub Copilot](<https://devfeed.tech/articles/cognitive-inclusivity-in-ai-coding-assistants-evidence-from-github-copilot-112673.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.41192>)

Author: Grace Kerr, Aarushi Parasrampuria, Faith Culas, Kelly Blincoe

Published: 2026-11-02T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Software Engineering](<https://devfeed.tech/sources/arxiv-software-engineering.md>)

Topics: [GitHub Copilot](<https://devfeed.tech/topics/github-copilot.md>), [human computer interaction](<https://devfeed.tech/topics/human-computer-interaction.md>), [UI/UX Design](<https://devfeed.tech/topics/ui-ux-design.md>), [coding assistant](<https://devfeed.tech/topics/coding-assistant.md>), [developer-productivity](<https://devfeed.tech/topics/developer-productivity.md>)

Tags: [ai-coding-assistants](<https://devfeed.tech/tags/ai-coding-assistants.md>), [code-generation](<https://devfeed.tech/tags/code-generation.md>), [cs-se](<https://devfeed.tech/tags/cs-se.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [framework](<https://devfeed.tech/tags/framework.md>), [github-copilot](<https://devfeed.tech/tags/github-copilot.md>), [inclusive](<https://devfeed.tech/tags/inclusive.md>), [interviews](<https://devfeed.tech/tags/interviews.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [learning](<https://devfeed.tech/tags/learning.md>), [refactoring](<https://devfeed.tech/tags/refactoring.md>), [student](<https://devfeed.tech/tags/student.md>), [user-interaction](<https://devfeed.tech/tags/user-interaction.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

A controlled lab study with 20 student participants found five cognitive-inclusivity issues in GitHub Copilot: how it explains suggestions, supports validation, facilitates learning, exposes features, and shapes problem solving. Participants used Copilot for code generation, debugging, and refactoring; researchers analyzed think-aloud sessions and interviews, then used GenderMag to examine patterns across cognitive styles. The findings suggest common assistant interactions may suit some cognitive styles while adding effort for others. The study offers design implications for more inclusive AI coding assistants.

### Source excerpt

arXiv:2610.41192v1 Announce Type: new Abstract: AI coding assistants are commonly used for daily software development tasks. While prior research has examined their impact on productivity and code quality, less is understood about how these tools support diverse ways of thinking. Evidence from non-AI software engineering tools suggests that mismatches between tool design and users' cognitive styles can introduce inclusivity barriers, but it remains unclear whether similar issues arise in AI coding assistants. We investigate how developers with different cognitive styles experience and interact with these tools and whether they exhibit cognitive inclusivity challenges. We conducted a controlled laboratory study with 20 student participants using GitHub Copilot to complete code generation, debugging, and refactoring tasks. Using think-aloud protocols, semi-structured interviews, and inductive thematic analysis, we identify patterns in user interaction. We then apply the GenderMag framework to examine how these patterns differ across cognitive styles. We identify five inclusivity issues concerning how Copilot explains suggestions, supports validation, facilitates learning, exposes its features, and influences the problem-solving process. Our findings suggest that common interaction paradigms in AI coding assistants can privilege certain cognitive styles while introducing additional cognitive effort for others. We derive design implications for more inclusive AI coding assistants.

## A small HER2 reader for routine H&E slides: real-world deployment

DevFeed: [A small HER2 reader for routine H&E slides: real-world deployment](<https://devfeed.tech/articles/a-small-her2-reader-for-routine-h-e-slides-real-world-deployment-111481.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.40662>)

Author: Thang Tran (CloudKites AI Lab, New South Wales, Australia), Lan Dang (Monash Business School, Monash University, Victoria, Australia)

Published: 2026-11-02T05:00:00Z

Content type: news

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>), [arXiv: Software Engineering](<https://devfeed.tech/sources/arxiv-software-engineering.md>)

Topics: [Advanced AI workflows](<https://devfeed.tech/topics/advanced-ai-workflows.md>), [MONAI](<https://devfeed.tech/topics/monai.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>)

Tags: [attention](<https://devfeed.tech/tags/attention.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [best](<https://devfeed.tech/tags/best.md>), [call](<https://devfeed.tech/tags/call.md>), [cases](<https://devfeed.tech/tags/cases.md>), [classifiers](<https://devfeed.tech/tags/classifiers.md>), [cross](<https://devfeed.tech/tags/cross.md>), [cs-cv](<https://devfeed.tech/tags/cs-cv.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [cs-se](<https://devfeed.tech/tags/cs-se.md>), [data](<https://devfeed.tech/tags/data.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [distillation](<https://devfeed.tech/tags/distillation.md>), [external](<https://devfeed.tech/tags/external.md>), [first](<https://devfeed.tech/tags/first.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hospitals](<https://devfeed.tech/tags/hospitals.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [point](<https://devfeed.tech/tags/point.md>), [q-bio-qm](<https://devfeed.tech/tags/q-bio-qm.md>), [real-world](<https://devfeed.tech/tags/real-world.md>), [routine](<https://devfeed.tech/tags/routine.md>), [slides](<https://devfeed.tech/tags/slides.md>), [small](<https://devfeed.tech/tags/small.md>)

### AI overview

The 22-million-parameter HER2 reader analyzes routine H&E slides and is designed as a research-use pre-read to help pathologists prioritize ISH for equivocal IHC 2+ cases. It combines a ViT-S/16 encoder trained with self-supervision, distillation from H-optimus-0 and JEPA refinement with five cross-validated slide classifiers. Across external hospitals, reported AUCs were 0.82 on Yale-HER2, 0.75 on HEROHE and 0.63 on BCNB biopsies; for 85 HEROHE IHC 2+ cases, it ranked ISH-amplified tumors above non-amplified ones at 0.76. It performed comparably to readers using encoders 4 to 50 times larger in the stated benchmarks. Per-laboratory score normalization and local slides improved performance at a new site. CPU inference takes 5-6 seconds per slide. The excerpt presents it as decision support alongside IHC, not a replacement for pathology review or confirmatory testing.

### Source excerpt

arXiv:2610.40662v1 Announce Type: cross Abstract: HER2 status decides who receives HER2-targeted therapy. Pathologists score HER2 protein by immunohistochemistry (IHC); equivocal IHC 2+ tumours, their hardest calls, wait for in situ hybridisation (ISH). We built a HER2 reader for routine H&E slides, owned end to end, as a baseline for pathologists. A 22M-parameter ViT-S/16 tile encoder was trained on public TCGA slides by self-supervision, distillation from H-optimus-0 and two rounds of JEPA refinement; five cross-validated attention-based slide classifiers average their probabilities; and an operating point is fixed on training data. Compared under one paired ten-seed protocol, readers on our encoder rank first of 7 on external validation from TCGA-BRCA to Yale-HER2 (AUC 0.812), level with readers on encoders 4 and 50 times larger. We also rebuilt PathBench's encoder benchmark on public cohorts; our replica reproduces its published order of reference encoders, and under it our encoder ranks first of 7 on TCGA-BRCA (0.689) and sits level with H-optimus-0 and Phikon on Yale-HER2 (0.897). On three external hospitals our deployed reader reaches AUC 0.82 on Yale-HER2 (about 0.81 with training slides balanced for tissue size), 0.75 on HEROHE and 0.63 on BCNB biopsies, and among HEROHE's 85 IHC 2+ cases it ranks ISH-amplified above non-amplified tumours at 0.76, best among our TCGA-trained readers. Per-laboratory score normalisation and a laboratory's own slides improve it further on a new site. Without a GPU our reader needs 5-6 s per slide. It serves as a research-use pre-read in a pathology platform, where it proposes a probability and a call beside IHC and helps prioritise ISH for equivocal cases. Weights, per-slide predictions and the data behind every figure are public at https://huggingface.co/cloudkites/her2-reader.

## HERA: Assessing an Autonomous Agent for Quantum Device Measurement

DevFeed: [HERA: Assessing an Autonomous Agent for Quantum Device Measurement](<https://devfeed.tech/articles/hera-assessing-an-autonomous-agent-for-quantum-device-measurement-108151.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.39074>)

Author: Zachary W. Gomez, Nathan Bittner, Jordan T. McCourt, Qingrui Cao, Sandesh S. Kalantre, Benjamin H. Alexander, Zhiren Zheng, Kenji Watanabe, Takashi Taniguchi, Aaron Sharpe

Published: 2026-10-30T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [quantum mechanics](<https://devfeed.tech/topics/quantum-mechanics.md>)

Tags: [ablation](<https://devfeed.tech/tags/ablation.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [cond-mat-mes-hall](<https://devfeed.tech/tags/cond-mat-mes-hall.md>), [cryogenic](<https://devfeed.tech/tags/cryogenic.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [device](<https://devfeed.tech/tags/device.md>), [experimental](<https://devfeed.tech/tags/experimental.md>), [graphene](<https://devfeed.tech/tags/graphene.md>), [knowledge](<https://devfeed.tech/tags/knowledge.md>), [knowledge-base](<https://devfeed.tech/tags/knowledge-base.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [language](<https://devfeed.tech/tags/language.md>), [language-model](<https://devfeed.tech/tags/language-model.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [literature](<https://devfeed.tech/tags/literature.md>), [material](<https://devfeed.tech/tags/material.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [measurements](<https://devfeed.tech/tags/measurements.md>), [models](<https://devfeed.tech/tags/models.md>), [quantum](<https://devfeed.tech/tags/quantum.md>)

### AI overview

HERA performs cryogenic transport measurements of quantum devices using a lab-curated knowledge base of protocols, software tools, and literature. In a blinded trial, its conclusions on the stacking order of two multilayer graphene devices matched an expert's sealed conclusions. Ablation and hundreds of offline replays found that the knowledge base improved measurement design and reasoning across every model tested. For a superconductivity task in another material system, experts first spent a few hours developing a new protocol; HERA then autonomously found and characterized superconductivity. The evidence covers these tested tasks and models, and shows that extending the agent to a new problem required expert protocol development.

### Source excerpt

arXiv:2610.39074v1 Announce Type: cross Abstract: Large language model agents have begun to carry out laboratory measurements, but their scientific conclusions are useful only if they hold up under scrutiny. Demonstrating this requires controlled studies because models are stochastic and the context a model is given steers its conclusions. Here we introduce HERA, an autonomous agent designed to carry out cryogenic transport measurements of quantum devices. HERA draws on a knowledge base curated by lab members that holds measurement protocols, software tools, and literature references. We assessed HERA with a blinded trial against an expert on the stacking order of two multilayer graphene devices, an ablation of its knowledge base, and hundreds of offline replays in which models designed the trial's central measurement without running it. HERA's conclusions matched the expert's sealed conclusions on both tested devices. The ablation and the replay studies measured the impact of the knowledge base across different models, showing that with it every model designed better measurements and reasoned more soundly. To test how readily HERA extends to a new problem, we tasked it with finding and characterizing superconductivity in a different material system. This required a few hours of expert time to develop a new experimental protocol, after which HERA did both autonomously. These results show that, with a knowledge base designed for the task, language model agents are capable of sound experimental science.

## Science robotics startup Opentrons raises $40M to standardize laboratory automation

DevFeed: [Science robotics startup Opentrons raises $40M to standardize laboratory automation](<https://devfeed.tech/articles/science-robotics-startup-opentrons-raises-40m-to-standardize-laboratory-automation-106465.md>)

Original publisher: [Read original article](<https://siliconangle.com/2026/10/29/science-robotics-startup-opentrons-raises-40m-to-standardize-laboratory-automation/>)

Author: Kyt Dotson

Published: 2026-10-29T16:40:44Z

Content type: news

Language: en

Sources: [SiliconANGLE](<https://devfeed.tech/sources/siliconangle.md>)

Topics: [Robotics](<https://devfeed.tech/topics/robotics.md>), [AI for science](<https://devfeed.tech/topics/ai-for-science.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Automation](<https://devfeed.tech/topics/automation.md>)

Tags: [2014](<https://devfeed.tech/tags/2014.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-robotics](<https://devfeed.tech/tags/ai-robotics.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [automate](<https://devfeed.tech/tags/automate.md>), [automation](<https://devfeed.tech/tags/automation.md>), [data](<https://devfeed.tech/tags/data.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [emerging-tech](<https://devfeed.tech/tags/emerging-tech.md>), [funding](<https://devfeed.tech/tags/funding.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [james-atwood](<https://devfeed.tech/tags/james-atwood.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [laboratory-automation](<https://devfeed.tech/tags/laboratory-automation.md>), [motorized](<https://devfeed.tech/tags/motorized.md>), [news](<https://devfeed.tech/tags/news.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [raises](<https://devfeed.tech/tags/raises.md>), [robotic](<https://devfeed.tech/tags/robotic.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [robots](<https://devfeed.tech/tags/robots.md>), [scale](<https://devfeed.tech/tags/scale.md>), [science](<https://devfeed.tech/tags/science.md>), [software](<https://devfeed.tech/tags/software.md>), [startup](<https://devfeed.tech/tags/startup.md>), [tasks](<https://devfeed.tech/tags/tasks.md>), [the-latest](<https://devfeed.tech/tags/the-latest.md>)

### AI overview

Opentrons raised $40 million in a Series E led by Avego, with BroadOak Capital Partners participating, to scale its robotics, software, and data infrastructure. Its Flex handles more samples and can use an optional gripper to move plates, enabling more experiment steps without human intervention; the smaller, less expensive OT-2 focuses on routine liquid handling. Both are programmable benchtop robots, and Opentrons also offers AI software for sample preparation and repetitive experimental work. The robots can be configured for applications such as DNA extraction, PCR, protein purification, and ELISAs. The company says it has deployed more than 11,000 robots in over 80 countries, including at 90% of the top 20 global pharmaceutical companies.

### Source excerpt

Opentrons Labworks Inc., a leader in artificial intelligence-driven robotic laboratory automation, today announced it raised $40 million in new funding to scale its robotics, software and data infrastructure tools. Healthcare investor Avego led the Series E round alongside BroadOak Capital Partners. Founded in 2014, Opentrons makes benchtop robots that automate repetitive laboratory tasks, using motorized [...] The post Science robotics startup Opentrons raises $40M to standardize laboratory automation appeared first on SiliconANGLE.

## Canadian Startup Aims to Be Quantum's 'Picks' and 'Shovels'

DevFeed: [Canadian Startup Aims to Be Quantum's 'Picks' and 'Shovels'](<https://devfeed.tech/articles/canadian-startup-aims-to-be-quantum-s-picks-and-shovels-105638.md>)

Original publisher: [Read original article](<https://www.eetimes.com/canadian-startup-aims-to-be-quantums-picks-and-shovels/>)

Author: Gary Hilson

Published: 2026-10-28T20:50:32Z

Content type: news

Language: en

Sources: [EE Times](<https://devfeed.tech/sources/ee-times.md>)

Topics: [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [Computational Chemistry / Materials Science](<https://devfeed.tech/topics/computational-chemistry-materials-science.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-and-big-data](<https://devfeed.tech/tags/ai-and-big-data.md>), [ai-for-drug-discovery](<https://devfeed.tech/tags/ai-for-drug-discovery.md>), [ai-machine-learning](<https://devfeed.tech/tags/ai-machine-learning.md>), [alternatives](<https://devfeed.tech/tags/alternatives.md>), [analog](<https://devfeed.tech/tags/analog.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [applications](<https://devfeed.tech/tags/applications.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [business](<https://devfeed.tech/tags/business.md>), [canada](<https://devfeed.tech/tags/canada.md>), [challenge](<https://devfeed.tech/tags/challenge.md>), [communications](<https://devfeed.tech/tags/communications.md>), [community](<https://devfeed.tech/tags/community.md>), [cooling](<https://devfeed.tech/tags/cooling.md>), [costs](<https://devfeed.tech/tags/costs.md>), [designline](<https://devfeed.tech/tags/designline.md>), [digital](<https://devfeed.tech/tags/digital.md>), [education](<https://devfeed.tech/tags/education.md>), [ee-times](<https://devfeed.tech/tags/ee-times.md>), [ee-times-university](<https://devfeed.tech/tags/ee-times-university.md>), [eelife](<https://devfeed.tech/tags/eelife.md>), [eelive-electronic-design](<https://devfeed.tech/tags/eelive-electronic-design.md>), [eet](<https://devfeed.tech/tags/eet.md>), [electrical-engineering](<https://devfeed.tech/tags/electrical-engineering.md>), [electronic-components](<https://devfeed.tech/tags/electronic-components.md>), [electronic-engineering-times](<https://devfeed.tech/tags/electronic-engineering-times.md>), [electronics](<https://devfeed.tech/tags/electronics.md>), [embedded-systems](<https://devfeed.tech/tags/embedded-systems.md>), [fundamentals-courses](<https://devfeed.tech/tags/fundamentals-courses.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [learning](<https://devfeed.tech/tags/learning.md>), [materials](<https://devfeed.tech/tags/materials.md>), [mixed-signal](<https://devfeed.tech/tags/mixed-signal.md>), [news](<https://devfeed.tech/tags/news.md>), [opinion](<https://devfeed.tech/tags/opinion.md>), [part-search](<https://devfeed.tech/tags/part-search.md>), [product-search](<https://devfeed.tech/tags/product-search.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computers](<https://devfeed.tech/tags/quantum-computers.md>), [scale](<https://devfeed.tech/tags/scale.md>), [semiconductors](<https://devfeed.tech/tags/semiconductors.md>), [software](<https://devfeed.tech/tags/software.md>), [startup](<https://devfeed.tech/tags/startup.md>), [tech-papers](<https://devfeed.tech/tags/tech-papers.md>), [technologies](<https://devfeed.tech/tags/technologies.md>), [technology](<https://devfeed.tech/tags/technology.md>), [testing](<https://devfeed.tech/tags/testing.md>), [toronto](<https://devfeed.tech/tags/toronto.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

Meissner Technologies is developing superconducting materials for single-photon detectors, aiming to let photonic quantum systems operate at warmer temperatures without sacrificing performance. The company combines AI and machine-learning simulations with fabrication and lab tests, feeding experimental results back into its discovery model. It says replacing dilution refrigerators with closed-loop helium-4 cryostats could cut cooling costs by up to 95%; that is a stated potential, not a demonstrated result. Meissner is moving candidates into thin-film and nanowire detector testing, with a pilot-ready minimum viable detector as its next milestone. Unlike established suppliers ID Quantique and Single Quantum, which Leng characterizes as refining existing detector designs, Meissner is pursuing new materials. Its longer-term plans include automated, self-driving labs, while experimental validation remains a staffing bottleneck.

### Source excerpt

Toronto-based Meissner Technologies uses AI and laboratory testing to discover superconducting materials that could cut cooling costs and help quantum computers scale. The post Canadian Startup Aims to Be Quantum's 'Picks' and 'Shovels' appeared first on EE Times.

## When Can a Learned Design Surrogate Be Trusted in a Self-Driving Microfluidics Laboratory? An Offline Closed-Loop Framework Built and Tested on Six Public Tables

DevFeed: [When Can a Learned Design Surrogate Be Trusted in a Self-Driving Microfluidics Laboratory? An Offline Closed-Loop Framework Built and Tested on Six Public Tables](<https://devfeed.tech/articles/when-can-a-learned-design-surrogate-be-trusted-in-a-self-driving-microfluidics-laboratory-an-offline-closed-loop-framework-built-and-tested-on-six-public-tables-103949.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.36395>)

Author: Zhiyuan Zhang

Published: 2026-10-28T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [model architecture](<https://devfeed.tech/topics/model-architecture.md>), [ML model versioning](<https://devfeed.tech/topics/ml-model-versioning.md>), [A/B Testing](<https://devfeed.tech/topics/a-b-testing.md>)

Tags: [3](<https://devfeed.tech/tags/3.md>), [abstract](<https://devfeed.tech/tags/abstract.md>), [acquisition](<https://devfeed.tech/tags/acquisition.md>), [adapting](<https://devfeed.tech/tags/adapting.md>), [all](<https://devfeed.tech/tags/all.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [beats](<https://devfeed.tech/tags/beats.md>), [better](<https://devfeed.tech/tags/better.md>), [boosting](<https://devfeed.tech/tags/boosting.md>), [budget](<https://devfeed.tech/tags/budget.md>), [can](<https://devfeed.tech/tags/can.md>), [chemistry](<https://devfeed.tech/tags/chemistry.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [design](<https://devfeed.tech/tags/design.md>), [driving](<https://devfeed.tech/tags/driving.md>), [framework](<https://devfeed.tech/tags/framework.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [offline](<https://devfeed.tech/tags/offline.md>), [public](<https://devfeed.tech/tags/public.md>), [self-driving](<https://devfeed.tech/tags/self-driving.md>), [tables](<https://devfeed.tech/tags/tables.md>)

### AI overview

Across six public droplet-microfluidic and liposome tables evaluated offline, warm-start residual boosting recovered surrogate performance within 20 target measurements in 94% of chemistry tiers, 90% of laboratory tiers, and 40% of device tiers; chemistry recovery fell to 27% under a strict noise-floor reading. In closed-loop simulations, proposing all at once and warm-starting after each measurement reached a 10% target in 2.80 measurements versus 3.31 for the reference, and in 5.72 versus 11.35 for random proposals on 301 hard units at 3% tolerance. These gains survived drift and shuffled-label controls and were concentrated in sparsely sampled tiers. No tested label-free acquisition rule survived shuffled-label controls, no interval-aware proposal or alternative model family performed better, and no diagnostic threshold beat continuing once escalation was priced. The work is entirely offline and claims no wet-lab validation; its results do not establish that any label-free statistic can certify individual candidates.

### Source excerpt

arXiv:2610.36395v1 Announce Type: new Abstract: Self-driving laboratories couple a learned design surrogate to an instrument, and on every new target domain it must answer four questions: when it can be trusted, how many target measurements it needs, which to make first, and when to stop adapting, switch models or escalate. We state a framework making each measurable (a competence ratio, a budget $k^*(\varepsilon)$, a time-to-target and a priced decision) and instantiate it without wet experiments on six public droplet-microfluidic and liposome tables as offline laboratories: 107 leave-one-group tiers whose rows are hidden labels. A sealed, pre-registered programme (two sealed negative validations and a pre-registered test on three more tables) and seven frozen closed-loop protocols, each run twice, yield 23 findings. Shift cost is stratified by shift type; no label-free statistic certifies individual candidates; random-split model-family rankings predicted neither the sealed nor the loop rankings. Warm-start residual boosting returns a surrogate to $1.25\times$ its development reference within 20 target rows in 94% of chemistry tiers (27% under a strict noise-floor reading), 90% of laboratory and 40% of device tiers, and a budget planned from the shift type on other tables is not measurably worse on a new one. In the loop, one gain survives drift and shuffled-label controls on all tiers, carried by the sparsely sampled ones: proposing at once and warm-starting on every measurement reaches a 10% target in 2.80 measurements against 3.31 for the reference, and in 5.72 against 11.35 for random proposals on 301 hard units at a 3% tolerance. No label-free acquisition rule survives the shuffled-label control, no interval-aware proposal or other model family does better, and no threshold rule on the tested diagnostics beats always continuing once escalation is priced. No wet-lab validation is claimed.

## PMU Foundation Model Bridge: A Tool for Connecting Synchrophasor Streams to Time Series Foundation Models

DevFeed: [PMU Foundation Model Bridge: A Tool for Connecting Synchrophasor Streams to Time Series Foundation Models](<https://devfeed.tech/articles/pmu-foundation-model-bridge-a-tool-for-connecting-synchrophasor-streams-to-time-series-foundation-models-101741.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.33387>)

Author: Haozong Wang, Jiahui Yang, Yuru Wu, Chujie Zeng, Qian Liu, Yan Wen, Yayu Yang, Muhammad Umar Afzaal, Yilu Liu

Published: 2026-10-27T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Software Engineering](<https://devfeed.tech/sources/arxiv-software-engineering.md>)

Topics: [Time Series](<https://devfeed.tech/topics/time-series.md>), [AI Integration Strategies](<https://devfeed.tech/topics/ai-integration-strategies.md>), [streaming data analytics](<https://devfeed.tech/topics/streaming-data-analytics.md>), [Data pipelines](<https://devfeed.tech/topics/data-pipelines.md>)

Tags: [bridge](<https://devfeed.tech/tags/bridge.md>), [command-line-interface](<https://devfeed.tech/tags/command-line-interface.md>), [configure](<https://devfeed.tech/tags/configure.md>), [cs-se](<https://devfeed.tech/tags/cs-se.md>), [cs-sy](<https://devfeed.tech/tags/cs-sy.md>), [desktop](<https://devfeed.tech/tags/desktop.md>), [eess-sy](<https://devfeed.tech/tags/eess-sy.md>), [forecasting](<https://devfeed.tech/tags/forecasting.md>), [foundation-model](<https://devfeed.tech/tags/foundation-model.md>), [interface](<https://devfeed.tech/tags/interface.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [offline](<https://devfeed.tech/tags/offline.md>), [online](<https://devfeed.tech/tags/online.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [streams](<https://devfeed.tech/tags/streams.md>), [time](<https://devfeed.tech/tags/time.md>), [time-series](<https://devfeed.tech/tags/time-series.md>), [time-series-foundation-models](<https://devfeed.tech/tags/time-series-foundation-models.md>), [timestral](<https://devfeed.tech/tags/timestral.md>), [tool](<https://devfeed.tech/tags/tool.md>), [virtual](<https://devfeed.tech/tags/virtual.md>)

### AI overview

PMU Foundation Model Bridge connects online PMU or PDC streams to time series foundation models through a desktop interface or command line. Users can select measurement channels, configure preprocessing and outputs, and choose among four models supported in the initial release: Chronos-2, TimesFM 2.5, Timestral, and TimesFM-3. The tool accepts raw synchrophasor data, can derive additional quantities for forecasting, and produces either files or synchrophasor streams for offline use or integration into C37.118-based online workflows. The abstract reports validation with a virtual PMU and real-time laboratory measurements from a frequency disturbance recorder.

### Source excerpt

arXiv:2610.33387v1 Announce Type: cross Abstract: This paper presents PMU Foundation Model Bridge, an open source tool that connects online phasor measurement unit (PMU) or phasor data concentrator (PDC) streams to publicly available time series foundation models (TSFM). Through a desktop interface or command line interface, users can select measurement channels, define preprocessing, choose TSFMs, and configure outputs. In the initial release, 4 publicly available TSFMs were supported, including Chronos-2, TimesFM 2.5, Timestral, and TimesFM-3. The tool supports both file and synchrophasor streaming outputs, therefore enabling both offline use and integration into C37.118-based online workflow applications. In addition to supporting raw synchrophasor data, the framework provides a preprocessing layer for deriving customized additional quantities for forecasting. The tool is validated using a virtual PMU and real-time laboratory measurements from a frequency disturbance recorder (FDR). The tool aims to support community research on combining synchrophasor technology with TSFMs.

## How Should Transformers Encode Numeric Values in Electronic Health Records?

DevFeed: [How Should Transformers Encode Numeric Values in Electronic Health Records?](<https://devfeed.tech/articles/how-should-transformers-encode-numeric-values-in-electronic-health-records-99431.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2607.01391>)

Author: Maria Elkj{\\ae}r Montgomery, Christian Igel, Mikkel Odgaard, Martin Sillesen, Mads Nielsen

Published: 2026-10-26T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Transformers](<https://devfeed.tech/topics/transformers.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Encoding](<https://devfeed.tech/topics/encoding.md>)

Tags: [arithmetic](<https://devfeed.tech/tags/arithmetic.md>), [continuous](<https://devfeed.tech/tags/continuous.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [electronic-health-records](<https://devfeed.tech/tags/electronic-health-records.md>), [encoding](<https://devfeed.tech/tags/encoding.md>), [hybrid](<https://devfeed.tech/tags/hybrid.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [optimisation](<https://devfeed.tech/tags/optimisation.md>), [precision](<https://devfeed.tech/tags/precision.md>), [robustness](<https://devfeed.tech/tags/robustness.md>), [study](<https://devfeed.tech/tags/study.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The comparison finds that transformer numeric encodings trade precision against training stability and architectural flexibility. Explicitly modeling interactions between values and clinical concepts performs best on precision-sensitive arithmetic tasks when the architecture allows it. A hybrid approach that bins values before projection is described as more robust and broadly applicable; its best bin count follows an empirically derived power law in dataset size. Models generally achieve reliable approximate rather than exact arithmetic, and laboratory values improve clinical prediction only for some tasks. The authors therefore present hybrid token-based encoding as a practical default when robustness and deployability matter.

### Source excerpt

arXiv:2607.01391v2 Announce Type: replace Abstract: How do we encode numeric values in transformer-based sequence processing, particularly in electronic health record (EHR) data? We systematically compare discrete, continuous, and hybrid value encoding strategies using synthetic arithmetic tasks embedded within real-world EHR data, as well as real-world clinical prediction tasks. Our study reveals trade-offs between numeric precision, optimisation stability, and architectural flexibility. We find that approaches that explicitly model value-concept interactions perform best on precision-sensitive arithmetic tasks when architectural constraints permit. Hybrid token-based approaches that retain numeric values but apply binning prior to projection provide a more robust and broadly applicable alternative, with the optimal number of bins following a simple empirically derived power-law in dataset size. Across tasks, models consistently exhibit reliable "good enough" numeric computation rather than exact arithmetic, while clinical gains from incorporating laboratory values are task-dependent. This suggests that robustness and deployability often outweigh maximal numeric precision in practice, motivating hybrid token-based approaches as a practical default.

## Quest Diagnostics confirms data breach exposed patient records

DevFeed: [Quest Diagnostics confirms data breach exposed patient records](<https://devfeed.tech/articles/quest-diagnostics-confirms-data-breach-exposed-patient-records-97737.md>)

Original publisher: [Read original article](<https://www.bleepingcomputer.com/news/security/quest-diagnostics-confirms-data-breach-exposed-patient-records/>)

Author: Bill Toulas

Published: 2026-10-23T16:40:07Z

Content type: news

Language: en

Sources: [BleepingComputer](<https://devfeed.tech/sources/bleepingcomputer.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [enterprise data protection](<https://devfeed.tech/topics/enterprise-data-protection.md>)

Tags: [actor](<https://devfeed.tech/tags/actor.md>), [breach](<https://devfeed.tech/tags/breach.md>), [company](<https://devfeed.tech/tags/company.md>), [computer-help](<https://devfeed.tech/tags/computer-help.md>), [computer-security](<https://devfeed.tech/tags/computer-security.md>), [computers](<https://devfeed.tech/tags/computers.md>), [data](<https://devfeed.tech/tags/data.md>), [data-breach](<https://devfeed.tech/tags/data-breach.md>), [diagnostics](<https://devfeed.tech/tags/diagnostics.md>), [exposed](<https://devfeed.tech/tags/exposed.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [infosec](<https://devfeed.tech/tags/infosec.md>), [infosec-computer-security](<https://devfeed.tech/tags/infosec-computer-security.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [linux](<https://devfeed.tech/tags/linux.md>), [mac](<https://devfeed.tech/tags/mac.md>), [malware](<https://devfeed.tech/tags/malware.md>), [malware-removal](<https://devfeed.tech/tags/malware-removal.md>), [patient-data](<https://devfeed.tech/tags/patient-data.md>), [quest](<https://devfeed.tech/tags/quest.md>), [quest-diagnostics](<https://devfeed.tech/tags/quest-diagnostics.md>), [records](<https://devfeed.tech/tags/records.md>), [security](<https://devfeed.tech/tags/security.md>), [spyware](<https://devfeed.tech/tags/spyware.md>), [support](<https://devfeed.tech/tags/support.md>), [tech-support](<https://devfeed.tech/tags/tech-support.md>), [technical-support](<https://devfeed.tech/tags/technical-support.md>), [virus](<https://devfeed.tech/tags/virus.md>), [virus-removal](<https://devfeed.tech/tags/virus-removal.md>), [windows](<https://devfeed.tech/tags/windows.md>)

### AI overview

Quest Diagnostics says unauthorized access to its Quanum LSM system, discovered October 21, 2026, may have exposed some patient records; the affected data types remain undetermined while the company and external experts investigate. The company notified law enforcement and says customer-facing operations have not been disrupted. Direct-customer LIS interfaces and EMRs were not affected. Quest has stopped Quanum LSM and temporarily paused access to Essential Insights and LabHQ; customers are advised to use paper requisitions or Quanum eLabs to place orders and receive results.

### Source excerpt

American clinical laboratory company Quest Diagnostics disclosed that patient records have been exposed after a threat actor gained unauthorized access to its Quanum LSM system. [...]

## Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition

DevFeed: [Towards Field-Deployable Wearable IMU Systems for Occupational Activity Recognition](<https://devfeed.tech/articles/towards-field-deployable-wearable-imu-systems-for-occupational-activity-recognition-97028.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.31160>)

Author: Ihsan Ul Haq (Centre for Computational Science and Mathematical Modelling, Coventry University, United Kingdom, School of Architecture and Built Environment Deakin University, Australia), James Brusey

Published: 2026-10-23T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [ai evaluation](<https://devfeed.tech/topics/ai-evaluation.md>), [Data science and engineering](<https://devfeed.tech/topics/data-science-and-engineering.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [field](<https://devfeed.tech/tags/field.md>), [fusion](<https://devfeed.tech/tags/fusion.md>), [imu](<https://devfeed.tech/tags/imu.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [pilot](<https://devfeed.tech/tags/pilot.md>), [protocol](<https://devfeed.tech/tags/protocol.md>), [real-world](<https://devfeed.tech/tags/real-world.md>), [recognition](<https://devfeed.tech/tags/recognition.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [sensor](<https://devfeed.tech/tags/sensor.md>), [series](<https://devfeed.tech/tags/series.md>), [wearable](<https://devfeed.tech/tags/wearable.md>), [windows](<https://devfeed.tech/tags/windows.md>), [worker](<https://devfeed.tech/tags/worker.md>)

### AI overview

On the VTT-ConIoT benchmark, high aggregate accuracy can hide substantial differences between workers: in the main 16-class, multi-sensor LOSO configuration, per-subject accuracy ranged from 61.9% to 88.7% (mean 77.5%), despite all data being laboratory-collected. The study reports that sensor fusion and longer windows improve accuracy, standard time-series augmentation yields small consistent gains, and CTGAN augmentation reduces accuracy through a distributional-fidelity problem. In a personalised-calibration pilot, using 30% of a participant's own data recovered 16.8 percentage points for the weakest participants. These results offer configuration evidence toward field deployment, but do not establish performance in the field.

### Source excerpt

arXiv:2610.31160v1 Announce Type: new Abstract: Wearable inertial measurement unit (IMU) systems for construction worker human activity recognition (HAR) routinely report aggregate accuracies above 95 %, rarely under genuine subject-independent (leave-one-subject-out (LOSO)) evaluation, masking the per-individual variance that determines real-world deployment performance. Using the VTT-ConIoT benchmark, we conduct, to our knowledge, the first systematic configuration sensitivity analysis of this dataset: six classifiers across five sensor placements and three window sizes under a single LOSO protocol, a personalised-calibration pilot, and a validated comparison of tabular GAN-based and standard time-series augmentation. Per-subject LOSO accuracy for the main multi-sensor, 16-class configuration ranges from 61.9 % to 88.7 % (mean 77.5 %), a 27 pp spread, though all data are laboratory-collected. Multi-sensor fusion and longer windows both improve accuracy; standard augmentation gives small, consistent gains, while Conditional Tabular GAN (CTGAN) augmentation actively harms accuracy with a demonstrated distributional-fidelity mechanism; and personalised calibration recovers 16.8 pp for the weakest participants from just 30 % of their own data. These findings provide evidence-grounded configuration guidance for scaling occupational body sensor networks towards field deployment

## Implementation of Dynamic Microsegmentation for a Multi-Robot Network

DevFeed: [Implementation of Dynamic Microsegmentation for a Multi-Robot Network](<https://devfeed.tech/articles/implementation-of-dynamic-microsegmentation-for-a-multi-robot-network-95195.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.30102>)

Author: Vishwam Raval, Jaewon Kim, Ravi Pattabhi, Sandip Roy

Published: 2026-10-22T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Networking](<https://devfeed.tech/sources/arxiv-networking.md>)

Topics: [Network Segmentation](<https://devfeed.tech/topics/network-segmentation.md>), [Internet of things](<https://devfeed.tech/topics/iot.md>), [implementation](<https://devfeed.tech/topics/implementation.md>)

Tags: [asset](<https://devfeed.tech/tags/asset.md>), [automated](<https://devfeed.tech/tags/automated.md>), [automatically](<https://devfeed.tech/tags/automatically.md>), [based](<https://devfeed.tech/tags/based.md>), [cs-ma](<https://devfeed.tech/tags/cs-ma.md>), [cs-ni](<https://devfeed.tech/tags/cs-ni.md>), [cs-ro](<https://devfeed.tech/tags/cs-ro.md>), [cs-sy](<https://devfeed.tech/tags/cs-sy.md>), [eess-sy](<https://devfeed.tech/tags/eess-sy.md>), [fine](<https://devfeed.tech/tags/fine.md>), [functional](<https://devfeed.tech/tags/functional.md>), [health](<https://devfeed.tech/tags/health.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [internet-of-things](<https://devfeed.tech/tags/internet-of-things.md>), [iot](<https://devfeed.tech/tags/iot.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [network](<https://devfeed.tech/tags/network.md>), [networks](<https://devfeed.tech/tags/networks.md>), [operations](<https://devfeed.tech/tags/operations.md>), [policies](<https://devfeed.tech/tags/policies.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [report](<https://devfeed.tech/tags/report.md>), [robot](<https://devfeed.tech/tags/robot.md>), [robust](<https://devfeed.tech/tags/robust.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

The report demonstrates dynamic microsegmentation in a laboratory multi-robot IoT network using a commercial software-defined tool. Its fine-grained policies adapt segments based on assets' functional and health tags, with automated policy implementation. In the demonstration, the system isolated an indicated threat in real time while unaffected operations continued. The reported result suggests microsegmentation can support resilient IoT operation amid threats and disruptions; the evidence provided does not describe broader deployment or performance testing.

### Source excerpt

arXiv:2610.30102v1 Announce Type: cross Abstract: This report describes a laboratory instantiation of a dynamic microsegmentation function for an Internet-of-Things network, specifically a multi-robot network, using a commercial software-defined microsegmentation tool. Fine-grained dynamic segmentation policies are defined, where segments are adapted based on asset functional and health tags. Automated implementation of the dynamic policies in software is also undertaken. The microsegmentation solution is shown to automatically isolate an indicated threat in real time, while maintaining unaffected operations. The demonstration indicates that microsegmentation can be implemented in IoT networks, in a way that allows for robust operation in the face of threats and disruptions.

## ECG for laboratory values:A comprehensive physiological and technical analysis

DevFeed: [ECG for laboratory values:A comprehensive physiological and technical analysis](<https://devfeed.tech/articles/ecg-for-laboratory-values-a-comprehensive-physiological-and-technical-analysis-94427.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.28507>)

Author: Walentin Palanchuk

Published: 2026-10-21T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [Medical imaging](<https://devfeed.tech/topics/medical-imaging.md>)

Tags: [classification](<https://devfeed.tech/tags/classification.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [ecg](<https://devfeed.tech/tags/ecg.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [lead](<https://devfeed.tech/tags/lead.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [regression](<https://devfeed.tech/tags/regression.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

Using ECG recordings and patient metadata from MIMIC-IV, the thesis evaluates NT-proBNP prediction through regression and binary and three-class classification. The 12-lead setup outperformed lead II alone: mean absolute error was 514.27 versus 534.98 pg/mL, binary AUROC was 0.876 versus 0.849, and three-class macro-AUROC was 0.752 versus 0.738. Precise regression remained challenging, while broader concentration ranges were classified more effectively. The results are consistent with a shared cardiovascular basis for ECG features and NT-proBNP, but ECG prediction is presented as complementary: laboratory measurements remain necessary, and independent-dataset validation is still required. The abstract notes additional appendix experiments on glucose, hemoglobin, and potassium without reporting their results.

### Source excerpt

arXiv:2610.28507v1 Announce Type: new Abstract: Laboratory measurements are essential for clinical decision making but typically require invasive blood sampling. Recent research suggests that ECG recordings may contain information beyond cardiac rhythm and provide information related to laboratory values. This thesis investigates NT-proBNP prediction from ECG recordings and patient metadata using a multimodal deep learning framework, while considering the physiological plausibility of ECG-based prediction. It also compares a 1-lead configuration using lead II with the corresponding 12-lead configuration. Additional experiments on glucose, hemoglobin, and potassium are presented in the appendix. Regression, binary classification, and three-class classification experiments were conducted using MIMIC-IV, with the three-class task based on clinically established thresholds. The 12-lead configuration consistently achieved better performance. Regression reached a mean absolute error of 534.98 pg/mL for 1-lead and 514.27 pg/mL for 12-lead. Binary classification achieved AUROCs of 0.849 and 0.876, respectively, while three-class classification achieved macro-averaged AUROCs of 0.738 and 0.752. The results indicate that precise NT-proBNP regression remains challenging, whereas broader concentration ranges can be classified more effectively. The findings are consistent with the physiological hypothesis that ECG characteristics and NT-proBNP concentrations reflect shared cardiovascular processes. ECG-based NT-proBNP prediction may therefore provide complementary information, particularly for classifying broader concentration ranges. However, laboratory measurements remain necessary, and validation on independent datasets is required.

## Value-level data provenance in an electronic laboratory notebook used by humans and language-model agents: An experience report

DevFeed: [Value-level data provenance in an electronic laboratory notebook used by humans and language-model agents: An experience report](<https://devfeed.tech/articles/value-level-data-provenance-in-an-electronic-laboratory-notebook-used-by-humans-and-language-model-agents-an-experience-report-93709.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.27859>)

Author: Oleksiy V. Penkov, Boyang Shen

Published: 2026-10-21T04:00:00Z

Content type: news

Language: en

Sources: [arXiv: Software Engineering](<https://devfeed.tech/sources/arxiv-software-engineering.md>)

Topics: [Data Management](<https://devfeed.tech/topics/data-management.md>), [experience report](<https://devfeed.tech/topics/experience-report.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [agents](<https://devfeed.tech/tags/agents.md>), [all](<https://devfeed.tech/tags/all.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [cs-se](<https://devfeed.tech/tags/cs-se.md>), [data](<https://devfeed.tech/tags/data.md>), [data-provenance](<https://devfeed.tech/tags/data-provenance.md>), [electronic](<https://devfeed.tech/tags/electronic.md>), [experience](<https://devfeed.tech/tags/experience.md>), [experience-report](<https://devfeed.tech/tags/experience-report.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [language](<https://devfeed.tech/tags/language.md>), [language-model](<https://devfeed.tech/tags/language-model.md>), [model](<https://devfeed.tech/tags/model.md>), [notebook](<https://devfeed.tech/tags/notebook.md>), [physics-app-ph](<https://devfeed.tech/tags/physics-app-ph.md>), [provenance](<https://devfeed.tech/tags/provenance.md>), [report](<https://devfeed.tech/tags/report.md>), [used](<https://devfeed.tech/tags/used.md>), [value](<https://devfeed.tech/tags/value.md>)

### AI overview

An audit of eight baseline studies spanning nearly five years found that APS ELN linked samples to raw files but often lost the link from each file to its manually entered value, including the extractor version. The revised design records each value's source file and DFR plugin and version, plus any interactive selection or server-side formula used to derive it. An automated check reruns readers on stored files to detect drift. In two agent-recorded validation studies, in X-ray reflectivity and tribology, stored values had verifiable provenance with zero drift. The reported comparison with five other platforms found none linked individual stored numbers to both source files and extractor versions.

### Source excerpt

arXiv:2610.27859v1 Announce Type: cross Abstract: A published laboratory number can be verified only when its raw file and extraction method are known. This paper presents the value-level provenance architecture of APS ELN, an electronic laboratory notebook used by both researchers and language-model agents. The architecture was motivated by a production audit in a materials laboratory covering projects performed over almost 5 years. Tracing eight baseline studies from sample to stored value showed that the initial ELN structure preserved the link from sample to raw file. Still, the link from raw file to stored value was lost, mainly due to human factors: manual entries carried no file reference or extractor version. To close this gap, the database design and workflow were reworked so that every stored value records its source file, the data file reader/viewer (DFR) plugin and version, any interactive analysis selection, or the server-side formula for derived quantities. An automated drift check re-runs each DFR on the stored files. In two end-to-end validation studies recorded by agents in X-ray reflectivity and tribology, all stored values retained verifiable provenance with zero drift. A comparison with five other platforms shows that none binds individual stored numbers to both their source files and extractor versions.

## The Danger of Falling to System Role in AWS SDK Client

DevFeed: [The Danger of Falling to System Role in AWS SDK Client](<https://devfeed.tech/articles/the-danger-of-falling-to-system-role-in-aws-sdk-client-92020.md>)

Original publisher: [Read original article](<https://blog.doyensec.com/2022/10/18/cloudsectidbit-dataimport.html>)

Published: 2022-10-17T22:00:00Z

Content type: article

Language: en

Sources: [Doyensec](<https://devfeed.tech/sources/doyensec.md>)

Topics: [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Authorization](<https://devfeed.tech/topics/authorization.md>), [HashiCorp Vault](<https://devfeed.tech/topics/hashicorp-vault.md>)

Tags: [amazon-web-services](<https://devfeed.tech/tags/amazon-web-services.md>), [application-security](<https://devfeed.tech/tags/application-security.md>), [auditing](<https://devfeed.tech/tags/auditing.md>), [automation](<https://devfeed.tech/tags/automation.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-sdk](<https://devfeed.tech/tags/aws-sdk.md>), [bugs](<https://devfeed.tech/tags/bugs.md>), [client](<https://devfeed.tech/tags/client.md>), [cloud-infrastructure](<https://devfeed.tech/tags/cloud-infrastructure.md>), [cloud-security](<https://devfeed.tech/tags/cloud-security.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [credentials](<https://devfeed.tech/tags/credentials.md>), [doyensec](<https://devfeed.tech/tags/doyensec.md>), [doyensecurity](<https://devfeed.tech/tags/doyensecurity.md>), [end-user](<https://devfeed.tech/tags/end-user.md>), [exploit](<https://devfeed.tech/tags/exploit.md>), [hacking](<https://devfeed.tech/tags/hacking.md>), [iac](<https://devfeed.tech/tags/iac.md>), [iam](<https://devfeed.tech/tags/iam.md>), [infosec](<https://devfeed.tech/tags/infosec.md>), [infrastructure-as-code](<https://devfeed.tech/tags/infrastructure-as-code.md>), [infrastructure-as-code-iac](<https://devfeed.tech/tags/infrastructure-as-code-iac.md>), [keys](<https://devfeed.tech/tags/keys.md>), [laboratory](<https://devfeed.tech/tags/laboratory.md>), [offensive](<https://devfeed.tech/tags/offensive.md>), [pentest](<https://devfeed.tech/tags/pentest.md>), [pentester](<https://devfeed.tech/tags/pentester.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [security](<https://devfeed.tech/tags/security.md>), [system](<https://devfeed.tech/tags/system.md>), [vulnerability](<https://devfeed.tech/tags/vulnerability.md>)

### AI overview

In an AWS SDK for Go (v1) S3 import feature, an error handler cleared the configured credentials and retried the request. With credentials set to nil, the SDK used its provider chain, which could assume the host's IAM role; the article's case study says that role had access to internal S3 buckets, allowing a user who knew bucket names to import their contents. The article distinguishes this from `AnonymousCredentials`, which keeps requests unsigned for public resources, and static credentials, which use supplied keys. It advises auditors to trace user-reachable client initialization and fallback paths, then examine role permissions. For developers, it recommends anonymous credentials for public resources and avoiding fallback to the system role when integrating user-provided credentials.

### Source excerpt

Introduction to the series When it comes to Cloud Security, the first questions usually asked are: How is the infrastructure configured? Are there any public buckets? Are the VPC networks isolated? Does it use proper IAM settings? As application security engineers, we think that there are more interesting and context-related questions such as: Which services provided by the cloud vendor are used? Among the used services, which ones are directly integrated within the web platform logic? How is the web application using such services? How are they combined to support the internal logic? Is the usage of services ever exposed or reachable by the end-user? Are there any unintended behaviors caused by cloud services within the web platform? By answering these questions, we usually find bugs. Today we introduce the "CloudSecTidbits" series to share ideas and knowledge about such questions. CloudSec Tidbits is a blogpost series showcasing interesting bugs found by Doyensec during cloud security testing activities. We'll focus on times when the cloud infrastructure is properly configured, but the web application fails to use the services correctly. Each blogpost will discuss a specific vulnerability resulting from an insecure combination of web and cloud related technologies. Every article will include an Infrastructure as Code (IaC) laboratory that can be easily deployed to experiment with the described vulnerability. Tidbit # 1 - The Danger of Falling to System Role in AWS SDK Client Amazon Web Services offers a comprehensive SDK to interact with their cloud services. Let's first examine how credentials are configured. The AWS SDKs require users to pass access / secret keys in order to authenticate requests to AWS. Credentials can be specified in different ways, depending on the different use cases. When the AWS client is initialized without directly providing the credential's source, the AWS SDK acts using a clearly defined logic. The AWS SDK uses a different credential p