# Science Published articles for Science. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## New tool turns scientific papers into AI agents that can reproduce their analyses DevFeed: [New tool turns scientific papers into AI agents that can reproduce their analyses]() Original publisher: [Read original article]() Author: Brandon Vigliarolo Published: 2026-09-17T16:42:35Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Agent](), [Artificial Intelligence]() Tags: [agentic-ai](), [ai-agent](), [ai-and-ml](), [analysis](), [artificial-intelligence](), [reading](), [science](), [tool]() ### AI overview A new tool turns scientific papers into AI agents that can reproduce the studies' analyses, allowing users to interact with research computationally instead of reading each paper manually. ### Source excerpt Why go through the hassle of reading a study for yourself when you can turn it into an AI agent and tell it to reproduce the analysis for you? ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Naoki Egami's Research on Political Methodology and External Validity DevFeed: [Naoki Egami's Research on Political Methodology and External Validity]() Original publisher: [Read original article]() Author: Peter Dizikes | MIT News Published: 2026-09-16T04:00:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Statistics](), [Data Science](), [AI Research]() Tags: [artificial-intelligence](), [external-validity](), [faculty](), [idss](), [mit-political-science](), [mit-schwarzman-college-of-computing](), [naoki-egami](), [political-methodology](), [political-science](), [profile](), [research](), [school-of-humanities-arts-and-social-sciences](), [science](), [social-sciences](), [statistics](), [technology-and-society](), [voting-and-elections]() ### AI overview An MIT profile of political scientist Naoki Egami, whose research examines research methodology, external validity, and the mathematical and statistical challenges of studying civic and political phenomena. It also discusses his work on the use of AI tools in research. ### Source excerpt Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## A theoretical separation between quantum computers & LLMs DevFeed: [A theoretical separation between quantum computers & LLMs]() Original publisher: [Read original article]() Author: Srinivasan Arunachalam; Arkopal Dutt; Hari Krovi; Rik Sengupta; Ryan Mandelbaum Published: 2026-09-15T04:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [LLMs](), [Computer science]() Tags: [ai](), [computer-science](), [llms](), [quantum](), [research](), [science]() ### AI overview The article discusses research showing theoretical separations between shallow quantum circuits and restricted large language models. The work identifies computational problems involving function computation and sampling where shallow quantum circuits have a provable advantage, while emphasizing that the results are theoretical rather than immediately practical. ### Source excerpt Recent research further demonstrates the theoretical abilities of quantum computing ## Casey Muratori: Surprises In Computer History And Where Bad Code Comes From DevFeed: [Casey Muratori: Surprises In Computer History And Where Bad Code Comes From]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-09-14T13:03:29Z Content type: article Language: en Sources: [The Developing Dev]() Topics: [Computer science](), [Programming](), [Optimization](), [Code]() Tags: [code](), [optimization](), [performance](), [programming](), [science]() ### AI overview A podcast conversation with Casey Muratori explores computer science history, programming culture, the video game industry, and the lasting interpretations of Donald Knuth's warning about premature optimization. ### Source excerpt In this episode, my goal was to record a conversation that was completely free of any "AI doom" content. ## IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026 DevFeed: [IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-11T16:25:47Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [ibm](), [Quantum Computing](), [Supercomputing](), [Cloud](), [Algorithms](), [Optimization](), [Simulation and Design](), [Hardware]() Tags: [2026](), [ai](), [algorithms](), [amd](), [chemistry](), [cloud](), [collaboration](), [core](), [enterprise](), [financial-services](), [hardware](), [hub](), [ibm](), [infrastructure](), [manufacturing](), [optimization](), [processors](), [quantum](), [quantum-computing](), [science](), [simulation](), [supercomputing]() ### AI overview IBM and Lockheed Martin are establishing a quantum innovation hub at ETH Zurich, centered on an IBM Quantum System Two planned for installation at the Swiss National Supercomputing Centre by the end of 2026. The system will use IBM's 120-qubit Nighthawk r2 processor and support research in areas including chemistry, materials science, optimization, and financial services. ### Source excerpt IBM and Lockheed Martin are setting up a quantum innovation hub at ETH Zurich, and its core is Switzerland's first IBM Quantum System Two, to be installed at the Swiss National Supercomputing Centre (CSCS) in Lugano by the end of 2026. The hub comes out of an offset agreement with armasuisse, Switzerland's Federal Office for The post IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026 appeared first on StorageReview.com. ## Britain's technology brief is now everyone's job and nobody's responsibility DevFeed: [Britain's technology brief is now everyone's job and nobody's responsibility]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-11T13:12:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Machine learning](), [Architecture & Design](), [Security & Privacy](), [Vibe coding](), [NVLink](), [Aeternum]() Tags: [ai](), [cybersecurity](), [government](), [government-of-the-united-kingdom](), [open-source](), [opinion](), [public-sector](), [science](), [space](), [spacex](), [systems](), [technology](), [whitehall]() ### AI overview This technology news roundup examines how Britain's science, AI, and digital-government responsibilities are spread across competing ministerial portfolios. It also covers security incidents, AI companies, semiconductor infrastructure, open-source software, operating systems, and developer tools. ### Source excerpt Whitehall has scattered science, AI, and digital government across a thicket of competing ministerial portfolios ## Fable 5.1 vs. Fable 5: Results on a real-world budget, not the spec sheet DevFeed: [Fable 5.1 vs. Fable 5: Results on a real-world budget, not the spec sheet]() Original publisher: [Read original article]() Author: Jessica Wachtel Published: 2026-09-10T14:00:00Z Content type: comparison Language: en Sources: [The New Stack]() Topics: [Claude](), [anthropic](), [Command-line interface]() Tags: [ai-engineering](), [ai-models](), [anthropic](), [benchmark](), [claude](), [claude-code](), [large-language-models](), [science]() ### AI overview The article compares Claude Fable 5.1 and Fable 5 on five Terminal-Bench-Science tasks under a $12, 60-turn limit per test. It contrasts these constrained runs with Anthropic's published benchmark score and higher-cost leaderboard testing. ### Source excerpt When Anthropic launched Claude Fable 5.1 this month, it centered the announcement around one benchmark result: its Terminal-Bench-Science score. In The post Fable 5.1 vs. Fable 5: Results on a real-world budget, not the spec sheet appeared first on The New Stack. ## Introducing IBM and NASA's new foundation model for the Moon DevFeed: [Introducing IBM and NASA's new foundation model for the Moon]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-09-10T12:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [lunar foundation model](), [ibm](), [foundation-models](), [data](), [Artificial Intelligence](), [AI Architecture]() Tags: [accelerated-discovery](), [ai](), [ai-architecture](), [algorithms](), [ibm](), [lunar-foundation-model](), [mapping](), [model](), [nasa](), [release](), [science](), [space](), [us]() ### AI overview IBM and NASA are open-sourcing the NASA-IBM Lunar Foundation Model, a multimodal AI model that integrates lunar observations from US and Japanese missions across viewing angles, spatial scales, and measurement types. The model is intended to support lunar mapping, volcanic-history research, and searches for polar ice. ### Source excerpt The multi-modal model could help astronauts navigate craters, investigate ancient lava, and search for ice, as the US plans for a long-term lunar presence. ## Switzerland's first IBM Quantum System Two DevFeed: [Switzerland's first IBM Quantum System Two]() Original publisher: [Read original article]() Published: 2026-09-10T07:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [ibm](), [Supercomputing]() Tags: [2026](), [collaboration](), [development](), [hub](), [ibm](), [industry](), [innovation](), [news](), [quantum](), [quantum-community](), [quantum-computing](), [quantum-network](), [quantum-systems](), [research](), [science](), [switzerland](), [technology](), [with]() ### AI overview IBM and Lockheed Martin are launching a quantum innovation hub at ETH Zurich, anchored by Switzerland's first IBM Quantum System Two. Expected to be operational by the end of 2026 at the Swiss National Supercomputing Centre in Lugano, it will expand quantum computing access for Swiss universities, startups, companies, and researchers. ### Source excerpt Lockheed Martin and IBM are launching a Swiss quantum innovation hub at ETH Zurich to advance research, industry collaboration, and workforce development. ## Museas & Astronoma On Apple Vision Pro Support Curiosity & Discovery DevFeed: [Museas & Astronoma On Apple Vision Pro Support Curiosity & Discovery]() Original publisher: [Read original article]() Author: Laura Mingail Published: 2026-09-07T23:28:57Z Content type: article Language: en Sources: [UploadVR]() Topics: [3D](), [App]() Tags: [3d](), [apple](), [apps](), [art](), [design](), [discovery](), [education](), [exploration](), [interview](), [learning](), [science](), [technologies](), [vision]() ### AI overview An interview with Miguel Garcia Gonzalez examines how the Apple Vision Pro apps Museas and Astronoma use immersive design, real-world source material, and selective 3D presentation to encourage curiosity and exploration of art and science. ### Source excerpt What makes exploring art and science in spatial apps so compelling? We look at Apple Vision Pro apps Museas and Astronoma to learn how to design for curiosity and discovery. ## A connectomics milestone: Mapping the complete male fruit fly brain DevFeed: [A connectomics milestone: Mapping the complete male fruit fly brain]() Original publisher: [Read original article]() Published: 2026-09-03T16:00:03Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [datasets](), [Google]() Tags: [ai](), [datasets](), [general-science](), [google](), [health-bioscience](), [machine-intelligence](), [open-source](), [open-source-models-datasets](), [research](), [science]() ### AI overview Google Research describes a complete wiring map of the male fruit fly's brain and central nervous system, containing over 166,000 neurons and 125 million synaptic connections. The connectome was produced through a decade-long partnership using computing and AI, and is available to explore and download via Neuroglancer. ### Source excerpt General Science ## The economics of agent scale: tokens, ROI, and building platforms for AI-first teams (Part 2) DevFeed: [The economics of agent scale: tokens, ROI, and building platforms for AI-first teams (Part 2)]() Original publisher: [Read original article]() Author: Eira May Published: 2026-09-03T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI Bots](), [ai observability]() Tags: [agent](), [agentic](), [agentic-ai](), [agents](), [ai](), [ai-agents](), [business](), [cost](), [developers](), [engineering](), [engineering-leadership](), [google](), [infrastructure](), [leaders-of-code](), [model](), [observability](), [php](), [platform](), [platform-engineering](), [podcast](), [scale](), [science](), [tooling](), [work]() ### AI overview A podcast discussion on operating AI agents at scale, focusing on token efficiency, cost governance, context management, and platform tooling and observability. ### Source excerpt Andi Gutmans, head of Agentic Data Cloud at Google, returns for the second half of his Leaders of Code conversation to talk through the cost and infrastructure side of agentic development. ICYMI, part one covered judgment, code review, and data activation. ## Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science DevFeed: [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:30:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI research agents](), [OpenSSH]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [bionemo](), [claude](), [code](), [developer-tools-techniques](), [drug-discovery](), [gpu](), [microservices](), [nim](), [nvidia](), [science](), [simulation-modeling-design](), [workflow]() ### AI overview A tutorial for running NVIDIA BioNeMo NIM microservices with Claude Science to perform protein-structure prediction using multiple-sequence alignment and multiple folding models. ### Source excerpt Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.... ## Paving the way for greener ammonia production DevFeed: [Paving the way for greener ammonia production]() Original publisher: [Read original article]() Author: David L. Chandler | Department of Materials Science and Engineering Published: 2026-08-20T18:45:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Materials science and engineering](), [acid]() Tags: [agriculture](), [ai-for-materials-science](), [artificial-intelligence](), [bilge-yildiz](), [catalysts](), [chemistry](), [cleaner-fertilizer](), [cleaner-industry](), [computational-materials-science](), [computer-modeling](), [dmse](), [electrochemical-ammonia-production](), [emissions](), [energy](), [fertilizer-production](), [food](), [fossil-fuel](), [green-ammonia](), [greener-fertilizer](), [haber-bosch-process](), [industry](), [machine-learning](), [materials-science-and-engineering](), [mit-dmse](), [nitrogen-dissociation](), [nuclear-science-and-engineering](), [pollution](), [research](), [school-of-engineering](), [school-of-science](), [science](), [sustainability](), [transition-metal-nitrides]() ### AI overview MIT researchers developed an approach to predict promising catalyst materials for electrochemical ammonia production. The method could speed the search for alloys that may help make this lower-emissions process more competitive with the fossil-fuel-dependent Haber-Bosch process. ### Source excerpt New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products. ## When AI art has no author: Study finds generated images often can't be traced to training data DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-08-18T16:35:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [datasets](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [ablation](), [ai](), [ai-and-copyright-law](), [ai-generated-images](), [ai-governance](), [artificial-intelligence](), [arts](), [arts-technology-and-society](), [attribution-decay](), [causal-inference](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [counterfactual-analysis](), [counterfactual-radius](), [data](), [data-attribution](), [datasets](), [david-gifford](), [diffusion-ensembles](), [electrical-engineering-and-computer-science-eecs](), [ethics](), [generative-ai-images](), [generative-diffusion-models](), [image-similarity-metrics](), [images](), [law](), [machine-learning](), [machine-unlearning](), [mit-csail](), [mit-schwarzman-college-of-computing](), [model](), [model-interpretability](), [paper](), [privacy-preserving-machine-learning](), [research](), [school-of-engineering](), [science](), [technology-and-policy](), [technology-and-society](), [training-data-attribution](), [training-data-influence](), [zheng-dai]() ### AI overview MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change. ### Source excerpt A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves. ## Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery DevFeed: [Seeing beyond BMI: Estimating cardiometabolic risk with smartphone imagery]() Original publisher: [Read original article]() Published: 2026-08-17T10:34:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Deep neural networks](), [Deep learning](), [Machine learning](), [Google]() Tags: [deep-learning](), [general-science](), [google](), [health](), [health-bioscience](), [machine-intelligence](), [machine-learning](), [monitoring](), [multimodal](), [science]() ### AI overview Google Research presents PhotoScan, a deep learning approach that estimates body composition from smartphone photos and predicts insulin resistance with accuracy comparable to DXA scans in a clinical research setting. The article explains how body-composition measures such as fat distribution and visceral fat may complement wearable data for earlier cardiometabolic risk assessment. ### Source excerpt General Science ## What We Learned by Reproducing 2,200 papers from ICML DevFeed: [What We Learned by Reproducing 2,200 papers from ICML]() Original publisher: [Read original article]() Author: Abubakar Abid Published: 2026-08-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI research agents](), [AI-assisted coding](), [Human-AI evaluation](), [Claude Code](), [codex](), [cursor](), [Open Source]() Tags: [2026](), [agents](), [ai-agents](), [ai-research](), [claude-code](), [codex](), [community](), [cursor](), [open-source](), [research](), [science]() ### AI overview The article reports lessons from the ICML 2026 Open Reproductions challenge, in which the community used coding agents to reproduce research papers at scale. It discusses how agents can read papers, write code, run experiments, and report findings, while examining the continuing role of human oversight in AI research reproducibility. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## QOBLIB: tracking progress in quantum optimization DevFeed: [QOBLIB: tracking progress in quantum optimization]() Original publisher: [Read original article]() Published: 2026-08-12T13:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [benchmarking](), [Optimization](), [Framework](), [Website]() Tags: [benchmarking](), [benchmarks](), [community](), [framework](), [news](), [optimization](), [quantum](), [quantum-algorithms](), [quantum-community](), [quantum-computing](), [research](), [science]() ### AI overview IBM describes updates to the Quantum Optimization Benchmarking Library (QOBLIB), including a Nature Computational Science publication, a new website, and more than 2,000 submitted results. The initiative provides an open, community-driven framework for comparing quantum optimization benchmarks and assessing progress toward practical quantum advantage. ### Source excerpt Quantum advantage is here. The next question is where to apply it. New updates from the Quantum Optimization Working Group offer a glimpse at the path ahead. ## Highlights from MLSys 2026 DevFeed: [Highlights from MLSys 2026]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-08-11T15:07:08Z Content type: opinion Language: en Sources: [Capital One Tech]() Topics: [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [Machine learning](), [systems](), [Optimization](), [Deployment](), [Hardware](), [Streaming](), [Cache]() Tags: [2026](), [agentic](), [agentic-ai](), [ai-research](), [deployment](), [hardware](), [inference](), [large-language-model](), [machine-learning](), [optimization](), [research](), [retrieval-augmented-generation-rag](), [science]() ### AI overview Capital One's AI research team reviews themes and selected papers from MLSys 2026, focusing on efficient LLM serving, retrieval-augmented generation, cache management, model speculation, and agentic AI. The article highlights research on inference optimization, distributed compute and communication, streaming, and vector search. ### Source excerpt Capital One's AI research team recaps MLSys 2026, including optimizing serving LLMs, RAG and agentic AI. The 9th Annual Conference on Machine Learning and Systems (MLSys) took place in May in Bellevue, Washington. MLSys is a highly selective interdisciplinary conference sitting at the intersection of machine learning (ML) and systems design. The conference highlights cutting-edge research that combines generative AI, natural language processing, computer vision and reinforcement learning with infrastructure, deployment and hardware optimizations to make AI faster, scalable and more performant. MLSys offered Capital One associates the opportunity to learn from world-class conference sessions presented by experts in the field. All the attending associates left brimming with new ideas and planned collaborations. Kel Vanee, MVP, Machine Learning Engineering, presented some of the work happening at Capital One on using AI to make AI more efficient. Takeaways and favorite papers from MLSys 2026 Some of the most prevalent topics at MLSys this year were on cache management, model speculation, retrieval augmented generation (RAG) and agentic AI. With a plethora of relevant and interesting talks, we had no shortage of papers to choose favorites from. While a complete list of the papers we loved would be far too long, here are a few standouts: Large language model inference optimization One of the leading themes this year was how to more efficiently serve LLM models. We especially liked the papers on reducing self-attention costs, such as MAC-Attention: a Match-Amend-Complete scheme for fast and accurate attention computation and BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding. We found valuable insights in papers covering how best to overlap computation with communication, such as TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference, Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token and FlashAgen ## WeatherNext: AI model achieves breakthrough in forecasting cyclones DevFeed: [WeatherNext: AI model achieves breakthrough in forecasting cyclones]() Original publisher: [Read original article]() Author: WeatherNext team Published: 2026-08-06T15:06:15Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Open Source Models & Datasets](), [Google](), [data](), [Code]() Tags: [accelerate](), [ai](), [code](), [community](), [data](), [google](), [infrastructure](), [model](), [models](), [open](), [performance](), [research](), [science](), [speed](), [tpu]() ### AI overview WeatherNext is an AI weather-forecasting model that uses Functional Generative Networks to produce large ensembles of predictions and capture uncertainty. It generates 15-day forecasts in under a minute on a TPU, supports cyclone forecasting at relatively coarse resolution, and is being open sourced with its code and model weights for research and operational use. ### Source excerpt WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model. ## Ten advances in mathematics and theoretical computer science DevFeed: [Ten advances in mathematics and theoretical computer science]() Original publisher: [Read original article]() Published: 2026-08-01T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [Cryptography]() Tags: [ai](), [chatgpt](), [complexity](), [cost](), [cryptography](), [model](), [openai](), [publication](), [quantum](), [research](), [science]() ### AI overview OpenAI presents ten AI-assisted results on long-standing problems in mathematics and theoretical computer science. An internal Astra model produced solutions that humans prepared into manuscripts and that were formalized in Lean certificates. ### Source excerpt OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity. ## Quantum advantage through trusted quantum computation DevFeed: [Quantum advantage through trusted quantum computation]() Original publisher: [Read original article]() Published: 2026-07-30T10:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Simulation](), [Benchmark]() Tags: [news](), [process](), [quantum](), [quantum-algorithms](), [quantum-computing](), [quantum-error-correction-mitigation](), [quantum-research](), [reporting](), [science](), [techniques](), [the-result](), [validation](), [verification]() ### AI overview IBM reports three papers demonstrating quantum advantage with built-in validation, including validated error-mitigation techniques and methods for certifying classically hard quantum computations. The article explains how these approaches aim to establish trustworthy results when exact classical verification is unavailable. ### Source excerpt Demonstration shows trusted quantum computation in regimes where classical methods fail. [Next page]()