# datasets Published articles for datasets. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck DevFeed: [Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-15T17:23:54Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Data Infrastructure](), [AI Infrastructure](), [idc](), [Inference](), [Retrieval-Augmented Generation](), [synthetic-data]() Tags: [ai](), [compute](), [data-infrastructure](), [datasets](), [enterprise](), [genai](), [hdd](), [idc](), [inference](), [reports](), [research](), [retrieval](), [storage](), [synthetic-data]() ### AI overview Seagate and WD published separate studies indicating that AI is increasing enterprise storage requirements and extending data retention. Although their headline percentages differ because they asked different questions, both reports point to storage becoming a larger part of AI infrastructure planning alongside growing archive and retrieval needs. ### Source excerpt Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD's IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional The post Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck appeared first on StorageReview.com. ## 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 ## ToolGrad: Efficient tool-use dataset generation with textual "gradients" DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"]() Original publisher: [Read original article]() Published: 2026-09-10T22:50:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [dataset](), [Large Language Model](), [LLM Techniques](), [Machine learning](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [agent](), [cost](), [datasets](), [evaluation](), [fine-tuning](), [generation](), [llm](), [machine-intelligence](), [machine-learning](), [natural-language-processing](), [prompt-engineering](), [tool]() ### AI overview ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches. ### Source excerpt Machine Intelligence ## Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2 DevFeed: [Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:40:57Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Infrastructure](), [cloud-infrastructure](), [Cloud](), [data-processing](), [model-deployment]() Tags: [accelerators](), [ai](), [ai-infrastructure](), [cloud](), [cloud-storage](), [data](), [datasets](), [enterprise](), [inference](), [infrastructure](), [integration](), [performance](), [snapshots](), [storage]() ### AI overview Backblaze and WEKA validated a two-tier AI storage pipeline that uses WEKA NeuralMesh as the high-performance tier for GPU workloads and Backblaze B2 Cloud Storage as the capacity tier. Raw data, training sets, media, source files, checkpoints, and other assets can move between the tiers according to access needs. NeuralMesh's Snap-to-Object feature was also tested with B2 for storing consistent filesystem snapshots and supporting recovery. ### Source excerpt Backblaze and WEKA have validated their two platforms together for AI pipelines, pairing WEKA NeuralMesh as the performance tier that feeds GPUs with Backblaze B2 Cloud Storage as the capacity tier that holds everything else. The integration, sizing, tuning, and testing are already done, so an AI infrastructure team can deploy a proven two-tier layout The post Backblaze B2 and WEKA NeuralMesh Validated as a Two-Tier AI Storage Pipeline, With Snap-to-Object Checkpoints in B2 appeared first on StorageReview.com. ## (Re)introducing Developer Story DevFeed: [(Re)introducing Developer Story]() Original publisher: [Read original article]() Author: Philippe Beaudette Published: 2026-09-10T18:01:54Z Content type: news Language: en Sources: [Stack Overflow Blog]() Topics: [Programming](), [Code](), [Large Language Model](), [datasets]() Tags: [career](), [community](), [company](), [cv](), [datasets](), [developer](), [identity](), [llms](), [news](), [privacy](), [profile](), [programming](), [se-stackoverflow](), [se-tech](), [skills](), [work]() ### AI overview Stack Overflow is reintroducing Developer Story, a profile feature designed to showcase developers' careers, specialties, contributions, and technical identity. The article also introduces Stack Identity, a broader vision for verified proof of developer work, with privacy controls and integrations for verified contributions from other sites. ### Source excerpt For the past few years, we've been looking at ways to bring a little more of the individual developer back to Stack. ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules DevFeed: [How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules]() Original publisher: [Read original article]() Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Deep neural networks](), [datasets](), [Code]() Tags: [ai](), [ai-models](), [antibiotics](), [applied-ai](), [chatgpt](), [code](), [codex](), [datasets](), [deep-learning](), [search]() ### AI overview César de la Fuente's lab uses deep-learning models, ChatGPT, and Codex to search genome and protein datasets for antimicrobial candidates that could help fight drug-resistant infections. ### Source excerpt César de la Fuente's lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections. ## Introducing ChatGPT for Financial Services DevFeed: [Introducing ChatGPT for Financial Services]() Original publisher: [Read original article]() Published: 2026-09-10T07:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [data](), [datasets](), [Model Context Protocol](), [Latency]() Tags: [chatgpt](), [data](), [datasets](), [financial-services](), [gpt](), [latency](), [mcp](), [models](), [openai](), [product](), [reasoning](), [retrieval](), [security]() ### AI overview ChatGPT for Financial Services combines GPT-6 Astra with built-in, OpenAI-hosted financial data for research, financial modeling, and client materials. It includes data-provider datasets, citations, enterprise controls, and MCP performance improvements. ### Source excerpt Introducing ChatGPT for Financial Services, combining built-in financial data and GPT-6 Astra for research, modeling, and client-ready materials. ## NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral DevFeed: [NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-04T18:01:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [hugging face](), [Nvidia](), [AI Development](), [AI Infrastructure](), [Application Development](), [datasets](), [Cloud](), [Deployment](), [model-deployment](), [inference-providers]() Tags: [ai](), [ai-infrastructure](), [application-development](), [cloud](), [creators](), [datasets](), [deployment](), [enterprise](), [hugging-face](), [inference-providers](), [models](), [multi-cloud](), [nvidia](), [platforms]() ### AI overview NVIDIA has agreed to acquire Hugging Face for $12.93 billion, with plans to expand its infrastructure and AI development capabilities. Hugging Face is expected to retain its brand and operate as an open, hardware-neutral platform supporting models, datasets, applications, multiple clouds, accelerators, and inference providers. ### Source excerpt NVIDIA has agreed to acquire Hugging Face for $12.93 billion, a transaction that would extend the company's position from accelerated compute and AI infrastructure into one of the industry's most widely used platforms for open models, datasets, and application development. In an announcement published on the NVIDIA website, CEO Jensen Huang said the company plans The post NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral appeared first on StorageReview.com. ## Transfer learning for genomic prediction in underrepresented populations DevFeed: [Transfer learning for genomic prediction in underrepresented populations]() Original publisher: [Read original article]() Published: 2026-09-03T18:20:31Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Fine-tuning](), [Model Development](), [Google](), [datasets]() Tags: [datasets](), [evaluation](), [general-science](), [google](), [healthcare](), [learning](), [machine-intelligence](), [research](), [training]() ### AI overview Google Research evaluates transfer learning for polygenic risk-score prediction across populations. European-cohort transfer learning improves prediction for small underrepresented target cohorts but can reduce accuracy as target cohorts grow, particularly for population-specific traits. ### Source excerpt General Science ## 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 ## GHarchive data has become unreliable for measuring GitHub activity DevFeed: [GHarchive data has become unreliable for measuring GitHub activity]() Original publisher: [Read original article]() Author: KD (noreply@blogger.com) Published: 2026-09-03T16:00:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [data](), [datasets](), [GitHub](), [Crawler](), [GitHub API](), [Open Source](), [GraphQL]() Tags: [collect](), [data](), [dataset](), [datasets](), [github](), [google](), [google-open-source](), [graphql](), [open-data-sets](), [open-source](), [oss](), [pipeline](), [pull-requests](), [retention](), [stream](), [volume]() ### AI overview This article examines the reliability of open source data, focusing on GHarchive's coverage of GitHub events. It argues that GHarchive should not be used for real-time or volume-based metrics because event retention has declined and some activity is omitted by the GitHub Event stream and API limitations. ### Source excerpt by Sophia Vargas, Google Open Source & Andrew Nesbitt, Ecosyste.ms Every second, open source contribution quietly shapes the software we rely on, and yet our view of this open ecosystem is surprisingly opaque. Open source development is performed in public spaces -- we can see the commits, issues and comments, the APIs and endpoints are free to use -- the logs are just sitting there, so why can't we just collect all of the data? ...Said every researcher, everywhere. However in most cases of open source related data, we are only looking at part of the whole. Why am I writing this post? Because many of us (including many business decision-makers) are too comfortable with unsubstantiated data. We've gotten used to it. Our models assume that it's smelly and we adjust the logic and weights to compromise. When it comes to open source, our confidence is even lower, even though our resulting decisions can directly impact individuals whom we collectively depend on. Let's consider one of my favorite datasets: GHarchive. Started as a hobby project in 2011, this crawler has amassed more than 15 years of event data from GitHub. While this source provides a historical record of open source development on GitHub, as a real-time or comprehensive source of metrics, it's unreliable and should not be a source for volume-based metrics. In 2025, GHarchive captured 14% fewer events than in 2024, despite steady growth in platform adoption. Since 2025, we estimate that data retention in GHarchive has fallen to ~50% and in 2026 it may be as low as 20% for some event types (see figure below). Prior to 2025, you could make the general assumption that the majority of events would be represented in this pipeline. Since 2025, we must now assume we may be missing at least half of events and possibly more -- not to mention all of the additional activity that's left out of the event API (see GitHub's GraphQL API.) The crawler logic behind this dataset is simple: give me all the events from the GitHub Ev ## NVIDIA to Acquire Hugging Face DevFeed: [NVIDIA to Acquire Hugging Face]() Original publisher: [Read original article]() Author: 黄仁勋 Published: 2026-09-03T11:56:49Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [hugging face](), [datasets](), [Deployment](), [cloud-infrastructure]() Tags: [ai](), [artificial-intelligence](), [cloud](), [corporate](), [datasets](), [deployment](), [developers](), [high-performance-computing](), [hugging-face](), [models](), [nvidia](), [open-source](), [platform](), [software](), [supercomputing]() ### AI overview NVIDIA says it has agreed to acquire Hugging Face and plans to scale its platform and infrastructure. The announcement says Hugging Face will remain open, supporting model, framework, cloud, inference-provider and hardware choices across the AI ecosystem. ### Source excerpt I'm excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000. Together, we will scale Hugging Face's platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide. Over the past decade, Clem, Julien, Thomas and the team at Hugging Face have built something remarkable: a vibrant home for [...] ## Give Your Coding Agents a Memory You Own DevFeed: [Give Your Coding Agents a Memory You Own]() Original publisher: [Read original article]() Author: David Corvoysier Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI-assisted coding](), [Traces](), [Inference](), [dataset]() Tags: [agents](), [announcement](), [coding](), [datasets](), [embedding](), [guide](), [inference](), [local](), [memory](), [open-source](), [retrieval](), [traces]() ### AI overview funes is a local, durable memory layer for coding agents that indexes prior session traces so agents can retrieve past decisions with provenance. It uses a deterministic pipeline with vector and BM25 search, reranking, recency weighting, and local storage. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate DevFeed: [LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T15:22:26Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data](), [Disk image](), [Resilience](), [data centers](), [Artificial Intelligence](), [ibm]() Tags: [2026](), [ai](), [architectures](), [data](), [data-center](), [datasets](), [enterprise](), [enterprise-storage](), [hardware](), [ibm](), [report](), [resilience](), [retention](), [storage](), [training](), [upgrade]() ### AI overview LTO tape capacity shipments rose 57% year over year in Q1 2026, driven by continued LTO-9 adoption, the rollout of LTO-10, and growing enterprise data and archival needs. The article explains how tape supports economical cold storage, long-term retention, AI training datasets, and data center power constraints. ### Source excerpt The Linear Tape-Open (LTO) Program Technology Provider Companies, comprising Hewlett Packard Enterprise, IBM Corporation, and Quantum Corporation, have released their annual tape media shipment report. Following sustained enterprise data expansion, the report highlights a strong start to 2026, driven by continued LTO-9 adoption and the rollout and initial capacity ramp-up of LTO-10 media. According to The post LTO Tape Shipments Up 57% in Q1 2026 as AI and Archive Demand Accelerate appeared first on StorageReview.com. ## Catch AI Regressions Before They Ship with AI Evals in CI/CD DevFeed: [Catch AI Regressions Before They Ship with AI Evals in CI/CD]() Original publisher: [Read original article]() Author: Shibam Dhar Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [CI/CD](), [Testing](), [AI Development]() Tags: [agent](), [ai](), [ai-agent](), [ai-evals](), [ci-cd](), [datasets](), [production](), [quality](), [tests]() ### AI overview Harness AI Evals uses golden datasets, response-quality metrics, and blocking quality gates in CI/CD to catch AI agent regressions before production. In an e-commerce support-agent test, an early run passed about 65% of cases, below the 70% deployment threshold, revealing incorrect, missing, or incomplete answers. ### Source excerpt Harness AI Evals tests AI agent quality in CI/CD, using golden datasets and quality gates to catch behavioral regressions before production. | Blog ## Healthcare organizations can now connect EHR and additional industry data to ChatGPT DevFeed: [Healthcare organizations can now connect EHR and additional industry data to ChatGPT]() Original publisher: [Read original article]() Published: 2026-09-01T12:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [data](), [datasets]() Tags: [ai](), [chatgpt](), [compliance](), [data](), [datasets](), [healthcare](), [integration](), [product](), [workflow]() ### AI overview ChatGPT for Healthcare can connect authorized Epic patient context and structured public healthcare data, helping healthcare teams review records, research, and official sources within governed workflows. ### Source excerpt ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more. ## GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models DevFeed: [GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models]() Original publisher: [Read original article]() Author: Naoto Usuyama, Jeya Maria Jose Valanarasu, Tristan Naumann Published: 2026-08-31T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [foundation-models](), [datasets](), [data]() Tags: [analysis](), [data](), [datasets](), [foundation-models](), [models](), [performance](), [research](), [research-blog](), [scale](), [validation]() ### AI overview GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models designed to reduce computational demands while maintaining strong performance. They enable repeated analysis of larger cancer cohorts and support population-scale research into disease biology, biomarkers, and clinical outcomes. ### Source excerpt What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research. ## The Open ASR Leaderboard Adds Its First Global South Language DevFeed: [The Open ASR Leaderboard Adds Its First Global South Language]() Original publisher: [Read original article]() Author: Eric Bezzam; Shobhit Banga; Manas Dhir; Bhaskar Singh; Manmeet Kaur; Aaditya Pareek; Walecha; Sagar Jain; Hanuman Sidh; Vanshika Chhabra Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [asr](), [Benchmark](), [Human-AI evaluation]() Tags: [artificial-intelligence](), [asr](), [audio](), [benchmark](), [benchmarks](), [contributors](), [datasets](), [devices](), [evaluation](), [leaderboard](), [metrics](), [open](), [open-source](), [research](), [speech]() ### AI overview The Open ASR Leaderboard introduces Monsoon evaluation sets for Hindi in India, expanding coverage beyond European languages and testing how recognition performance varies across populations and conditions. The sets use public and private splits, speaker-disjoint data, detailed speaker attributes, and variation in geography, age, gender, vocabulary, devices, acoustic environments, speech type, speech rate, and transcript validity. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Benchmarking vector indexes DevFeed: [Benchmarking vector indexes]() Original publisher: [Read original article]() Author: Evgeniy Patlan Published: 2026-08-27T13:35:32Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [benchmarking](), [Ground truth / benchmark quality](), [Databases](), [ann](), [datasets](), [Sorting]() Tags: [ai-vector](), [ann](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [blog-post](), [data](), [database-performance](), [database-trends](), [databases](), [datasets](), [embeddings](), [index](), [measurements](), [model](), [open-source](), [percona](), [points](), [report](), [run](), [search](), [sorting](), [vector-search](), [vectorsearch]() ### AI overview This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy. ### Source excerpt Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona. ## From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers DevFeed: [From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers]() Original publisher: [Read original article]() Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [LLM evaluation / benchmarking](), [Natural language processing](), [datasets]() Tags: [datasets](), [evaluation](), [models](), [post-training](), [research](), [responses](), [search]() ### AI overview The article introduces a rubric-based reward framework for grounded open-domain question answering. It generates query-specific rubrics from retrieved evidence and decomposes them into quality dimensions for fine-grained post-training supervision. The method improves composition, grounding, and instruction-following results over the stated baselines and evaluation datasets. ### Source excerpt Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the... ## Edge infrastructure under siege: what two independent datasets reveal about who's exploiting your perimeter DevFeed: [Edge infrastructure under siege: what two independent datasets reveal about who's exploiting your perimeter]() Original publisher: [Read original article]() Author: Research Special Operations Published: 2026-08-26T13:00:00Z Content type: article Language: en Sources: [Tenable Blog]() Topics: [vulnerability](), [telemetry]() Tags: [analysis](), [attacks](), [china](), [complexity](), [containers](), [datasets](), [edge](), [infrastructure](), [iran](), [ransomware](), [telemetry](), [vulnerability]() ### AI overview A joint Tenable-SentinelOne analysis finds state-sponsored and criminal actors converging on the same edge vulnerabilities. It compares exposure and remediation patterns across vendors and recommends faster patching, attack-surface reduction, and endpoint protection. ### Source excerpt A joint Tenable-SentinelOne analysis of 93 CVE-actor attribution pairs reveals that both state-sponsored actors and cybercriminals independently converge on the same edge infrastructure. Special thanks to SentinelOne® Incident Readiness & Response for their contributions to this publication. It is the shared attack surface where state-sponsored threat actors and financially motivated criminal groups independently converge -- not the province of a single adversary category, and not exclusively a nation-state problem, despite two years of headlines about China-nexus actors targeting Ivanti, Fortinet, and Palo Alto Networks. The data here tells a different and much broader story. One focused on vendors vs CVEs. Key Takeaways Two independent observation systems, Tenable exposure telemetry across thousands of customer containers and SentinelOne DFIR casework across 66 CVEs, converge 79% on the same vendor attack surfaces despite minimal CVE-level overlap. Twelve CVEs in the combined dataset have confirmed multi-nexus attribution: state-sponsored and criminal actors independently exploiting the same vulnerability, across five nexus categories (China, Russia, DPRK, Iran, ransomware). The exposure picture is flatter than the headlines suggest: Fortinet, the vendor most associated with edge-device attacks in the press, sits mid-pack on container-grain exposure (25%) -- well behind F5 (54%) and in a tight 10-point band with Check Point, Ivanti, and Citrix. 54% of customer environments running F5 products have at least one exposed, actively-exploited CVE; Citrix customers show the slowest remediation patterns at 461 days median time to patch. Remediation complexity, particularly of high priority CVEs, leads to a statistically significant 24-day remediation gap, leaving large windows of opportunity for attackers. The same product lines get hit again and again: Ivanti EPMM and Ivanti Connect Secure each show a newly exploited CVE roughly every 8.5 to 13 months. Leverage multiple d ## Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers DevFeed: [Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen Published: 2026-08-26T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [sentence-transformers](), [AI search](), [AI, ML & Data Engineering]() Tags: [community](), [datasets](), [embedding](), [embeddings](), [evaluation](), [guide](), [nlp](), [open-source](), [retrieval](), [sentence-transformers](), [training]() ### AI overview A tutorial on finetuning multi-vector embedding models with Sentence Transformers. It explains late-interaction token-level retrieval, training components, and domain-specific retrieval improvements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## An AI tool for prioritizing candidate biomarkers from wearable sensor data DevFeed: [An AI tool for prioritizing candidate biomarkers from wearable sensor data]() Original publisher: [Read original article]() Published: 2026-08-21T17:02:24Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [data](), [Time Series](), [datasets](), [Temporal data](), [Training AI Models](), [Model Development]() Tags: [agents](), [ai](), [analysis](), [data](), [datasets](), [generative-ai](), [health-bioscience](), [memory](), [reasoning](), [research](), [series](), [time-series](), [training](), [validation](), [workflow]() ### AI overview The Biomarker Discovery Framework is a supervised multi-agent system for prioritizing biomarker candidates from wearable-sensor data. It combines hypothesis generation, statistical analysis, model training, adversarial validation, and literature-grounded reasoning in a traceable six-phase workflow. Across three cohorts, it recovered known clinical signals, found convergent biomarkers across independent datasets, and improved downstream prediction when demographic features were included. ### Source excerpt Generative AI [Next page]()