Telegram必备的搜索引擎，极搜JISOU帮你精准找到，想要的群组、频道、视频、音乐

👉 t.me/jisou2?start=a_8247614025

Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hari Prasad [ view email ] [v1] Wed, 8 Jul 2026 12:20:19 UTC (1,323 KB) Full-text links: Access Paper: View a PDF of the paper titled A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents, by Hari Prasad View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is

Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Didula Samaraweera [ view email ] [v1] Wed, 8 Jul 2026 09:18:56 UTC (807 KB) Full-text links: Access Paper: View a PDF of the paper titled Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation, by Didula Samaraweera and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliogr

aphic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Architecture Generalization with MetaNCA

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Meet Barot PhD [ view email ] [v1] Wed, 8 Jul 2026 08:31:57 UTC (1,043 KB) Full-text links: Access Paper: View a PDF of the paper titled Architecture Generalization with MetaNCA, by Meet Barot and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) To

ggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haozhan Tang [ view email ] [v1] Wed, 8 Jul 2026 06:23:42 UTC (699 KB) Full-text links: Access Paper: View a PDF of the paper titled Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE, by Haozhan Tang and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Pa

pers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

The great AI data centre cover

Then $75 per month. Complete digital access to quality FT journalism on any device. Cancel anytime during your trial. Access to eight surprising articles a day, hand-picked by FT editors. For seamless reading, access content via the FT Edit page on and receive the FT Edit newsletter. Essential digital access to quality FT journalism on any device. Pay a year upfront and save 20%. Complete digital access to quality FT journalism with expert analysis from industry leaders. Pay a year upfront and save 20%. Check whether you already have access via your university or organisation. Terms & Conditions apply Discover all the plans currently available in your country Digital access for organisations. Includes exclusive features and content. See why over a million readers pay to read the Financial Times.

Mercor acquires Deeptune to build AI training environments

Share Today's frontier AI models can pass nearly any exam you give them, but put an agent inside a real workflow and it stalls on the first task an entry-level employee could handle. This is because the model lacks experience. Experience comes from doing real work: navigating messy documents, legacy enterprise software, and sprawling company context while making hundreds of small decisions along the way. It means knowing what to do when something breaks or when the next step isn't obvious. A model learns to perform well only by practicing repeatedly in the environment where the work takes place. Building environments rigorous enough for that kind of training is incredibly hard. That's where Deeptune excels. Reinforcement learning has reached the point where a model can learn almost any task that can be clea

rly defined and scored. The constraint has shifted to the environments themselves: the places where models practice the work and get measured on whether they did it well. Building environments like these is exceptionally difficult. Each one has to faithfully recreate the software people use, translate expert judgment into realistic tasks, and measure performance clearly enough for a model to improve. Mercor's experts are already doing much of this work by writing evals. Deeptune provides the missing piece: the software platform where that expertise becomes realistic training environments at scale. Every environment has three parts: the software where the work takes place, the tasks that define what the agent must accomplish, and the verifiers that determine whether it succeeded. Mercor has built the expert layer behind that system. Our network of more than five million domain experts creates the tasks and verifiers that translate human judgment into learning signals for frontier models. Through APEX, we measure model performance on economically valuable tasks across thousands of real-world workflows. Deeptune builds the software layer. Over the past two years, the team has recreated hundreds of enterprise applications — from spreadsheets to Salesforce — and built some of the most sophisticated training environments used by frontier AI labs. We've seen the quality of their work firsthand because Mercor is already a Deeptune customer. Together, Mercor's expert network and Deeptune's software platform make it possible to build realistic training environments across far more industries, roles, and workflows than either company could alone. None of that happens without the people building it. I first met Tim, founder and CEO of Deeptune, two years ago, long before we started talking about working together. I was impressed by the technical ambition of Deeptune and Tim's conviction that environments would become the defining bottleneck for AI, long before most of the industry saw it. He and his team executed on that vision with remarkable speed. That's why I became an early investor, and ultimately why it felt obvious to bring our companies together. Deeptune is a small, focused team of engineers and operators with deep ties to frontier research. Earlier this year, Andreessen Horowitz led Deeptune's $43 million Series A. Tim and the entire Deeptune team are joining Mercor as we expand our team in NYC. For the labs we work with, this means environments that ship faster and train better. For enterprises deploying agents into their own workflows, it means one partner that can build the environment, staff the experts, and tell you whether the agent is ready. Every frontier lab is scaling environment production, and every enterprise will need to do the same. Training agents is becoming one of the largest categories of work in the economy, and the demand cuts across every profession. That is what we are building toward, and it is how we organize human intelligence to power the AI economy. Welcome to Mercor, Deeptune.

OpenAI and Google sell AI models to blacklisted China groups

Get 2 months free with an annual subscription at was $59.88 now $49 . Access to eight surprising articles a day, hand-picked by FT editors. For seamless reading, access content via the FT Edit page on and receive the FT Edit newsletter. Then $75 per month. Complete digital access to quality FT journalism on any device. Cancel or change your plan anytime during your trial. Essential digital access to quality FT journalism on any device. Pay a year upfront and save 20%. Complete digital access to quality FT journalism with expert analysis from industry leaders. Pay a year upfront and save 20%. Check whether you already have access via your university or organisation. Terms & Conditions apply Discover all the plans currently available in your country Digital access for organisations. Includes exclusive features and con

tent. See why over a million readers pay to read the Financial Times.

Show HN: R3 – A Local Code Review Tool for You and Your AI Agent

I built r3 to scratch my own itch when reviewing long design doc and planning doc from AI. The chat interface is inherently bad at tracking multiple pieces of feedback across different parts of a document, so I created a lightweight local web UI to organize the review process.I have been using it on my own projects for one week, and it has made giving structured feedback to AI much easier. I thought others working with AI coding agents might find it useful too.I'd love to hear what you think and...

SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Muhammet Sami Yavuz [ view email ] [v1] Sat, 4 Jul 2026 21:04:57 UTC (2,164 KB) Full-text links: Access Paper: View a PDF of the paper titled SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data, by Muhammet Sami Yavuz and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connect

ed Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Uncertainty-gated selection for block

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Thomas Rossi [ view email ] [v1] Sat, 4 Jul 2026 20:41:42 UTC (28 KB) Full-text links: Access Paper: View a PDF of the paper titled Uncertainty-gated selection for block-sparse attention, by Thomas Rossi View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) Toggle scite S

mart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle ( What is ? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-

Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhoujie Hou [ view email ] [v1] Sat, 4 Jul 2026 13:45:39 UTC (1,399 KB) Full-text links: Access Paper: View a PDF of the paper titled Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic, by Zhoujie Hou and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle