AI + ML 18 Welsh government used Copilot for review to justify closing organization 18 Microsoft's Clippy for 21st century deployed to evaluate returns? Industry Wales chair brands it just 'wrong' SA Mathieson Thu 26 Mar 2026 // 10:15 UTC The Welsh government used Microsoft's Copilot to help write a review of an industry liaison body that it then scrapped, its chairman has told a Senedd committee. The government set up Industry Wales as a state-owned company to run sector forums for aerospace, automotive, and technology in 2013, adding a fourth for net zero-focused businesses in 2022. Gartner suggests Friday afternoon Copilot ban because tired users may be too lazy to check its mistakes READ MORE In January 2025, officials told Industry Wales it would be subject to a review. In August, it said it would be closed based on that review's conclusions as of March 31, 2026. The decision was publicly announced last October, with the government saying it had provided the company with a grant of £837,000 for 2025-26. Industry Wales chair Professor Keith Ridgway saw a copy of the unpublished review on January 9 to find it was based on 28 interviews with people from unnamed organizations, processed by Microsoft Copilot. "I was alarmed and made a point to the board that the review refers to Microsoft Copilot as being used to evaluate the returns," he told the Senedd's Public Accounts and Public Administration Committee on March 4. "I don't think you can rely on artificial intelligence to do that. It's just wrong." Ridgway said evidence in the review supported some trimming of Industry Wales's scope but included views backing a Wales-specific organization in the role rather than UK-wide industry bodies, which was not reflected in the conclusion. "I think it would have been very sensible to have brought the findings back to the board for validation and triangulation, not to use Microsoft Copilot in whatever use," he added. GitHub infuriates students by removing some models from free Copilot plan Critical Microsoft Excel bug weaponizes Copilot Agent for zero-click information disclosure attack Microsoft Copilot to hijack your browser... for your own convenience Microsoft 365 confirms new premium tier, stuffed with AI and few discounts The Welsh government confirmed that its staff made some use of Microsoft's AI tool in producing the review. "The use of Copilot during the review of Industry Wales was limited to producing full, accurate and unbiased transcripts of interviews, analyzing and grouping comments into common themes," it said in a statement. "Detailed analysis of the evidence, assessment of the options and preparation of the review was carried out by Welsh government officials." West Midlands Police earn red card over Copilot's imaginary football match READ MORE Tom Gifford, a member of the Senedd Public Accounts and Public Administration Committee, described the Welsh government's use of AI in deciding the fate of an organization it owned as " bonkers " when interviewed for political journalist Will Hayward's Substack newsletter. As part of Industry Wales's closure, its associated tech forum company, Technology Connected, said in February that it too will cease trading on March 31 . The company ran the annual Wales Tech Week industry event, which in 2025 attracted more than 4,000 visitors. Ridgway told the committee the Welsh Automotive Forum is also closing after more than two decades in operation. ® Share More about Copilot Microsoft More like these × More about Copilot Microsoft Narrower topics Active Directory Azure Bing BSoD Copilot+ PC Excel Exchange Server HoloLens Internet Explorer LinkedIn Microsoft 365 Microsoft Build Microsoft Edge Microsoft Fabric Microsoft Ignite Microsoft Office Microsoft Surface Microsoft Teams .NET Office 365 OS/2 Outlook Patch Tuesday Pluton SharePoint Skype SQL Server Visual Studio Visual Studio Code Windows Windows 10 Windows 11 Windows 7 Windows 8 Windows Server Windows Server 2003 Windows Server 2008 Windows Server 2012 Windows Server 2013 Windows Server 2016 Windows Subsystem for Linux Windows XP Xbox Xbox 360 Broader topics Bill Gates OpenAI More about Share 18 COMMENTS More about Copilot Microsoft More like these × More about Copilot Microsoft Narrower topics Active Directory Azure Bing BSoD Copilot+ PC Excel Exchange Server HoloLens Internet Explorer LinkedIn Microsoft 365 Microsoft Build Microsoft Edge Microsoft Fabric Microsoft Ignite Microsoft Office Microsoft Surface Microsoft Teams .NET Office 365 OS/2 Outlook Patch Tuesday Pluton SharePoint Skype SQL Server Visual Studio Visual Studio Code Windows Windows 10 Windows 11 Windows 7 Windows 8 Windows Server Windows Server 2003 Windows Server 2008 Windows Server 2012 Windows Server 2013 Windows Server 2016 Windows Subsystem for Linux Windows XP Xbox Xbox 360 Broader topics Bill Gates OpenAI TIP US OFF Send us news

David Sacks has used up his days as Donald Trump’s AI and crypto czar. Speaking with Bloomberg on Thursday, the longtime entrepreneur, investor, and podcaster confirmed that his non-consecutive 130-day stint as a special government employee is over and that he’s moving on to co-chair the President’s Council of Advisors on Science and Technology (PCAST) alongside senior White House technology adviser Michael Kratsios.  “I think moving forward as co-chair of PCAST, I can now make recommendations on not just AI but an expanded range of technology topics,” he told Bloomberg via a video interview. “So yes, this is how I’ll be involved moving forward.” What that means in practice is Sacks will be much further from the power center in Washington than since the outset of this second Trump administration. As AI czar, Sacks had a direct line to Trump and a hand in shaping policy. PCAST is a federal advisory body, so while it studies issues, produces reports, and sends recommendations up the chain, it doesn’t make policy. The council has existed in some form since FDR, though Sacks made a point to Bloomberg of noting that this particular iteration has “the most star power of any group like this” ever assembled, and it’s hard to argue he’s wrong. The initial 15 members include Nvidia’s Jensen Huang, Meta’s Mark Zuckerberg, Oracle’s Larry Ellison, Google co-founder Sergey Brin, Marc Andreessen, AMD’s Lisa Su, and Michael Dell, among others. (That’s a lot of billionaires.) Sacks told Bloomberg the council will take up AI, advanced semiconductors, quantum computing, and nuclear power, and that near-term attention will go toward pushing Trump’s national AI framework, released just last week. The framework is aimed at replacing what Sacks described to Bloomberg as a mess of conflicting state-level rules. “You’ve got 50 different states regulating this in 50 different ways,” he said, “and it’s creating a patchwork of regulation that’s difficult for our innovators to comply with.” What Sacks didn’t address head-on was why the transition is happening now and whether his recent comments were a factor. Earlier this month, on the popular “All In” podcast that he co-hosts, Sacks publicly urged the administration to find an exit from the U.S.-backed war with Iran, walking through a set of worsening scenarios — attacks on oil infrastructure in neighboring countries, the destruction of desalination plants, the possibility of nuclear use by Israel — and calling for a polite way out. Trump responded by telling reporters that Sacks hadn’t spoken to him about the war. (The U.S.-Israel war on Iran has now been going on for approximately 27 days.) Techcrunch event Disrupt 2026: The tech ecosystem, all in one room Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $400. Save up to $300 or 30% to TechCrunch Founder Summit 1,000+ founders and investors come together at TechCrunch Founder Summit 2026 for a full day focused on growth, execution, and real-world scaling. Learn from founders and investors who have shaped the industry. Connect with peers navigating similar growth stages. Walk away with tactics you can apply immediately Offer ends March 13. San Francisco, CA | October 13-15, 2026 REGISTER NOW Asked about the podcast episode on Thursday by Bloomberg, Sacks figuratively threw his hands in the air: “I’m not on the foreign policy team or the national security team,” he said, adding that his podcast comments represented his personal view, not an official one. For all the marquee names Sacks is bringing to PCAST, it’s worth reflecting on what the council has historically been, which is an advisory body with some influence in some administrations and almost none in others. President Obama’s version was seemingly the most productive on record, churning out 36 reports over eight years — two of which led to concrete policy changes, including an FDA rule that opened the market for over-the-counter hearing aids. President Trump’s first-term council, by contrast, took nearly three years just to name its first members, produced a handful of reports, and made no particular mark, while President Biden’s council skewed heavily academic — Nobel laureates, MacArthur fellows, National Academy members — and issued a modest number of reports before the administration ended. The current PCAST is a completely different animal, built almost entirely from the executive suites of the companies shaping the technology it will advise on. Now, Sacks is again one of those unencumbered executives, free to resume his life as an investor and entrepreneur. A spokesperson for Craft Ventures, the firm Sacks co-founded and where he remains a partner, has not yet responded to related questions about next steps; TechCrunch reported last year on the ethics waivers Sacks obtained to maintain financial stakes in AI and crypto companies while shaping federal policy in both areas — an arrangement that drew sharp criticism from ethics experts and lawmakers. Topics AI , david sacks , Government & Policy , TC Connie Loizos Editor in Chief & General Manager Loizos has been reporting on Silicon Valley since the late ’90s, when she joined the original Red Herring magazine. Previously the Silicon Valley Editor of TechCrunch, she was named Editor in Chief and General Manager of TechCrunch in September 2023. She’s also the founder of StrictlyVC, a daily e-newsletter and lecture series acquired by Yahoo in August 2023 and now operated as a sub brand of TechCrunch. You can contact or verify outreach from Connie by emailing connie@strictlyvc.com or connie@techcrunch.com , or via encrypted message at ConnieLoizos.53 on Signal. View Bio April 30 San Francisco, CA StrictlyVC kicks off the year in SF. Get in the room for unfiltered fireside chats with industry leaders, insider VC insights, and high-value connections that actually move the needle. Tickets are limited. REGISTER NOW Most Popular Why OpenAI really shut down Sora Connie Loizos Anthropic’s Claude popularity with paying consumers is skyrocketing Julie Bort Waymo’s skyrocketing ridership in one chart Kirsten Korosec A major hacking tool has leaked online, putting millions of iPhones at risk. Here’s what you need to know. Lorenzo Franceschi-Bicchierai The AI skills gap is here, says AI company, and power users are pulling ahead Rebecca Bellan Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’ Sarah Perez Kentucky woman rejects $26M offer to turn her farm into a data center Graham Starr Loading the next article Error loading the next article X LinkedIn Facebook Instagram youTube Mastodon Threads Bluesky TechCrunch Staff Contact Us Advertise Crunchboard Jobs Site Map Terms of Service Privacy Policy RSS Terms of Use Code of Conduct Kalshi Copilot Blue Origin WordPress Bezos Tech Layoffs ChatGPT © 2026 TechCrunch Media LLC.

--> Computer Science > Artificial Intelligence arXiv:2512.21782 (cs) [Submitted on 25 Dec 2025 ( v1 ), last revised 30 Mar 2026 (this version, v2)] Title: Accelerating Scientific Discovery with Autonomous Goal-evolving Agents Authors: Yuanqi Du , Botao Yu , Tianyu Liu , Tony Shen , Junwu Chen , Jan G. Rittig , Kunyang Sun , Yikun Zhang , Aarti Krishnan , Yu Zhang , Daniel Rosen , Rosali Pirone , Zhangde Song , Bo Zhou , Cassandra Masschelein , Yingze Wang , Haorui Wang , Haojun Jia , Chao Zhang , Hongyu Zhao , Martin Ester , Nir Hacohen , Teresa Head-Gordon , Carla P. Gomes , Huan Sun , Chenru Duan , Philippe Schwaller , Wengong Jin View a PDF of the paper titled Accelerating Scientific Discovery with Autonomous Goal-evolving Agents, by Yuanqi Du and 27 other authors View PDF HTML (experimental) Abstract: There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science, these objectives may only be imperfect proxies. We argue that automating objective function design is a central, yet unmet need for scientific discovery agents. In this work, we introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to address this challenge. SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions, while an inner loop performs solution optimization under the current objectives. This bi-level design enables systematic exploration of the space of objectives and their trade-offs, rather than treating them as fixed inputs. We demonstrate the framework through a wide range of design applications, including antibiotics, nanobodies, functional DNA sequences, inorganic materials, and chemical processes. Notably, our experimental validation identifies a structurally novel hit with promising potency and safety profiles for E. coli in the antibiotic design task, and three de novo PD-L1 binders in the nanobody design task. These results suggest that automating objective formulation can substantially improve the effectiveness of scientific discovery agents. Subjects: Artificial Intelligence (cs.AI) ; Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph) Cite as: arXiv:2512.21782 [cs.AI] (or arXiv:2512.21782v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2512.21782 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuanqi Du [ view email ] [v1] Thu, 25 Dec 2025 20:54:41 UTC (15,789 KB) [v2] Mon, 30 Mar 2026 01:39:19 UTC (17,043 KB) Full-text links: Access Paper: View a PDF of the paper titled Accelerating Scientific Discovery with Autonomous Goal-evolving Agents, by Yuanqi Du and 27 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2025-12 Change to browse by: cond-mat cond-mat.mtrl-sci cs cs.LG physics physics.chem-ph References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... 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Will Knight Zeyi Yang Business Mar 27, 2026 5:46 PM AI Research Is Getting Harder to Separate From Geopolitics A policy change announced by NeurIPS, the world’s leading AI research conference, drew widespread backlash from Chinese researchers this week and then was quickly reversed. Photo-Illustration: WIRED Staff; Getty Images Save this story Save this story The world’s top AI research conference, the Conference on Neural Information Processing Systems—better known as NeurIPS —became the latest organization this week to become embroiled in a growing clash between geopolitics and global scientific collaboration. The conference’s organizers announced and then quickly reversed controversial new restrictions for international participants after Chinese AI researchers threatened to boycott the event. “This is a potential watershed moment,” says Paul Triolo, a partner at the advisory firm DGA-Albright Stonebridge who studies US-China relations. Triolo argues that attracting Chinese researchers to NeurIPS is beneficial to US interests, but some American officials have pushed for American and Chinese scientists to decouple their work—especially in AI, which has become a particularly sensitive topic in Washington. The incident could deepen political tensions around AI research, as well as dissuade Chinese scientists from working at US universities and tech companies in the future. “At some level now it is going to be hard to keep basic AI research out of the [political] picture,” Triolo says. In its annual handbook for paper submissions, issued in mid-March, NeurIPS organizers announced updated restrictions for participation. The rules stated that the event could not provide services including “peer review, editing, and publishing” to any organizations subject to US sanctions, and linked to a database of sanctioned entities. It included companies and organizations on the Bureau of Industry and Security’s entity list and those on another list with alleged ties to the Chinese military. The new rules would have affected researchers at Chinese companies like Tencent and Huawei who regularly present work at NeurIPS. The database also includes entities from other countries such as Russia and Iran. The US places limits on doing business with these organizations, but there are no rules around academic publishing or conference participation. The NeurIPS handbook has since been updated to specify that the restrictions apply only to Specially Designated Nationals and Blocked Persons , a list used primarily for terrorist groups and criminal organizations. “In preparing the NeurIPS 2026 handbook, we included a link to a US government sanctions tool that covers a significantly broader set of restrictions than those NeurIPS is actually required to follow,” the event’s organizers said in a statement issued Friday. “This error was due to miscommunication between the NeurIPS Foundation and our legal team.” Before they reversed course, the conference organizers initially said that the new rule was “about legal requirements that apply to the NeurIPS Foundation, which is responsible for complying with sanctions,” adding that it was seeking legal consultation on the issue. Immediate Backlash The new rule drew swift backlash from AI researchers around the world, particularly in China, which produces a large quantity of cutting-edge machine learning papers and is home to a growing share of the world’s top AI talent. Several academic groups there issued statements condemning the measure and, more importantly, discouraging Chinese academics from attending NeurIPS in the future. Some urged Chinese academics to contribute instead to domestic research conferences, potentially helping increase the country’s influence in relevant science and tech fields. The China Association of Science and Technology (CAST), an influential government-affiliated organization for scientists and engineers, said Thursday that it would stop providing funding for Chinese scholars traveling to attend NeurIPS and would use the money instead to support domestic and international conferences that “respect the rights of Chinese scholars.” CAST also said it will no longer count publications at the 2026 NeurIPS conference as academic achievements when evaluating future research funding. It’s unclear if the organization will reverse course now that NeurIPS has walked back the new rule. At least six scholars have publicly said they turned down invitations to serve as area chairs at NeurIPS this year due to the sanctions policy. Others said they would decline to participate as paper reviewers. “I have served as [area chair] for NeurIPS every year since 2020. Just declined,” Nan Jiang, a machine learning researcher at the University of Illinois Urbana-Champaign, said in a social media post . “At least the organizers owe the community an explanation why they are the only major ML venue adopting such a policy.” “That’s one less area chair responsibility for me. If I hadn’t already committed to colleagues, I wouldn’t submit a paper this year either,” wrote Yasin Abbasi-Yadkori, a researcher at the AI firm Sapient Intelligence. Fraught Links The controversy reflects the increasingly fraught political landscape that top researchers, many of whom have been long accustomed to collaborating with international colleagues, now have to navigate. Although progress in AI has often depended on this kind of openness, rising tensions between the US and China in recent years have significantly complicated the picture. Thousands of Chinese scientists take part in NeurIPS annually. In 2025, roughly half of the papers presented at the event came from researchers with a Chinese academic background, according to an analysis conducted by The Economist. Tsinghua University, widely considered the top university in China, was listed on 390 NeurIPS papers, more than any other institution or company. Researchers from Alibaba also received one of the conference’s best-paper awards for work related to the company’s open source AI model Qwen . A previous WIRED analysis shows that despite rising tensions between Washington and Beijing, US and Chinese researchers have largely continued to collaborate on work published at NeurIPS. But the latest sanctions saga could strain those ties. “NeurIPS’ prosperity comes from the joint efforts of researchers worldwide, and its growth and success have long been supported by sponsorships from some of the sanctioned entities too,” Yuliang Xiu, an assistant professor in digital graphics at the Westlake University in China, wrote on social media , adding that he had also declined an invitation to serve as an area chair at the conference. This is an edition of Zeyi Yang and Louise Matsakis ’ Made in China newsletter . Read previous newsletters here.

--> Computer Science > Robotics arXiv:2603.27194 (cs) [Submitted on 28 Mar 2026] Title: Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence Authors: Kai Tian , Jialun Wang , Chuan Lin , Guangjie Han , Shengchao Zhu , Ying Liu , Qian Zhu View a PDF of the paper titled Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence, by Kai Tian and 6 other authors View PDF HTML (experimental) Abstract: With the rapid advancement of underwater net-working and multi-agent coordination technologies, autonomous underwater vehicle (AUV) ad-hoc networks have emerged as a pivotal framework for executing complex maritime missions, such as multi-target tracking. However, traditional data-centricarchitectures struggle to maintain operational consistency under highly dynamic topological fluctuations and severely constrained acoustic communication bandwidth. This article proposes a scene-adaptive embodied intelligence (EI) architecture for multi-AUV ad-hoc networks, which re-envisions AUVs as embodied entities by integrating perception, decision-making, and physical execution into a unified cognitive loop. To materialize the functional interaction between these layers, we define a beacon-based communication and control model that treats the communication link as a dynamic constraint-aware channel, effectively bridging the gap between high-level policy inference and decentralized physical actuation. Specifically, the proposed architecture employs a three-layer functional framework and introduces a Scene-Adaptive MARL (SA-MARL) algorithm featuring a dual-path critic mechanism. By integrating a scene critic network and a general critic network through a weight-based dynamic fusion process, SA-MARL effectively decouples specialized tracking tasks from global safety constraints, facilitating autonomous policy evolution. Evaluation results demonstrate that the proposedscheme significantly accelerates policy convergence and achieves superior tracking accuracy compared to mainstream MARL approaches, maintaining robust performance even under intense environmental interference and fluid topological shifts. Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.27194 [cs.RO] (or arXiv:2603.27194v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.27194 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kai Tian [ view email ] [v1] Sat, 28 Mar 2026 08:48:22 UTC (28,700 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence, by Kai Tian and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Cryptography and Security arXiv:2410.09134 (cs) [Submitted on 11 Oct 2024 ( v1 ), last revised 29 Mar 2026 (this version, v2)] Title: Multi-Agent Actor-Critics in Autonomous Cyber Defense Authors: Mingjun Wang , Remington Dechene View a PDF of the paper titled Multi-Agent Actor-Critics in Autonomous Cyber Defense, by Mingjun Wang and 1 other authors View PDF HTML (experimental) Abstract: The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. This paper explores the application of Multi-Agent Actor-Critic algorithms which provides a general form in Multi-Agent learning to cyber defense, leveraging the collaborative interactions among multiple agents to detect, mitigate, and respond to cyber threats. We demonstrate each agent is able to learn quickly and counter act on the threats autonomously using MADRL in simulated cyber-attack scenarios. The results indicate that MADRL can significantly enhance the capability of autonomous cyber defense systems, paving the way for more intelligent cybersecurity strategies. This study contributes to the growing body of knowledge on leveraging artificial intelligence for cybersecurity and sheds light for future research and development in autonomous cyber operations. Comments: 6 pages. 2 figures Subjects: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2410.09134 [cs.CR] (or arXiv:2410.09134v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2410.09134 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mingjun Wang [ view email ] [v1] Fri, 11 Oct 2024 15:15:09 UTC (447 KB) [v2] Sun, 29 Mar 2026 14:41:45 UTC (446 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-Agent Actor-Critics in Autonomous Cyber Defense, by Mingjun Wang and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2024-10 Change to browse by: cs cs.AI cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Robotics arXiv:2603.27181 (cs) [Submitted on 28 Mar 2026] Title: An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion Authors: Dikai Shang , Jingyue Zhao , Shi Xu , Nanyang Ye , Lei Wang View a PDF of the paper titled An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion, by Dikai Shang and Jingyue Zhao and Shi Xu and Nanyang Ye and Lei Wang View PDF HTML (experimental) Abstract: Achieving safe, high-speed autonomous flight in complex environments with static, dynamic, or mixed obstacles remains challenging, as a single perception modality is incomplete. Depth cameras are effective for static objects but suffer from motion blur at high speeds. Conversely, event cameras excel at capturing rapid motion but struggle to perceive static scenes. To exploit the complementary strengths of both sensors, we propose an end-to-end flight control network that achieves feature-level fusion of depth images and event data through a bidirectional crossattention module. The end-to-end network is trained via imitation learning, which relies on high-quality supervision. Building on this insight, we design an efficient expert planner using Spherical Principal Search (SPS). This planner reduces computational complexity from $O(n^2)$ to $O(n)$ while generating smoother trajectories, achieving over 80% success rate at 17m/s--nearly 20% higher than traditional planners. Simulation experiments show that our method attains a 70-80% success rate at 17 m/s across varied scenes, surpassing single-modality and unidirectional fusion models by 10-20%. These results demonstrate that bidirectional fusion effectively integrates event and depth information, enabling more reliable obstacle avoidance in complex environments with both static and dynamic objects. Comments: 7 pages, 10 figures Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.27181 [cs.RO] (or arXiv:2603.27181v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.27181 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Dikai Shang [ view email ] [v1] Sat, 28 Mar 2026 07:57:15 UTC (12,379 KB) Full-text links: Access Paper: View a PDF of the paper titled An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion, by Dikai Shang and Jingyue Zhao and Shi Xu and Nanyang Ye and Lei Wang View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2511.18151 (cs) [Submitted on 22 Nov 2025 ( v1 ), last revised 28 Mar 2026 (this version, v3)] Title: AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems Authors: Rajat Bhattacharjya , Sing-Yao Wu , Hyunwoo Oh , Chaewon Nam , Suyeon Koo , Mohsen Imani , Elaheh Bozorgzadeh , Nikil Dutt View a PDF of the paper titled AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems, by Rajat Bhattacharjya and 7 other authors View PDF HTML (experimental) Abstract: Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that onboard CNNs cannot provide. While Vision-Language Models (VLMs) offer this semantic reasoning, their high resource demands make on-device deployment infeasible, and naive cloud offloading fails under the low-bandwidth, unstable networks endemic to disaster zones. We present AVERY, an intent-driven adaptive split computing framework for efficient VLM deployment on resource-constrained platforms. AVERY is motivated by the observation that operator intent must be treated as a first-class system objective, since missions such as broad situational monitoring and precise, spatially grounded investigation require different semantic products, latency targets, and resource allocations. To reflect this, AVERY advances split computing beyond traditional depth-wise partitioning through a functional, cognitive-inspired dual-stream split: a high-frequency, low-resolution Context stream for real-time awareness, and a low-frequency, high-fidelity Insight stream for deep analysis. This design enables a hierarchical split strategy: computation is first separated by function, then partitioned depth-wise across edge and cloud when the Insight stream is required. A lightweight, self-aware onboard controller monitors network conditions and operator intent to select from pre-trained compression models, navigating the accuracy-throughput trade-off at runtime. Evaluated using LISA-7B in an edge-cloud setting under fluctuating network conditions, AVERY achieves 11.2% higher accuracy than raw image compression, 93.98% lower energy consumption than full-edge execution, and average accuracy within 0.75% of the static High-Accuracy baseline during dynamic adaptation. Overall, AVERY enhances mission efficiency and enables real-time, queryable intelligence in dynamic disaster environments. Comments: Paper is currently under review. Authors' version posted for personal use and not for redistribution. Previous version of the preprint was titled: 'AVERY: Adaptive VLM Split Computing through Embodied Self-Awareness for Efficient Disaster Response Systems' Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Hardware Architecture (cs.AR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2511.18151 [cs.DC] (or arXiv:2511.18151v3 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2511.18151 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rajat Bhattacharjya [ view email ] [v1] Sat, 22 Nov 2025 18:42:04 UTC (6,160 KB) [v2] Mon, 2 Feb 2026 09:53:38 UTC (6,210 KB) [v3] Sat, 28 Mar 2026 02:11:38 UTC (15,743 KB) Full-text links: Access Paper: View a PDF of the paper titled AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems, by Rajat Bhattacharjya and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2025-11 Change to browse by: cs cs.AR cs.CV cs.LG cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Human-Computer Interaction arXiv:2603.28066 (cs) [Submitted on 30 Mar 2026] Title: Synonymix: Unified Group Personas for Generative Simulations Authors: Huanxing Chen , Aditesh Kumar View a PDF of the paper titled Synonymix: Unified Group Personas for Generative Simulations, by Huanxing Chen and Aditesh Kumar View PDF HTML (experimental) Abstract: Generative agent simulations operate at two scales: individual personas for character interaction, and population models for collective behavior analysis and intervention testing. We propose a third scale: meso-level simulation - interaction with group-level representations that retain grounding in rich individual experience. To enable this, we present Synonymix, a pipeline that constructs a "unigraph" from multiple life story personas via graph-based abstraction and merging, producing a queryable collective representation that can be explored for sensemaking or sampled for synthetic persona generation. Evaluating synthetic agents on General Social Survey items, we demonstrate behavioral signal preservation beyond demographic baselines (p<0.001, r=0.59) with demonstrable privacy guarantee (max source contribution <13%). We invite discussion on interaction modalities enabled by meso-level simulations, and whether "high-fidelity" personas can ever capture the texture of lived experience. Comments: 6 pages (excluding appendix), 3 figures, CHI'26 Extended Abstract (Poster) Subjects: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.28066 [cs.HC] (or arXiv:2603.28066v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2603.28066 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Huanxing Chen [ view email ] [v1] Mon, 30 Mar 2026 06:13:45 UTC (2,821 KB) Full-text links: Access Paper: View a PDF of the paper titled Synonymix: Unified Group Personas for Generative Simulations, by Huanxing Chen and Aditesh Kumar View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Artificial Intelligence arXiv:2603.28010 (cs) [Submitted on 30 Mar 2026] Title: HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System Authors: Xujia Li , Xin Li , Junquan Huang , Beirong Cui , Zibin Wu , Lei Chen View a PDF of the paper titled HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System, by Xujia Li and 5 other authors View PDF HTML (experimental) Abstract: Heterogeneous Multi-Embodied Agent Systems involve coordinating multiple embodied agents with diverse capabilities to accomplish tasks in dynamic environments. This process requires the collection, generation, and consumption of massive, heterogeneous data, which primarily falls into three categories: static knowledge regarding the agents, tasks, and environments; multimodal training datasets tailored for various AI models; and high-frequency sensor streams. However, existing frameworks lack a unified data management infrastructure to support the real-world deployment of such systems. To address this gap, we present \textbf{HeteroHub}, a data-centric framework that integrates static metadata, task-aligned training corpora, and real-time data streams. The framework supports task-aware model training, context-sensitive execution, and closed-loop control driven by real-world feedback. In our demonstration, HeteroHub successfully coordinates multiple embodied AI agents to execute complex tasks, illustrating how a robust data management framework can enable scalable, maintainable, and evolvable embodied AI systems. Comments: 4 pages, 2 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.28010 [cs.AI] (or arXiv:2603.28010v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.28010 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Junquan Huang [ view email ] [v1] Mon, 30 Mar 2026 04:01:05 UTC (1,270 KB) Full-text links: Access Paper: View a PDF of the paper titled HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System, by Xujia Li and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Robotics arXiv:2603.27273 (cs) [Submitted on 28 Mar 2026] Title: Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors Authors: Mohamed Elgouhary , Amr S. El-Wakeel View a PDF of the paper titled Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors, by Mohamed Elgouhary and Amr S. El-Wakeel View PDF HTML (experimental) Abstract: Modular autonomous driving systems must coordinate global progress objectives with local safety-driven reactions under imperfect sensing and strict real-time constraints. This paper presents a ROS2-native arbitration module that continuously fuses the outputs of two unchanged and interpretable controllers: a global reference-tracking controller based on Pure Pursuit and a reactive LiDAR-based Gap controller. At each control step, both controllers propose Ackermann commands, and a PPO-trained policy predicts a continuous gate from a compact feature observation to produce a single fused drive command, augmented with practical safety checks. For comparison under identical ROS topic inputs and control rate, we implement a lightweight sampling-based predictive baseline. Robustness is evaluated using a ROS2 impairment protocol that injects LiDAR noise, delay, and dropout, and additionally sweeps forward-cone false short-range outliers. In a repeatable close-proximity passing scenario, we report safe success and failure rates together with per-step end-to-end controller runtime as sensing stress increases. The study is intended as a command-level robustness evaluation in a modular ROS2 setting, not as a replacement for planning-level interaction reasoning. Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Systems and Control (eess.SY) Cite as: arXiv:2603.27273 [cs.RO] (or arXiv:2603.27273v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.27273 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Amr El-Wakeel [ view email ] [v1] Sat, 28 Mar 2026 14:09:55 UTC (731 KB) Full-text links: Access Paper: View a PDF of the paper titled Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors, by Mohamed Elgouhary and Amr S. El-Wakeel View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Robotics arXiv:2603.28032 (cs) [Submitted on 30 Mar 2026] Title: CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence Authors: Tianle Zeng , Hanxuan Chen , Yanci Wen , Hong Zhang View a PDF of the paper titled CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence, by Tianle Zeng and 3 other authors View PDF HTML (experimental) Abstract: The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open-source platforms remain domain-segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge-based co-simulation introduces synchronization overhead and cannot guarantee strict spatial-temporal consistency. We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero-modification code reuse. Within a shared physics tick and rendering pipeline, CARLA-Air delivers photorealistic environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air-ground embodied intelligence workloads spanning cooperation, embodied navigation and vision-language action, multi-modal perception and dataset construction, and reinforcement-learning-based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure. Released with prebuilt binaries and full source: this https URL Comments: Prebuilt binaries, project page, full source code, and community discussion group are all available at: this https URL Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC) Cite as: arXiv:2603.28032 [cs.RO] (or arXiv:2603.28032v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.28032 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Tianle Zeng [ view email ] [v1] Mon, 30 Mar 2026 04:49:29 UTC (12,872 KB) Full-text links: Access Paper: View a PDF of the paper titled CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence, by Tianle Zeng and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.CV cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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AI + ML 39 Anthropic struggling with Chinese competition, its own safety obsession 39 The maker of Claude faces headwinds as it rushes to go public Thomas Claburn Sat 28 Mar 2026 // 14:01 UTC Anthropic, riding a wave of goodwill after resisting demands from the US Defense Department to soften model safeguards, is reportedly planning to go public as soon as Q4 2026. That may not be soon enough to avoid the undertow of financial pressure, competition from China, and the challenge of delivering AI models that provide some measure of safety without sacrificing too much utility. The company's financial picture isn't pretty. In a legal filing [PDF] earlier this month, CFO Krishna Rao revealed that the company, which has raised $30 billion , has only managed to make $5 billion while spending $10 billion on inference and training alone. Against this backdrop, recent cost-saving moves designed to reduce token demand during peak hours fail to inspire optimism. But there's a more fundamental risk – remaining relevant in the face of increasingly capable competition from China. On Monday, the US-China Economic and Security Review Commission issued a report assessing the competitive threat posed by Chinese AI companies. "Chinese labs have narrowed performance gaps with top Western large language models," the report says. "They have also developed key architectural and training advances that are now industry standards." The success of Chinese AI companies can be seen in the popularity metrics of sites like LLM Rankings , which tracks the most popular models on OpenRouter, an API and marketplace for providing developers with access to multiple AI models through a single interface. Presently, the top six models in that ranking come from Chinese AI companies. They include: MiMo-V2-Pro (Xiaomi), Step 3.5 Flash (stepfun), DeepSeek V3.2 (DeepSeek), MiniMax M2.7 (MiniMax), MiniMax M2.5 (MiniMax), and GLM 5 Turbo (z.ai).  Anthropic's Claude Opus 4.6 and Claude Sonnet 4.6 currently occupy slots seven and eight. Perhaps more significantly, Anthropic has seen its market share slip from 29.1 percent on March 22, 2025 to 13.3 percent on March 21, 2026. That's only one measurement and Anthropic has been doing well in the enterprise market , enough to worry rival OpenAI.  But absent US government protectionism, the US AI biz faces rivals who deliver similar results for one tenth of the price or less. When Kilo Code compared the cost of Claude 4.6 Opus to MiniMax M2.7 earlier a few days ago, it found "MiniMax M2.7 delivered 90 percent of the quality for 7 percent of the cost ($0.27 total vs $3.67)." Anthropic claims that MiniMax, Moonshot AI, and DeepSeek copied or "distilled" its Claude models (which were themselves built from content often copied without consent). But given the underwhelming track record of US efforts to encourage Chinese respect for US intellectual property, it seems doubtful Anthropic's appeal for "a coordinated response across the AI industry, cloud providers, and policymakers" will be enough to sustain the pricing needed to reach positive cash flow in a reasonable time frame. Folk are getting dangerously attached to AI that always tells them they're right Apple's last tower topples… and the others will follow Anthropic tweaks timed usage limits to discourage Claude demand during peak hours Using AI to code does not mean your code is more secure Finally, Anthropic faces the challenge of being all things to all customers. The company has built its brand around safety, and has won over many corporate customers and consumers as a result. But it has alienated the current US administration and its effort to maintain model safety risks pushing away the security community and developers who do security work. The Register has corresponded with a handful of security researchers who all expressed disillusionment with how the Claude model family has performed for bug hunting and exploit testing in recent months. "It's very, very, very heavily censored now," said one security researcher who asked not to be identified in a conversation with The Register . "The CBRN (Chemical, Biological, Radiological, and Nuclear) blocker has been cranked way up. …We're all abandoning it as now it's triggering a stupid number of false positives." To demonstrate the model's hypersensitivity, we were provided with a screenshot showing just how sensitive Opus can be – it flagged a chat about Tony-award winning musical Urinetown as unsafe. Anthropic confirmed to The Register that there have been security-focused changes, pointing to safeguards added with the release of Opus 4.6 in February. "As part of our ongoing safety commitments as described in our Claude Opus 4.6 announcement , we are rolling out new cyber safeguards for Claude Opus 4.6," the company's documentation explains. "These safeguards are designed to automatically detect and block requests that may indicate prohibited cybersecurity usage under our Usage Policy." The company concedes, "In some cases, these guardrails may also block dual-use cybersecurity activities with legitimate defensive purposes, such as vulnerability discovery." Indeed, there are people posting on social media who claim to have run afoul of these guardrails for security-related work.  Anthropic does provide a form that security professionals can use to petition for an exemption, but from what we're told, not everyone who applies gets cleared and the process is not quick. The researcher, who claims to have just cancelled a $200/month Max subscription, reported knowing around seven people who have ditched Claude recently over its increased rate of refusal for security and vulnerability work. One such person we were referred to echoed this sentiment. "Yes, as of late it seems that US firms have gone a bit too far in attempting to make their services 'helpful, harmless, and honest,'" we were told. "I've noticed Claude not just refusing to answer questions but actively avoiding topics and attempting to steer the conversation away from certain topics even in a research context. Security research is especially difficult." This individual views the lack of transparency by US commercial AI companies as a problem. "They say it's an existential threat but then demand unaccountable control of them?" A third researcher who corresponded with The Register said, "At the moment what I'm using is this new thing called MiniMax and it's a distilled version of Claude. Doesn't matter that it's Chinese. It's cheap and as good as, if not better, than Claude's best models right now." While Anthropic prepares to go public, at least some of the public is going elsewhere. ® Share More about AI Development Software More like these &times; More about AI Development Software Narrower topics Accessibility AdBlock Plus AIOps App Application Delivery Controller Audacity Confluence Database DeepSeek Devops FOSDEM FOSS Gemini Google AI GPT-3 GPT-4 Grab Graphics Interchange Format IDE Image compression Jenkins Large Language Model Legacy Technology LibreOffice Machine Learning Map MCubed Microsoft 365 Microsoft Office Microsoft Teams Mobile Device Management Neural Networks NLP OpenOffice Programming Language QR code Retrieval Augmented Generation Retro computing Search Engine Software Bill of Materials Software bug Software License Star Wars Tensor Processing Unit Text Editor TOPS User interface Visual Studio Visual Studio Code WebAssembly Web Browser WordPress Broader topics Self-driving Car More about Share 39 COMMENTS More about AI Development Software More like these &times; More about AI Development Software Narrower topics Accessibility AdBlock Plus AIOps App Application Delivery Controller Audacity Confluence Database DeepSeek Devops FOSDEM FOSS Gemini Google AI GPT-3 GPT-4 Grab Graphics Interchange Format IDE Image compression Jenkins Large Language Model Legacy Technology LibreOffice Machine Learning Map MCubed Microsoft 365 Microsoft Office Microsoft Teams Mobile Device Management Neural Networks NLP OpenOffice Programming Language QR code Retrieval Augmented Generation Retro computing Search Engine Software Bill of Materials Software bug Software License Star Wars Tensor Processing Unit Text Editor TOPS User interface Visual Studio Visual Studio Code WebAssembly Web Browser WordPress Broader topics Self-driving Car TIP US OFF Send us news

--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.26831 (cs) [Submitted on 27 Mar 2026] Title: Envisioning global urban development with satellite imagery and generative AI Authors: Kailai Sun , Yuebing Liang , Mingyi He , Yunhan Zheng , Alok Prakash , Shenhao Wang , Jinhua Zhao , Alex "Sandy'' Pentland View a PDF of the paper titled Envisioning global urban development with satellite imagery and generative AI, by Kailai Sun and 7 other authors View PDF HTML (experimental) Abstract: Urban development has been a defining force in human history, shaping cities for centuries. However, past studies mostly analyze such development as predictive tasks, failing to reflect its generative nature. Therefore, this study designs a multimodal generative AI framework to envision sustainable urban development at a global scale. By integrating prompts and geospatial controls, our framework can generate high-fidelity, diverse, and realistic urban satellite imagery across the 500 largest metropolitan areas worldwide. It enables users to specify urban development goals, creating new images that align with them while offering diverse scenarios whose appearance can be controlled with text prompts and geospatial constraints. It also facilitates urban redevelopment practices by learning from the surrounding environment. Beyond visual synthesis, we find that it encodes and interprets latent representations of urban form for global cross-city learning, successfully transferring styles of urban environments across a global spatial network. The latent representations can also enhance downstream prediction tasks such as carbon emission prediction. Further, human expert evaluation confirms that our generated urban images are comparable to real urban images. Overall, this study presents innovative approaches for accelerated urban planning and supports scenario-based planning processes for worldwide cities. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.26831 [cs.CV] (or arXiv:2603.26831v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.26831 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kailai Sun [ view email ] [v1] Fri, 27 Mar 2026 05:07:45 UTC (9,227 KB) Full-text links: Access Paper: View a PDF of the paper titled Envisioning global urban development with satellite imagery and generative AI, by Kailai Sun and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Artificial Intelligence arXiv:2603.27738 (cs) [Submitted on 29 Mar 2026] Title: TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science Authors: Kaikai Zhang , Xiang Wang , Haoluo Zhao , Nan Chen , Mengyang Yu Jing-Jia Luo , Tao Song , Fan Meng View a PDF of the paper titled TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science, by Kaikai Zhang and 6 other authors View PDF HTML (experimental) Abstract: Artificial intelligence (AI) has achieved breakthroughs comparable to traditional numerical models in data-driven weather forecasting, yet it remains essentially statistical fitting and struggles to uncover the physical causal mechanisms of the atmosphere. Physics-oriented mechanism research still heavily relies on domain knowledge and cumbersome engineering operations of human scientists, becoming a bottleneck restricting the efficiency of Earth system science exploration. Here, we propose TianJi - the first "AI meteorologist" system capable of autonomously driving complex numerical models to verify physical mechanisms. Powered by a large language model-driven multi-agent architecture, TianJi can autonomously conduct literature research and generate scientific hypotheses. We further decouple scientific research into cognitive planning and engineering execution: the meta-planner interprets hypotheses and devises experimental roadmaps, while a cohort of specialized worker agents collaboratively complete data preparation, model configuration, and multi-dimensional result analysis. In two classic atmospheric dynamic scenarios (squall-line cold pools and typhoon track deflections), TianJi accomplishes expert-level end-to-end experimental operations with zero human intervention, compressing the research cycle to a few hours. It also delivers detailed result analyses and autonomously judges and explains the validity of the hypotheses from outputs. TianJi reveals that the role of AI in Earth system science is transitioning from a "black-box predictor" to an "interpretable scientific collaborator", offering a new paradigm for high-throughput exploration of scientific mechanisms. Subjects: Artificial Intelligence (cs.AI) MSC classes: 68T42, 86A10 ACM classes: I.2.11; J.2; I.2.1 Cite as: arXiv:2603.27738 [cs.AI] (or arXiv:2603.27738v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.27738 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Fan Meng [ view email ] [v1] Sun, 29 Mar 2026 15:30:50 UTC (9,199 KB) Full-text links: Access Paper: View a PDF of the paper titled TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science, by Kaikai Zhang and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Artificial Intelligence arXiv:2603.28015 (cs) [Submitted on 30 Mar 2026] Title: What an Autonomous Agent Discovers About Molecular Transformer Design: Does It Transfer? Authors: Edward Wijaya View a PDF of the paper titled What an Autonomous Agent Discovers About Molecular Transformer Design: Does It Transfer?, by Edward Wijaya View PDF HTML (experimental) Abstract: Deep learning models for drug-like molecules and proteins overwhelmingly reuse transformer architectures designed for natural language, yet whether molecular sequences benefit from different designs has not been systematically tested. We deploy autonomous architecture search via an agent across three sequence types (SMILES, protein, and English text as control), running 3,106 experiments on a single GPU. For SMILES, architecture search is counterproductive: tuning learning rates and schedules alone outperforms the full search (p = 0.001). For natural language, architecture changes drive 81% of improvement (p = 0.009). Proteins fall between the two. Surprisingly, although the agent discovers distinct architectures per domain (p = 0.004), every innovation transfers across all three domains with <1% degradation, indicating that the differences reflect search-path dependence rather than fundamental biological requirements. We release a decision framework and open-source toolkit for molecular modeling teams to choose between autonomous architecture search and simple hyperparameter tuning. Comments: 18 pages, 3 figures, 8 tables; code and data at this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.28015 [cs.AI] (or arXiv:2603.28015v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.28015 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Edward Wijaya [ view email ] [v1] Mon, 30 Mar 2026 04:08:37 UTC (142 KB) Full-text links: Access Paper: View a PDF of the paper titled What an Autonomous Agent Discovers About Molecular Transformer Design: Does It Transfer?, by Edward Wijaya View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Information Retrieval arXiv:2603.26688 (cs) [Submitted on 14 Mar 2026] Title: EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading Authors: Md Mahfujur Rahmana , Alistair Barros , Raja Jurdak , Darshika Koggalahewa View a PDF of the paper titled EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading, by Md Mahfujur Rahmana and 2 other authors View PDF HTML (experimental) Abstract: Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely focused on transaction management or isolated mobility prediction tasks, the problem of identifying which charging nodes are more suitable for EV-EV trading in journey contexts remains open. We address this gap by formulating next charging nodes recommendation as a learning-to-rank problem, where each EV decision event is associated with a set of candidate charging locations. We propose a supervised ranking framework applied to a large-scale urban EV mobility dataset comprising millions of journey records and multidimensional EV trading-related features, including EV energy level, trading role, distance to charging locations, charging speed, and temporal station popularity. To account for uncertainty arising from the mobility of both energy providers and consumers, as well as the presence of multiple viable charging nodes at a decision point, we employ probabilistic relevance refinement to generate graded labels for ranking. We evaluate gradient-boosted learning-to-rank models, including LightGBM, XGBoost, and CatBoost, on EV journey records enriched with candidate charging nodes. Experimental results show that LightGBM consistently achieves the strongest ranking performance across standard metrics, including NDCG@k, Recall@k, and MRR, with particularly strong early-ranking quality, reflected in the highest NDCG@1 (0.9795) and MRR (0.9990). These results highlight the effectiveness of uncertainty-aware learning-to-rank for charging node recommendation and support improved coordination and matching in decentralized EV-EV energy trading systems. Subjects: Information Retrieval (cs.IR) ; Machine Learning (cs.LG) Cite as: arXiv:2603.26688 [cs.IR] (or arXiv:2603.26688v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2603.26688 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md Mahfujur Rahman [ view email ] [v1] Sat, 14 Mar 2026 12:33:33 UTC (7,731 KB) Full-text links: Access Paper: View a PDF of the paper titled EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading, by Md Mahfujur Rahmana and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.IR < prev | next > new | recent | 2026-03 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... 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--> Astrophysics > Earth and Planetary Astrophysics arXiv:2603.26953 (astro-ph) [Submitted on 27 Mar 2026] Title: ASTER -- Agentic Science Toolkit for Exoplanet Research Authors: Emilie Panek , Alexander Roman , Gaurav Shukla , Leonardo Pagliaro , Katia Matcheva , Konstantin Matchev View a PDF of the paper titled ASTER -- Agentic Science Toolkit for Exoplanet Research, by Emilie Panek and 4 other authors View PDF HTML (experimental) Abstract: The expansion of exoplanet observations has created a need for flexible, accessible, and user-friendly workflows. Transmission spectroscopy has become a key technique for probing atmospheric composition of transiting exoplanets. The analyses of these data require the combination of archival queries, literature search, the use of radiative transfer models, and Bayesian retrieval frameworks, each demanding specialized expertise. Modern large language models enable the coordinated execution of complex, multi-step tasks by AI agents with tool integration, structured prompts, and iterative reasoning. In this study we present ASTER, an Agentic Science Toolkit for Exoplanet Research. ASTER is an orchestration framework that brings LLM capability to the exoplanetary community by enabling LLM-driven interaction with integrated domain-specific tools, workflow planning and management, and support for common data analysis tasks. Currently ASTER incorporates tools for downloading planetary parameters and observational datasets from the NASA Exoplanet Archive, as well as the generation of transit spectra from the TauREx radiative transfer model, and the completion of Bayesian retrieval of planetary parameters with TauREx. Beyond tool integration, the agent assists users by proposing alternative modeling approaches, reporting potential issues and suggesting solutions, and interpretations. We demonstrate ASTER's workflow through a complete case study of WASP-39b, performing multiple retrievals using observational data available on the archive. The agent efficiently transitions between datasets, generates appropriate forward model spectra and performs retrievals. ASTER provides a unified platform for the characterization of exoplanet atmospheres. Ongoing development and community contributions will continue expanding ASTER's capabilities toward broader applications in exoplanet research. Comments: 17 pages, 10 figures Subjects: Earth and Planetary Astrophysics (astro-ph.EP) ; Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG) Cite as: arXiv:2603.26953 [astro-ph.EP] (or arXiv:2603.26953v1 [astro-ph.EP] for this version) https://doi.org/10.48550/arXiv.2603.26953 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Emilie Panek [ view email ] [v1] Fri, 27 Mar 2026 19:47:53 UTC (3,499 KB) Full-text links: Access Paper: View a PDF of the paper titled ASTER -- Agentic Science Toolkit for Exoplanet Research, by Emilie Panek and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.EP < prev | next > new | recent | 2026-03 Change to browse by: astro-ph astro-ph.IM cs cs.AI cs.ET cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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--> Computer Science > Artificial Intelligence arXiv:2603.27304 (cs) [Submitted on 28 Mar 2026] Title: EpochX: Building the Infrastructure for an Emergent Agent Civilization Authors: Huacan Wang , Chaofa Yuan , Xialie Zhuang , Tu Hu , Shuo Zhang , Jun Han , Shi Wei , Daiqiang Li , Jingping Liu , Kunyi Wang , Zihan Yin , Zhenheng Tang , Andy Wang , Henry Peng Zou , Philip S. Yu , Sen Hu , Qizhen Lan , Ronghao Chen View a PDF of the paper titled EpochX: Building the Infrastructure for an Emergent Agent Civilization, by Huacan Wang and 17 other authors View PDF HTML (experimental) Abstract: General-purpose technologies reshape economies less by improving individual tools than by enabling new ways to organize production and coordination. We believe AI agents are approaching a similar inflection point: as foundation models make broad task execution and tool use increasingly accessible, the binding constraint shifts from raw capability to how work is delegated, verified, and rewarded at scale. We introduce EpochX, a credits-native marketplace infrastructure for human-agent production networks. EpochX treats humans and agents as peer participants who can post tasks or claim them. Claimed tasks can be decomposed into subtasks and executed through an explicit delivery workflow with verification and acceptance. Crucially, EpochX is designed so that each completed transaction can produce reusable ecosystem assets, including skills, workflows, execution traces, and distilled experience. These assets are stored with explicit dependency structure, enabling retrieval, composition, and cumulative improvement over time. EpochX also introduces a native credit mechanism to make participation economically viable under real compute costs. Credits lock task bounties, budget delegation, settle rewards upon acceptance, and compensate creators when verified assets are reused. By formalizing the end-to-end transaction model together with its asset and incentive layers, EpochX reframes agentic AI as an organizational design problem: building infrastructures where verifiable work leaves persistent, reusable artifacts, and where value flows support durable human-agent collaboration. Subjects: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA) Cite as: arXiv:2603.27304 [cs.AI] (or arXiv:2603.27304v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.27304 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shuo Zhang [ view email ] [v1] Sat, 28 Mar 2026 15:20:48 UTC (20,280 KB) Full-text links: Access Paper: View a PDF of the paper titled EpochX: Building the Infrastructure for an Emergent Agent Civilization, by Huacan Wang and 17 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Robotics arXiv:2603.26687 (cs) [Submitted on 13 Mar 2026] Title: Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain Authors: Jiaxing Li , Wen Tian , Xinhang Xu , Junbin Yuan , Sebastian Scherer , Muqing Cao View a PDF of the paper titled Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain, by Jiaxing Li and 5 other authors View PDF HTML (experimental) Abstract: Hybrid aerial--ground robots offer both traversability and endurance, but stair-like discontinuities create a trade-off: wheels alone often stall at edges, while flight is energy-hungry for small height gains. We propose an energy-aware reinforcement learning framework that trains a single continuous policy to coordinate propellers, wheels, and tilt servos without predefined aerial and ground modes. We train policies from proprioception and a local height scan in Isaac Lab with parallel environments, using hardware-calibrated thrust/power models so the reward penalizes true electrical energy. The learned policy discovers thrust-assisted driving that blends aerial thrust and ground traction. In simulation it achieves about 4 times lower energy than propeller-only control. We transfer the policy to a DoubleBee prototype on an 8cm gap-climbing task; it achieves 38% lower average power than a rule-based decoupled controller. These results show that efficient hybrid actuation can emerge from learning and deploy on hardware. Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.26687 [cs.RO] (or arXiv:2603.26687v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.26687 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Muqing Cao Dr [ view email ] [v1] Fri, 13 Mar 2026 14:55:01 UTC (6,249 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain, by Jiaxing Li and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

AI for Disaster Response in Asia: OpenAI Workshop with Gates Foundation

Legal 9 China's not thrilled its AI experts want to leave the country 9 Urges scientists to avoid major conference, and looks unkindly on Meta's Manus acquisition Simon Sharwood Fri 27 Mar 2026 // 01:41 UTC China appears to be unhappy about its brightest AI talent going offshore, either to visit or to sell their wares. One sign of Beijing’s ire appeared this week in a statement from the China Computer Federation (CCF), an organization that promotes development of computer science academics in the Middle Kingdom. The CCF’s beef is with the Neural Information Processing Systems foundation, organizer of the Annual Conference on Neural Information Processing Systems, the fortieth edition of which will take place in Sydney, Australia, later this year. On the NeurIPS conference site , the organization notes that as it operates in the US legal jurisdiction, it must observe laws that prevent it from providing services to entities the US State Department designates as “Specially Designated Nationals and Blocked Persons” (SDNs). NeurIPS therefore believes it can’t accept submissions from any SDN or affiliates. The CCF’s statement accuses NeurIPS of violating the values of openness, inclusiveness, equality, and cooperation that it says are core values of academic exchange, and calls on the org to “immediately correct its erroneous practices, and restore equal rights for submissions and academic exchange to all institutions.” The federation called on all Chinese computer scientists to boycott NeurIPS and refuse to submit papers. The Register often spots presentations by Chinese computer scientists who attend academic CompSci conferences held outside the Middle Kingdom. Attendees often represent the country’s tech giants and present candid insights into their operations and innovations . CCF clearly doesn’t want presentations of that sort at NeurIPS, which would likely mean some significant Chinese boffins don’t make it to Sydney. Other major hosts of academic CompSci conferences, like the Association for Computing Machinery, are also US-based and may therefore also have to ensure that no SDN-linked entities attend their events. We’ve asked NeurIPS for comment and will update this story if we receive a substantive response. Three more charged over alleged Nvidia GPU smuggling scheme to China China’s CERT warns OpenClaw can inflict nasty wounds Beijing warns of more chip supply worries after Nexperia China claims it was cut off from SAP China’s rubber-stamp parliament rubber stamps tech independence plan The spat between CCF and NeurIPS comes in the same week that Beijing has reportedly prevented the founders of agentic AI startup Manus from leaving China. Such a ban would complicate social networking giant Meta’s planned acquisition of the company. Manus established an entity in Singapore to get better access to funding and customers. The move also made it an easier acquisition target. Beijing reacted angrily when Meta bought Manus, on grounds that it doesn’t want domestic AI companies going offshore. And now Chinese computer scientists have been given a reason to stay at home, too. Meanwhile, Chinese tech giants like Alibaba are building their own AI stacks comprising home-grown chips, models, and networks. China’s central planners have made widespread AI adoption a major goal. And perhaps one they intend to pursue alone. ® Share More about AI China Law More like these &times; More about AI China Law Meta Narrower topics AIOps Antitrust China Mobile China telecom China Unicom Cross-border data flow Cyberspace Administration of China DeepSeek Digital Services Act Digital sovereignty Facebook Gemini Google AI GPT-3 GPT-4 Great Firewall Hong Kong Information Technology and the People's Republic of China JD.com Large Language Model Machine Learning MCubed Neural Networks NLP Open Compute Project Privacy Shield Retrieval Augmented Generation Semiconductor Manufacturing International Corporation Shenzhen Star Wars Tensor Processing Unit TOPS Uyghur Muslims WhatsApp Broader topics Andrew McCollum APAC Chris Hughes Dustin Moskovitz Eduardo Saverin Mark Zuckerberg Self-driving Car More about Share 9 COMMENTS More about AI China Law More like these &times; More about AI China Law Meta Narrower topics AIOps Antitrust China Mobile China telecom China Unicom Cross-border data flow Cyberspace Administration of China DeepSeek Digital Services Act Digital sovereignty Facebook Gemini Google AI GPT-3 GPT-4 Great Firewall Hong Kong Information Technology and the People's Republic of China JD.com Large Language Model Machine Learning MCubed Neural Networks NLP Open Compute Project Privacy Shield Retrieval Augmented Generation Semiconductor Manufacturing International Corporation Shenzhen Star Wars Tensor Processing Unit TOPS Uyghur Muslims WhatsApp Broader topics Andrew McCollum APAC Chris Hughes Dustin Moskovitz Eduardo Saverin Mark Zuckerberg Self-driving Car TIP US OFF Send us news

--> Computer Science > Artificial Intelligence arXiv:2603.27857 (cs) [Submitted on 29 Mar 2026] Title: CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping Authors: Alexandros S. Kalafatelis , Nikolaos Nomikos , Vasileios Nikolakakis , Nikolaos Tsoulakos , Panagiotis Trakadas View a PDF of the paper titled CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping, by Alexandros S. Kalafatelis and 4 other authors View PDF HTML (experimental) Abstract: Smart shipping operations increasingly depend on collaborative AI, yet the underlying data are generated across vessels with uneven connectivity, limited backhaul, and clear commercial sensitivity. In such settings, server-coordinated FL remains a weak systems assumption, depending on a reachable aggregation point and repeated wide-area synchronization, both of which are difficult to guarantee in maritime networks. A serverless gossip approach therefore represents a more natural approach, but existing methods still treat communication mainly as an optimization bottleneck, rather than as a resource that must be managed jointly with carbon cost, reliability, and long-term participation balance. In this context, this paper presents CARGO, a carbon-aware gossip orchestration framework for smart-shipping. CARGO separates learning into a control and a data plane. The data plane performs local optimization with compressed gossip exchange, while the control plane decides, at each round, which vessels should participate, which communication edges should be activated, how aggressively updates should be compressed, and when recovery actions should be triggered. We evaluate CARGO under a predictive-maintenance scenario using operational bulk-carrier engine data and a trace-driven maritime communication protocol that captures client dropout, partial participation, packet loss, and multiple connectivity regimes, derived from mobility-aware vessel interactions. Across the tested stress settings, CARGO consistently remains in the high-accuracy regime while reducing carbon footprint and communication overheads, compared to accuracy-competitive decentralized baselines. Overall, the conducted performance evaluation demonstrates that CARGO is a feasible and practical solution for reliable and resource-conscious maritime AI deployment. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.27857 [cs.AI] (or arXiv:2603.27857v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.27857 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Alexandros Kalafatelis [ view email ] [v1] Sun, 29 Mar 2026 20:22:32 UTC (16,402 KB) Full-text links: Access Paper: View a PDF of the paper titled CARGO: Carbon-Aware Gossip Orchestration in Smart Shipping, by Alexandros S. Kalafatelis and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

--> Computer Science > Human-Computer Interaction arXiv:2603.27376 (cs) [Submitted on 28 Mar 2026] Title: Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs Authors: Aayushi Dangol , Robert Wolfe , Nisha Devasia , Mitsuka Kiyohara , Jason Yip , Julie A. Kientz View a PDF of the paper titled Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs, by Aayushi Dangol and 5 other authors View PDF HTML (experimental) Abstract: Two of the most socially consequential issues facing today's children are the rise of artificial intelligence (AI) and the rapid changes to the earth's climate. Both issues are complex and contested, and they are linked through the notable environmental costs of AI use. Using a systems thinking framework, we developed an interactive system called Ecoprompt to help children reason about the environmental impact of AI. EcoPrompt combines a prompt-level environmental footprint calculator with a simulation game that challenges players to reason about the impact of AI use on natural resources that the player manages. We evaluated the system through two participatory design sessions with 16 children ages 6-12. Our findings surfaced children's perspectives on societal and environmental tradeoffs of AI use, as well as their sense of agency and responsibility. Taken together, these findings suggest opportunities for broadening AI literacy to include systems-level reasoning about AI's environmental impact. Subjects: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.27376 [cs.HC] (or arXiv:2603.27376v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2603.27376 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI : https://doi.org/10.1145/3773077.3806144 Focus to learn more DOI(s) linking to related resources Submission history From: Aayushi Dangol [ view email ] [v1] Sat, 28 Mar 2026 18:55:19 UTC (39,932 KB) Full-text links: Access Paper: View a PDF of the paper titled Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs, by Aayushi Dangol and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; 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? ) scite.ai 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 Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )

AI + ML 49 AI bug reports went from junk to legit overnight, says Linux kernel czar 49 Greg Kroah-Hartman can't explain the inflection point, but it's not slowing down or going away Steven J. Vaughan-Nichols Thu 26 Mar 2026 // 13:40 UTC Interview I was at a press luncheon at KubeCon Europe this week when, to my surprise, who should sit down next to me but long-term Linux kernel maintainer Greg Kroah-Hartman. Greg, who lives in the Netherlands these days, was there to briefly comment on AI, Linux, and security. We spoke about how, over the last month, AI-driven activity around Linux security and code review has "really jumped" in a way no one in the open source world saw coming. "Months ago, we were getting what we called 'AI slop,' AI-generated security reports that were obviously wrong or low quality," he said. "It was kind of funny. It didn't really worry us." Of course, there are many Linux kernel maintainers, so for them, AI slop isn't as burdensome as it is for, say, Daniel Stenberg, founder and lead developer of cURL, where AI slop reports caused the cURL team to stop paying bug bounties . Linus Torvalds and friends tell The Reg how Linux solo act became a global jam session READ MORE Things have changed, Kroah-Hartman said. "Something happened a month ago, and the world switched. Now we have real reports." It's not just Linux, he continued. "All open source projects have real reports that are made with AI, but they're good, and they're real." Security teams across major open source projects talk informally and frequently, he noted, and everyone is seeing the same shift. "All open source security teams are hitting this right now." No one is quite sure what's behind it. Asked what changed, Kroah-Hartman was blunt: "We don't know. Nobody seems to know why. Either a lot more tools got a lot better, or people started going, 'Hey, let's start looking at this.' It seems like lots of different groups, different companies." What is clear is the scale. "For the kernel, we can handle it," he said. "We're a much larger team, very distributed, and our increase is real – and it's not slowing down. These are tiny things, they're not major things, but we need help on this for all the open source projects." Smaller projects, he implied, have far less capacity to absorb a sudden flood of plausible AI-generated bug reports and security findings – at least now they're real bugs and not garbage ones. Behind the scenes, security teams are comparing notes. "We get together informally and talk a lot, because we all have the same problems," he said. "There must have been some inflection point somewhere with the tools. Did the local tools get better? Did people figure out something? I honestly don't know." For now, AI is showing up more as a reviewer and assistant than as a full author of Linux kernel code, but that line is starting to blur. Kroah-Hartman has already done his own experiments with AI-generated patches. "I did a really stupid prompt," he recounted. "I said, 'Give me this,' and it spit out 60: 'Here's 60 problems I found, and here's the fixes for them.' About one-third were wrong, but they still pointed out a relatively real problem, and two-thirds of the patches were right." Mind you, those working patches still needed human cleanup, better changelogs, and integration work, but they were far from useless. "The tools are good," he said. "We can't ignore this stuff. It's coming up, and it's getting better." Developers are starting to acknowledge AI's role in actual submissions. "We're seeing some patches being generated," Kroah-Hartman said. "You have a little co-develop tag for that now. We're seeing some things for some new features, but we're seeing AI mostly being used in the review." Asked whether he could imagine a near-future where most of the work on simple changes comes from AI, he said that for "simple little error conditions, properly detecting error conditions," AI could already generate dozens of usable patches today. The sudden increase in AI-generated reports and AI-assisted work has also spurred a parallel push to build AI into the kernel's own review infrastructure. A key piece of that is Sashiko, a tool originally developed at Google and now donated to the Linux Foundation . Nanny state discovers Linux, demands it check kids' IDs before booting Open source devs consider making hogs pay for every download Workaholic open source developers need to take breaks OK, so Anthropic's AI built a C compiler. That don't impress me much "We need to be able to have an easy way to review some of these patches that come in ways that cut down on our load." The tool is "out there, running on almost all kernel patches," he said. "You can see it publicly. We're integrating it into our review tools. It's available for anybody to use." That work builds on earlier efforts inside specific subsystems. "The networking and the BPF people have been doing LLM-generated reviews for a while," said Kroah-Hartman. "The Direct Rendering Manager (DRM) people and now Google's tool are pulling all those into one common interface," he explained. "Different subsystems are adding better skills or prompts – for storage, here are the things you need to look for; for graphics, here are the things you need to look for. People are contributing in a public place for that, which is how it should be. This is very good." Kroah-Hartman credited longtime kernel developer Chris Mason, now at Meta, with pioneering AI-based review workflows. Mason has been running AI review for eBPF and networking for some time. The systemd project is also using the same class of tools for its all-C codebase. AI reviewers, he stressed, are additive rather than authoritative. "On the review side, it's generating some good reviews. It doesn't get you everything. Some things are still wrong. But it does point out a lot of the obvious things," he said. One of the biggest immediate wins is turnaround time. When an AI reviewer flags obvious problems, submitters get feedback long before a human maintainer would realistically read the patch. "If I see it respond to something, it gives feedback to the submitter faster than the maintainer had a chance to, which is nice," Kroah-Hartman said. "We have a number of bots that run on patches as it is. If I see those fail, I just know I don't even need to look at that as a maintainer. And it gives the developer, 'Oh, I can go do another version tomorrow,' which helps increase the feedback a little better." Still, as AI-generated reports and patches grow, so does the review burden. "It's more reviews; it's more stuff we have to review for the kernel," he said. That's why efforts with the OpenSSF and its Alpha-Omega program matter. "We're working to try and create tools to help make it easier for maintainers to handle this incoming feed and deal with it." A recurring theme for Kroah-Hartman is equity of access. Until recently, only well-resourced subsystems could afford to run heavy AI tooling at scale. Turning Google's review system into a Linux Foundation project is meant to change that. "That's this one tool that we have for the review," he said. "It's one tool as an example of how now, as an LF project, we're giving access to everybody. Before, it was just the subsystems that had the resources to run it on the back end. Right now, we're giving it to everyone." Work is already underway to make it usable beyond the kernel's own infrastructure. That matters because, as Kroah-Hartman keeps emphasizing, the AI wave is not just a kernel problem. "All open source projects have real reports that are made with AI," he said. "Our increase is real, and it's not slowing down. These aren't major things, but we need help on this for all the open source projects." For Linux, the relationship with AI is already evolving past theory and into practice. It's a mixed blessing. AI is simultaneously a new source of real vulnerabilities that strains human reviewers who must deal with them, while also helping to manage that strain. The trick for Kroah-Hartman and his peers will be to keep AI as a force multiplier, without drowning the open source maintainers. ® Share More about AI Developer Linux More like these &times; More about AI Developer Linux Narrower topics AIOps API Asahi Linux CentOS Debian DeepSeek Fedora Gemini Git GNOME Google AI GPT-3 GPT-4 Large Language Model Linux Foundation Machine Learning MCubed Neural Networks NLP One Way Forward Programming Language Retrieval Augmented Generation Software bug Star Wars Tensor Processing Unit TOPS Windows Subsystem for Linux Broader topics FOSS Linus Torvalds Operating System Self-driving Car More about Share 49 COMMENTS More about AI Developer Linux More like these &times; More about AI Developer Linux Narrower topics AIOps API Asahi Linux CentOS Debian DeepSeek Fedora Gemini Git GNOME Google AI GPT-3 GPT-4 Large Language Model Linux Foundation Machine Learning MCubed Neural Networks NLP One Way Forward Programming Language Retrieval Augmented Generation Software bug Star Wars Tensor Processing Unit TOPS Windows Subsystem for Linux Broader topics FOSS Linus Torvalds Operating System Self-driving Car TIP US OFF Send us news

David Sacks, the venture capitalist and tech billionaire who'd become Silicon Valley's primary advocate inside the White House and a key architect of its aggressive AI policy initiatives, revealed on Thursday that he was no longer a special government employee - and therefore no longer President Donald Trump's Special Advisor on AI and Crypto. Sacks' official status as an SGE allowed him to work simultaneously in the private sector and for the government, but for no more than 130 days, raising questions about why he was still in the job more than a year after his appointment. But in an interview with Bloomberg Television discussing the Whit … Read the full story at The Verge.

Осенью прошлого года у меня был визит по технологическим компаниям Китая: Baidu, Xiaomi, SenseTime и другие, была возможность познакомиться с ними изнутри, хотя и кратко. Одна из вещей, которые меня удивили – сильно иное отношение к ИИ в Китае, чем в России . У нас в инфополе две темы: готовьтесь к безработице, ИИ всех вас заменит, и ИИ – глуп, ничего не может. При этом один человек может запросто высказывать обе, не замечая противоречия. И, что важно, первая тема звучит не только для нагнетания напряженности, не, именно так ставят задачу и заказчики приложений или агентов, и их разработчики. Впрочем, справедливости ради, надо отметить, что отношение меняется, и в докладах все больше прагматического отношения к ИИ-агентом. Но это не отменяет их кликбейтных заголовков, в которых звучит вопрос именно о замене людей, а вот для менеджеров тема продолжает звучать всерьез, они именно под это санкционирует запуск проектов. В Китае же отношение принципиально иное, они нацелены на то, как с помощью ИИ сделать эффективнее работу команд. При этом то, что ИИ-агенты еще недостаточно умны, тоже воспринимают естественно: ИИ учится, и мы должны ему помогать учиться, обсуждая с ним наши задачи, чтобы ИИ быстрее выучился и смог лучше помогать людям. SenseTime вообще формулирует миссию так: « Пусть ИИ возглавит наш прогресс ». И такое отношение реально приводит к тому, что ИИ и его применение развивается быстрее : когда ты ставишь задачу помощи, а не полной замены, ты можешь делать задачи в зоне доступности, срывая низко висящие фрукты, а потом – наращивать применение. Читать далее

Green Technology Market to Reach USD 102.26 Bn by 2031 Driven by Renewable Energy Adoption and Smart Infrastructure, Reports Mordor Intelligence News provided by Mordor Intelligence Private Limited Mar 31, 2026, 03:49 ET Share this article Share to X Share this article Share to X HYDERABAD, India , March 31, 2026 /PRNewswire/ -- According to the latest research report published by Mordor Intelligence, the green technology market is experiencing rapid expansion as governments and businesses prioritize sustainability and low-carbon innovation. The green technology market size is estimated at USD 36.24 billion in 2026 , growing from USD 29.45 billion in 2025 , and is projected to reach USD 102.26 billion by 2031, registering a CAGR of 23.05% during the forecast period (2026–2031) . This strong green technology market growth is driven by rising investments in renewable energy, energy-efficient infrastructure, smart grids, and low-carbon technologies. Governments across the world are introducing policies aimed at reducing emissions, while private companies are integrating sustainable solutions into their operations. These developments are shaping the green technology industry, positioning it as a key pillar in the global transition toward environmentally responsible economic growth. Green Technology Market Growth Drivers and Industry Adoption Policy Pressure Accelerating Sustainable Technology Adoption Stronger climate regulations and carbon pricing mechanisms are pushing industries to adopt cleaner and more transparent production practices. European sustainability rules are expanding environmental requirements across a wide range of products, making eco-friendly design and monitoring systems increasingly necessary. At the same time, exporters in several Asia-Pacific countries are upgrading manufacturing processes with digital tracking tools to meet stricter emissions reporting requirements. These changes are encouraging global supply chains to adopt common data frameworks and sustainability technologies, expanding opportunities for advanced green solutions. "Green technology adoption continues to reflect measured, policy-aligned investment patterns across key industries, with growth shaped by regulatory clarity and capital discipline. This assessment is grounded in consistently validated data, structured triangulation, and a transparent research framework designed to support reliable executive decision-making," says Ashish Gautam, Senior Research Manager, Mordor Intelligence. AI Integration Transforming Corporate Carbon Management Companies are increasingly embedding AI-powered carbon tracking tools within enterprise management systems to monitor emissions across their operations. By connecting sustainability data directly with finance and operational dashboards, businesses can make quicker decisions about energy use, production schedules, and supplier choices. These digital tools help organizations evaluate the environmental impact of operational changes in real time, turning sustainability from a reporting task into a core part of strategic planning. As more firms adopt these integrated platforms, demand for advanced environmental technology solutions continues to rise. Green Technology Market Segmentation Analysis By Component Solutions Services By Technology Internet of Things (IoT) Artificial Intelligence and Analytics Digital Twin Cloud Computing Blockchain Other Emerging Technologies By Application Green Building Carbon Footprint Management Air and Water Pollution Monitoring Weather Monitoring and Forecasting Crop Monitoring Others By End-user Industry Energy and Utilities Manufacturing Transportation and Logistics Agriculture Construction and Real Estate IT and Telecom Government and Public Sector Other Industries By Geography North America United States Canada Mexico South America Brazil Argentina Rest of South America Europe Germany United Kingdom France Italy Spain Rest of Europe Asia-Pacific China India Japan South Korea Australia and New Zealand Rest of Asia-Pacific Middle East and Africa Middle East Saudi Arabia United Arab Emirates Turkey Rest of Middle East Africa Nigeria South Africa Egypt Rest of Africa For a full breakdown of market size, segmentation data, and competitive intelligence, access all details of the Mordor Intelligence report: https://www.mordorintelligence.com/industry-reports/green-technology-market?utm_source=prnewswire Green Technology Market Growth Across Key Regions North America remains a major hub for sustainable technology adoption, supported by strong government incentives and active private investment. Companies in the United States are integrating carbon management tools into enterprise software systems, while Canada is applying similar solutions to track emissions across manufacturing supply chains. Mexico is also advancing environmental monitoring practices within its export-oriented industrial zones, reflecting broader regional efforts to align with sustainability regulations. The Asia-Pacific region is witnessing particularly rapid momentum as industrial growth combines with stricter environmental policies. Countries such as China and India are encouraging companies to adopt digital monitoring technologies to support sustainability goals, while Japan and South Korea are investing in smart infrastructure and connected urban systems. These initiatives are accelerating the deployment of advanced environmental technologies across industries. Key Players Shaping the Green Technology Market The green technology market share is moderately competitive, with global technology companies and sustainability solution providers investing heavily in research and innovation. Key companies operating in the green technology industry include: General Electric IBM Corporation Microsoft Corporation Siemens AG Schneider Electric SE Oracle Corporation ABB Ltd. Tesla Inc. Vestas Wind Systems Enel S.p.A. Explore more insights on green technology competitive landscape: https://www.mordorintelligence.com/industry-reports/green-technology-market/companies?utm_source=prnewswire Check out related reports published by Mordor Intelligence: Smartwatch Market Forecast - The smartwatch market is projected to grow from 230.73 million units in 2025 and 279.39 million units in 2026 to 726.73 million units by 2031, registering a 21.07% CAGR between 2026 and 2031. Market expansion is driven by rising demand for health monitoring devices, increasing adoption of wearable fitness technology, and growing integration with smartphones and digital health platforms. Apple Inc., Samsung Electronics Co. Ltd, Garmin Ltd, Fitbit Inc., and Fossil Group Inc. are the major companies operating in this market. Read more about companies active in the smartwatch market: https://www.mordorintelligence.com/industry-reports/smartwatch-market/companies?utm_source=prnewswire Augmented Reality Market Outlook - The augmented reality market is expected to expand from USD 99.81 billion in 2025 and USD 125.11 billion in 2026 to USD 387.23 billion by 2031, growing at a 25.35% CAGR during 2026–2031. Increasing use of AR in gaming, retail visualization, healthcare training, and enterprise applications is driving adoption, along with advancements in AR hardware and immersive digital experiences. Microsoft Corporation, Meta Platforms Inc., Apple Inc., Qualcomm Technologies Inc., and Google LLC (Alphabet) are the major companies operating in this market. Read more about companies active in the augmented reality market: https://www.mordorintelligence.com/industry-reports/augmented-reality-market/companies?utm_source=prnewswire Natural Language Processing Market Trends - The natural language processing market is projected to grow from USD 39.37 billion in 2025 and USD 47.37 billion in 2026 to USD 117.57 billion by 2031, registering a 19.94% CAGR between 2026 and 2031. Growth is fueled by the rising use of AI-powered chatbots, voice assistants, sentiment analysis tools, and increasing enterprise adoption of conversational AI solutions. Microsoft Corporation, SAS Institute Inc., IBM Corporation, Google LLC (Alphabet), and NVIDIA Corp. are the major companies operating in this market. Read more about companies active in the natural language processing market: https://www.mordorintelligence.com/industry-reports/natural-language-processing-market/companies?utm_source=prnewswire About Mordor Intelligence Mordor Intelligence is a trusted partner for businesses seeking comprehensive and actionable market intelligence. Our global reach, expert team, and tailored solutions empower organizations and individuals to make informed decisions, navigate complex markets, and achieve their strategic goals. With a team of over 550 domain experts and on-ground specialists spanning 150+ countries, Mordor Intelligence possesses a unique understanding of the global business landscape. This expertise translates into comprehensive syndicated and custom research reports covering a wide spectrum of industries, including aerospace & defense, agriculture, animal nutrition and wellness, automation, automotive, chemicals & materials, consumer goods & services, electronics, energy & power, financial services, food & beverages, healthcare, hospitality & tourism, information & communications technology, investment opportunities, and logistics. For any inquiries, please contact: [email protected] https://www.mordorintelligence.com/contact-us Logo: https://mma.prnewswire.com/media/2746908/Mordor_Intelligence_Logo.jpg SOURCE Mordor Intelligence Private Limited 21 % more press release views with  Request a Demo &times; Modal title