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OpenAI kicked off an AI revolution with DALL-E and ChatGPT, making the organization the epicenter of the artificial intelligence boom. Led by CEO Sam Altman, OpenAI became a story unto itself when Altman was briefly fired and then brought back...

After the smartphone, headphones are the gadget we reach for most often in our daily lives. Here, The Verge tracks the best headphones for every budget and purpose, whether they be wireless, noise cancelling, in-ear, or over-ear. With all the...

For more than 25 years, Sony’s PlayStation has been synonymous with gaming. It’s given players experiences like God of War, The Last of Us, and Final Fantasy VII alongside technological innovations from CD-ROMs all the way up to 4K, VR,...

Cybersecurity is the rickety scaffolding supporting everything you do online. For every new feature or app, there are a thousand different ways it can break – and a hundred of those can be exploited by criminals for data breaches, identity...

Cinema isn’t just about the latest Disney/Pixar project or Star Wars spin-off. Memorable storytelling is happening all over the film industry, from Hollywood’s box-office-busting superhero smashes to small, innovative indie experiments. The Verge’s film section is here to help you...

Artificial intelligence is more a part of our lives than ever before. While some might call it hype and compare it to NFTs or 3D TVs, AI is causing a sea change in nearly every part of the technology industry....

Verge Science is here to bring you the most up-to-date space news and analysis, whether it’s about the latest findings from NASA or comprehensive coverage of the next SpaceX rocket launch to the International Space Station. We’ll take you inside...

India’s $1.1B fund-of-funds will invest through private VCs to support deep-tech and manufacturing startups.

arXiv:2602.13213v1 Announce Type: new Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing. While AI offers substantial efficiency improvements, existing solutions lack comprehensive reasoning capabilities and internal...

arXiv:2602.13224v1 Announce Type: new Abstract: The term "hallucination" in large language models conflates distinct phenomena with different geometric signatures in embedding space. We propose a taxonomy identifying three types: unfaithfulness (failure to engage with provided context), confabulation (invention of semantically...

arXiv:2602.13226v1 Announce Type: new Abstract: Detecting text generated by large language models (LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rely on text-level features, leading to imprecise detection ability. In this...

arXiv:2602.13237v1 Announce Type: new Abstract: Automated reasoning is critical in domains such as law and governance, where verifying claims against facts in documents requires both accuracy and interpretability. Recent work adopts structured reasoning pipelines that translate natural language into first-order...

arXiv:2602.13248v1 Announce Type: new Abstract: Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs), yet existing approaches lack systematic frameworks for analysing how humans linguistically construct driving rationales across diverse scenarios. This paper...

arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively...

arXiv:2602.13275v1 Announce Type: new Abstract: Alignment research focuses on making individual AI systems reliable. Human institutions achieve reliable collective behaviour differently: they mitigate the risk posed by misaligned individuals through organisational structure. Multi-agent AI systems should follow this institutional model...

arXiv:2602.13280v1 Announce Type: new Abstract: Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high...

arXiv:2602.13516v1 Announce Type: new Abstract: LLM-powered agents are beginning to automate user's tasks across the open web, often with access to user resources such as emails and calendars. Unlike standard LLMs answering questions in a controlled ChatBot setting, web agents...

arXiv:2602.13559v1 Announce Type: new Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-Tuning (SFT) or Offline Reinforcement Learning (RL) using static datasets....

arXiv:2602.13616v1 Announce Type: new Abstract: We propose DiffusionRollout, a novel selective rollout planning strategy for autoregressive diffusion models, aimed at mitigating error accumulation in long-horizon predictions of physical systems governed by partial differential equations (PDEs). Building on the recently validated...

arXiv:2602.13665v1 Announce Type: new Abstract: While agentic AI systems rely on LLMs to translate user intent into structured function calls, this process is fraught with computational redundancy, leading to high inference latency that hinders real-time applications. This paper identifies and...

arXiv:2602.13466v1 Announce Type: new Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is widely employed but not well characterized. We find that language model embeddings typically...

arXiv:2602.13567v1 Announce Type: new Abstract: Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate layer's thought process as a black box. While feature-based distillation attempts to bridge this gap,...

arXiv:2602.13575v1 Announce Type: new Abstract: Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability. We introduce Elo-Evolve, a...

arXiv:2602.13701v1 Announce Type: new Abstract: Metaphors are a distinctive feature of literary language, yet they remain less studied experimentally than everyday metaphors. Moreover, previous psycholinguistic and computational approaches overlooked the temporal dimension, although many literary metaphors were coined centuries apart...

arXiv:2602.13840v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in personalized tasks involving sensitive, context-dependent information, where privacy violations may arise in agents' action due to the implicitness of contextual privacy. Existing approaches rely on external,...

arXiv:2602.14060v1 Announce Type: new Abstract: We introduce LM-Lexicon, an innovative definition modeling approach that incorporates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts architecture. By decomposing the definition modeling task into specialized semantic domains, where small...

arXiv:2602.15212v1 Announce Type: new Abstract: In this paper, we introduce a secure wireless agentic AI network comprising one supervisor AI agent and multiple other AI agents to provision quality of service (QoS) for users' reasoning tasks while ensuring confidentiality of...

arXiv:2602.15248v1 Announce Type: new Abstract: Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is...

arXiv:2602.17902v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to automate scientific workflows, yet their integration with heterogeneous computational tools remains ad hoc and fragile. Current agentic approaches often rely on unstructured text to manage context and...

arXiv:2602.18201v1 Announce Type: new Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology-preserving representation method based on...