arXiv:2504.05695v5 Announce Type: replace-cross Abstract: We prove that overparametrized neural networks are able to generalize with a test error that is independent of the level of overparametrization, and independent of the Vapnik-Chervonenkis (VC) dimension. We prove explicit bounds that only depend on the metric geometry of the test and training sets, on the regularity properties of the activation function, and on the operator norms of the weights and norms of biases. For overparametrized deep ReLU networks with a […]

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An AI-generated report, delivered directly to the email inboxes of journalists, was an essential tool in the Times’ coverage. It was also one of the first signals that conservative media was turning against the administration […] Built in-house and known internally as the “Manosphere Report,” the tool uses large language models (LLMs) to transcribe and summarize new episodes of dozens of podcasts. “The Manosphere Report gave us a really fast and clear signal that this was not going […]

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arXiv:2601.04202v1 Announce Type: new Abstract: Language Models (LLMs) are increasingly explored in the telecom industry to support engineering tasks, accelerate troubleshooting, and assist in interpreting complex technical documents. However, recent studies show that LLMs perform poorly on telecom standards, particularly 3GPP specifications. We argue that a key reason is that these standards densely include tables to present essential information, yet the LLM knowledge and interpretation ability of such tables remains largely unexamined. To address this gap, we introduce […]

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arXiv:2602.09222v1 Announce Type: new Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users’ behalf. While these agents offer powerful capabilities, their design exposes them to indirect prompt injection attacks embedded in untrusted web content, enabling adversaries to hijack agent behavior and violate user intent. Despite growing awareness of this threat, existing evaluations rely on fixed attack templates, manually selected injection […]

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What is your opinion on long appendices in conference papers? I am observing that appendix lengths in conference papers (ICML, NeurIPS, etc.) are getting longer and longer, and in some fields they are now basically the standard and a central part of the paper. From my point of view, this is becoming a bit problematic. I have many times been asked to add more experiments which, in order to be included, require several extra pages beyond the main […]

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Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means the model was trained from the ground up to process and reason across text and visual inputs simultaneously — not a vision module […]

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SpyderBot is a cutting-edge LLM analytics platform that reveals exactly how AI models like ChatGPT, Grok, and Gemini see your brand and your competitors. Using a network of over 20,000 distributed LLM-bots, SpyderBot tracks real-time Share of Voice, competitor visibility scores, sentiment analysis, and LLM crawler activity on your website — insights that traditional analytics tools completely miss. For brands and agencies, it answers critical questions: → Where do we rank in AI answers? → Which competitors are […]

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The growing demand for cost-efficient digital transactions has driven the need for scalable and low-cost payment solutions. Traditional blockchain-based transactions suffer from high fees and slow processing times, making decentralized off-chain payment networks a promising alternative. In this paper, we propose SpeedyMurmurs, an AI-enhanced decentralized routing algorithm that significantly reduces payment processing costs and transaction delays. Our approach optimizes payment routing efficiency through embedding-based path discovery, reducing routing overhead by up to two orders of magnitude and cutting […]

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arXiv:2509.11907v2 Announce Type: replace-cross Abstract: In this work, we consider the problem of identifying an unknown linear dynamical system given a finite hypothesis class. In particular, we analyze the effect of the excitation input on the sample complexity of identifying the true system with high probability. To this end, we present sample complexity lower bounds that capture the choice of the selected excitation input. The sample complexity lower bound gives rise to a system theoretic condition to determine […]

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