arXiv:2603.02413v1 Announce Type: new Abstract: Safe highway autonomy for heavy trucks remains an open and unsolved challenge: due to long braking distances, scene understanding of hundreds of meters is required for anticipatory planning and to allow safe braking margins. However, existing driving datasets primarily cover urban scenes, with perception effectively limited to short ranges of only up to 100 meters. To address this gap, we introduce TruckDrive, a highway-scale multimodal driving dataset, captured with a sensor suite purpose-built […]

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arXiv:2509.26429v2 Announce Type: replace Abstract: Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential outcomes over long horizons is notoriously difficult. Existing methods that break the curse of the horizon typically lack strong theoretical guarantees such as orthogonality and quasi-oracle efficiency. In this paper, we revisit the problem of predicting individualized potential outcomes in sequential decision-making (i.e., estimating […]

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arXiv:2601.19931v1 Announce Type: new Abstract: We present a hybrid neuro-symbolic system for the SemEval-2026 Task 4 on Narrative Story Similarity. Our approach combines neural self-consistency voting with a novel Multi-Scale Narrative Analysis Ensemble that operates as a symbolic tiebreaker. The neural network component uses a large language model with multiple parallel votes, applying a supermajority threshold for confident decisions and escalating uncertain cases to additional voting rounds. When votes result in a perfect tie, a symbolic ensemble combining […]

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arXiv:2601.22185v1 Announce Type: new Abstract: The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts […]

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Privacy in blockchains is often known as more than just a cryptographic problem. If transaction values are hidden, sender and receiver identities are obfuscated, and proofs reveal nothing beyond validity, then privacy is secured here. In practice, this assumption breaks down, once transactions leave the wallet and enter the peer-to-peer network. Data in transit, even though protected by cryptographic security, can reveal information about the origin. Even the strongest on-chain privacy mechanisms do not prevent trackers from watching […]

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Simulation theory has been thrown around the internet for years, stemming from the early days of computing. It has long been theorized that everything around us is the result of a computer generated simulation, a fake reality that is being fed through us and presented as real. The Matrix is the first popular reference of simulation theory, and made the idea not only more understood by the general public but more accepted as well. Technology Acceleration Moore’s Law […]

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In the high-stakes bazaar of the dark web, there is a clear hierarchy of value. A stolen credit card is a mere commodity, often trading for the price of a cup of coffee. A Social Security number is a versatile tool. But a comprehensive medical record? That is a masterpiece. It contains a permanent, unchangeable blueprint of a human life — history, genetic markers, and biometric vulnerabilities. Unlike a compromised bank account, which can be frozen and reset, […]

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Your chatbot confidently told a customer your return policy is 90 days. It’s 30.It later described features your product does not even have. That is the gap between a great demo and a real production system. Language models sound sure even when they are wrong, and in production that gets expensive fast. This is why serious AI teams use RAG. Not because it is trendy, but because it keeps models grounded in real information. What most people miss is […]

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arXiv:2601.17090v1 Announce Type: new Abstract: Partial differential equations (PDEs) govern complex systems, yet neural operators often struggle to efficiently capture the long-range, nonlocal interactions inherent in their solution maps. We introduce Spectral Filtering Operator (SFO), a neural operator that parameterizes integral kernels using the Universal Spectral Basis (USB), a fixed, global orthonormal basis derived from the eigenmodes of the Hilbert matrix in spectral filtering theory. Motivated by our theoretical finding that the discrete Green’s functions of shift-invariant PDE […]

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