arXiv:2601.12231v1 Announce Type: new Abstract: Insider threat detection is a key challenge in enterprise security, relying on user activity logs that capture rich and complex behavioral patterns. These logs are often multi-channel, non-stationary, and anomalies are rare, making anomaly detection challenging. To address these issues, we propose a novel framework that integrates wavelet-aware modulation, multi-resolution wavelet decomposition, and resolution-adaptive attention for robust anomaly detection. Our approach first applies a deviation-aware modulation scheme to suppress routine behaviors while amplifying […]

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This summer, the NFC Summit returns June 4-6, 2026, with its fifth edition at the iconic Unicorn Factory Lisboa, establishing itself as the world’s largest Web3 pop-culture festival.  This year’s event expands beyond traditional blockchain programming to integrate Japanese pop culture through the inaugural KAWAII summit, AI creative innovations via ACAI days, the agentic era with the first-ever Agents vs Agents Hackathon, and Stablecoin Day exploring the future of digital money, bringing together leaders in fintech, DeFi, and […]

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We study a class of wrapping operators acting on the space of formal languages over a fixed finite alphabet. The underlying space is equipped with a length-based ultrametric, in which two languages are close whenever they coincide on all sufficiently short words. We prove that every wrapping operator generated by a finite family of guards with positive total guard length is a contraction. As a consequence, Banach’s contraction principle yields existence and uniqueness of a fixed point for […]

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arXiv:2601.08005v1 Announce Type: new Abstract: Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications can occur internally when companies deploy highly capable systems within their own organizations, such as for automating R&D, accelerating critical business processes, and handling sensitive proprietary data. This paper examines how frontier AI regulations in the United States and European Union in 2025 handle internal deployment. We identify three […]

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The era of cloud-tethered computing is officially coming to an end. For the last three years, developers have been held hostage by API rate limits, exorbitant subscription costs, and the looming threat of closed-source data harvesting. Big Tech told us that local AI was a pipe dream. They claimed that running frontier models required server farms the size of small cities. They wanted us dependent on their infrastructure, paying rent for every token generated. Then came the lobster. […]

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arXiv:2602.00912v1 Announce Type: new Abstract: Recent initiatives advocating responsible, transparent research assessment have intensified the call to use open research information rather than proprietary databases. This study evaluates the coverage and citation representation of publications recorded in the Current Research Information Systems (CRIS), all instances of the IRIS software platform, of six Italian universities within OpenCitations, a community-owned open infrastructure. Using persistent identifiers (DOIs, PMIDs, and ISBNs) specified in the IRIS installations involved, we matched the publications recorded […]

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By Prash Pandit, VP Validation Business at P2P.org A technical look at how decentralized validator architecture gives institutions better reliability, auditability, and system-level resilience. If you’ve ever actually run validators — not reviewed a diagram, not talked strategy in a meeting, but operated them — you figure out quickly that staking isn’t passive. It behaves like a live distributed system. Clients drift. Gossip traffic gets noisy. Relays hiccup at precisely the wrong moment. And when you scale that […]

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Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task-Agnostic Continual Learning, referred to as SETA, a framework that resolves the plasticity-stability conflict by decomposing the model into modular subspaces. Unlike standard updates, where tasks compete for the same […]

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