A new paper by Avihu Mordechai Levy (StarkWare) describes Quantum-Safe Bitcoin (QSB), a way to spend legacy Bitcoin outputs that would stay secure even if an attacker had a cryptographically relevant quantum computer. It works inside today’s Bitcoin Script with no soft fork. Under the paper’s Shor-only threat model, its recommended configuration (Config A) achieves roughly 2^118 second-preimage resistance while fitting inside Bitcoin’s existing legacy script limits of 201 opcodes and under 10,000 bytes (§4.5, §2.2). The spender […]

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arXiv:2603.22472v1 Announce Type: new Abstract: Autonomous aerial and aquatic robots that attain mobility by perturbing their medium, such as multicopters and torpedoes, produce wake effects that act as disturbances for adjacent robots. Wake effects are hard to model and predict due to the chaotic spatio-temporal dynamics of the fluid, entangled with the physical geometry of the robots and their complex motion patterns. Data-driven approaches using neural networks typically learn a memory-less function that maps the current states of […]

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Over the past few years working with data teams inside large enterprises, I’ve met a lot of data leaders who tell me they’ve tried and failed to “do DataOps.” The pattern is usually the same. They write standards, add a few tests, and stand up observability tools. Processes get documented. Release checklists are made. Teams try—earnestly—to follow them. And then the backlog piles up, exceptions multiply, and the team has to hold it all together with memory and […]

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arXiv:2601.05461v1 Announce Type: new Abstract: Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a benchmark for reasoning-based conversational information retrieval comprising 707 conversations (2,971 turns) across eleven domains. To ensure quality, our Decomposition-and-Verification framework transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation, where atomic facts are verified against sources and explicit retrieval reasoning is generated for each turn. Comprehensive evaluation reveals that combining […]

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arXiv:2602.15883v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large spatiotemporal domains is hindered by computational bottlenecks and optimization instabilities. In this work, we propose a robust distributed PINNs framework designed for efficient flow reconstruction via spatiotemporal domain decomposition. A critical challenge in such distributed solvers is pressure […]

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arXiv:2403.03007v3 Announce Type: replace-cross Abstract: The generalized linear mixed model (GLMM) is widely used for analyzing correlated data, particularly in large-scale biomedical and social science applications. Scalable Bayesian inference for GLMMs is challenging because the marginal likelihood is intractable and conventional Markov chain Monte Carlo (MCMC) methods become computationally prohibitive as the number of subjects grows. We develop a stochastic gradient MCMC (SGMCMC) algorithm tailored to GLMMs that enables accurate posterior inference in the large-sample regime. Our approach […]

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Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this work, we conduct a systematic study of calibration in two widely used fine-tuning paradigms: supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). We show that while RLVR improves task performance, it produces extremely overconfident […]

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