arXiv:2603.18483v1 Announce Type: new Abstract: In the present paper we study the performance of linear denoisers for noisy data of the form $mathbf{x} + mathbf{z}$, where $mathbf{x} in mathbb{R}^d$ is the desired data with zero mean and unknown covariance $mathbf{Sigma}$, and $mathbf{z} sim mathcal{N}(0, mathbf{Sigma}_{mathbf{z}})$ is additive noise. Since the covariance $mathbf{Sigma}$ is not known, the standard Wiener filter cannot be employed for denoising. Instead we assume we are given samples $mathbf{x}_1,dots,mathbf{x}_n in mathbb{R}^d$ from the true distribution. […]

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Every time a new wave of automation appears, the same fear resurfaces: “This time is different. This time, jobs are really gone.” AI feels like that moment for software development. But what’s actually happening is far less dramatic — and far more interesting. ==Programming is not disappearing. Its economic role is changing.== For a long time, software teams scaled linearly. More features meant more people, more ideas meant more engineers. That made sense when writing code was the bottleneck. Today, it […]

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arXiv:2602.06214v1 Announce Type: new Abstract: End-to-End Autonomous Driving (E2E-AD) systems are typically grouped by the nature of their outputs: (i) waypoint-based models that predict a future trajectory, and (ii) action-based models that directly output throttle, steer and brake. Most recent benchmark protocols and training pipelines are waypoint-based, which makes action-based policies harder to train and compare, slowing their progress. To bridge this waypoint-action gap, we propose a novel, differentiable vehicle-model framework that rolls out predicted action sequences to […]

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There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the field of machine unlearning, which aims to eliminate the influence of specific data subsets without the cost of full retraining. In this work, we propose a statistical framework for machine unlearning with generic loss functions and establish theoretical guarantees. For squared loss, especially, we develop Unlearning Least Squares […]

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arXiv:2602.07186v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems improve LLM reasoning through iterative deliberation, but remain vulnerable to debate collapse, a failure type where final agent decisions are compromised on erroneous reasoning. Existing methods lack principled mechanisms to detect or prevent such failures. To address this gap, we first propose a hierarchical metric that quantifies behavioral uncertainty at three levels: intra-agent (individual reasoning uncertainty), inter-agent (interactive uncertainty), and system-level (output uncertainty). Empirical analysis across several benchmarks reveals […]

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New York, USA, May 15th, 2026/Chainwire/–E Estate Group Inc. announced that it will host E-Estate 1 Year Live: Washington DC Summit on June 13, 2026, bringing together company leadership, agents, buyers, strategic partners, and guests interested in the future of blockchain-based real estate ownership. The summit will take place at The Watergate Hotel in Washington, D.C. and will mark one year since the launch of the E-Estate platform. The event is designed as a milestone gathering for the […]

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arXiv:2601.05910v1 Announce Type: new Abstract: Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized by multi-source, multi-fidelity data, addressing challenges of data sparsity and varying task correlations. The proposed framework leverages inter-task relationships across outputs and fidelity levels to improve predictive performance and reduce computational […]

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Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge. A fundamental difficulty in online RL is the lack of direct samples from the target distribution; instead, the target is an unnormalized Boltzmann distribution defined by the Q-function. To address this, two seemingly distinct families of methods have been proposed for diffusion policies: a noise-expectation family, which utilizes a weighted average of noise […]

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