arXiv:2603.28889v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized natural language interaction with data. The “holy grail” of data analytics is to build autonomous Data Agents that can self-drive complex data analysis workflows. However, current implementations are still limited to linear “ChatBI” systems. These systems struggle with joint analysis across heterogeneous data sources (e.g., databases, documents, and data files) and often encounter “context explosion” in complex and iterative data analysis workflows. To address these challenges, we […]

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arXiv:2604.06263v1 Announce Type: new Abstract: Generative advertising in large language model (LLM) responses requires optimizing sponsorship configurations under two strict constraints: the strategic behavior of advertisers and the high cost of stochastic generations. To address this, we propose the Incentive-Aware Multi-Fidelity Mechanism (IAMFM), a unified framework coupling Vickrey-Clarke-Groves (VCG) incentives with Multi-Fidelity Optimization to maximize expected social welfare. We compare two algorithmic instantiations (elimination-based and model-based), revealing their budget-dependent performance trade-offs. Crucially, to make VCG computationally feasible, we […]

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arXiv:2603.15759v1 Announce Type: new Abstract: Simulation-to-real transfer remains a central challenge in robotics, as mismatches between simulated and real-world dynamics often lead to failures. While reinforcement learning offers a principled mechanism for adaptation, existing sim-to-real finetuning methods struggle with exploration and long-horizon credit assignment in the low-data regimes typical of real-world robotics. We introduce Simulation Distillation (SimDist), a sim-to-real framework that distills structural priors from a simulator into a latent world model and enables rapid real-world adaptation via […]

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arXiv:2602.15147v1 Announce Type: new Abstract: We introduce a new method, dubbed textbf{textit{Geometric Structure-Preserving Interpolation ({Gamma}-SPIN)}}, to simultaneously preserve physics-constraints inherent in the material parameter limits of the finite-strain Cosserat micropolar model, and satisfy objectivity under superimposed rigid body motions. The method advocates to interpolate the Cosserat rotation tensor using geodesic elements, which maintain objectivity and correctly represent curvature measures. At the same time, it proposes relaxing the interaction between the rotation tensor and the deformation tensor to alleviate […]

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Machine unlearning offers a practical alternative to avoid full model re-training by approximately removing the influence of specific user data. While existing methods certify unlearning via statistical indistinguishability from re-trained models, these guarantees do not naturally extend to model outputs when inputs are adversarially perturbed. In particular, slight perturbations of forget samples may still be correctly recognized by the unlearned model – even when a re-trained model fails to do so – revealing a novel privacy risk: information […]

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arXiv:2602.05704v1 Announce Type: cross Abstract: Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. To that end, a commonly used tool is the Statistical Queries (SQ) framework, which studies performance limits of algorithms based on noisy interaction with the data. However, it is known that the formal connection between the SQ framework and SGD is tenuous: Existing results typically rely on adversarial or specially-structured gradient noise that […]

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Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution. We argue that striving for the full joint distribution could be overkill; for greater data efficiency, models should prioritize learning the conditional distribution $P(ymid bm{X})$, as suggested by recent theoretical analysis. Therefore, we overcome this limitation with textbf{ReTabSyn}, a textbf{Re}inforced textbf{Tab}ular textbf{Syn}thesis pipeline that provides direct feedback on […]

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It’s hard to justify Tahoe icons Devastating critique of the new menu icons in macOS Tahoe by Nikita Prokopov, who starts by quoting the 1992 Apple HIG rule to not “overload the user with complex icons” and then provides comprehensive evidence of Tahoe doing exactly that. In my opinion, Apple took on an impossible task: to add an icon to every menu item. There are just not enough good metaphors to do something like that. But even if […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The Long Now of the Web: Inside the Internet Archive’s Fight Against Forgetting By @zbruceli [ 18 Min read ] A deep dive into the Internet Archive’s custom tech stack. Read More. Indie Hacking Vibe Coding Setup: What Changed in 6 Months By @ivankuznetsov […]

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