arXiv:2601.21789v1 Announce Type: cross Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regression benchmarks, our method recovers a larger fraction of target equations than competing state-of-the-art approaches while requiring substantially less computation. Leveraging this efficiency, ECSEL […]

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arXiv:2601.02513v1 Announce Type: new Abstract: We derive and analyze well-posed, energy- and entropy-stable boundary conditions (BCs) for the two-dimensional linear and nonlinear rotating shallow water equations (RSWE) in vector invariant form. The focus of the study is on subcritical flows, which are commonly observed in atmospheric, oceanic, and geostrophic flow applications. We consider spatial domains with smooth boundaries and formulate both linear and nonlinear BCs using mass flux, Riemann’s invariants, and Bernoulli’s potential, ensuring that the resulting initial […]

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Systematic literature reviews (SLRs) are fundamental to evidence-based research, but manual screening is an increasing bottleneck as scientific output grows. Screening features low prevalence of relevant studies and scarce, costly expert decisions. Traditional active learning (AL) systems help, yet typically rely on fixed query strategies for selecting the next unlabeled documents. These static strategies do not adapt over time and ignore the relational structure of scientific literature networks. This thesis introduces AutoDiscover, a framework that reframes AL as […]

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I think I am at least somewhat more productive than average, and people sometimes ask me for productivity tips.  So I decided to just write them all down in one place. Compound growth gets discussed as a financial concept, but it works in careers as well, and it is magic.  A small productivity gain, compounded over 50 years, is worth a lot.  So it’s worth figuring out how to optimize productivity. If you get 10% more done and […]

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LadybirdBrowser/ladybird: Abandon Swift adoption Back in August 2024 the Ladybird browser project announced an intention to adopt Swift as their memory-safe language of choice. As of this commit it looks like they’ve changed their mind: Everywhere: Abandon Swift adoption After making no progress on this for a very long time, let’s acknowledge it’s not going anywhere and remove it from the codebase. Via Hacker News Tags: ladybird, swift

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On June 9, 1,000+ founders, investors, and decision-makers will gather for TechCrunch Founder Summit 2026. This isn’t just foot traffic. It’s a full day of concentrated deal flow. An exhibit table turns that energy into measurable growth. You don’t need more awareness. You need customers, capital, and strategic partners. Secure your exhibit table and put your startup at the center of the startup universe. Why exhibiting drives real growth Direct access to buyers: […]

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Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challenging due to the scarcity of high-quality trajectories, especially under limited resources. Existing methods typically scale up rollout sizes and indiscriminately allocate computational resources among intermediate steps. Such attempts inherently waste substantial computation budget on trivial steps while failing to guarantee sample quality. To address this, we propose textbf{Spark} (textbf{S}trategic textbf{P}olicy-textbf{A}ware explotextbf{R}ation via textbf{K}ey-state dynamic branching), a novel framework […]

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arXiv:2602.12380v1 Announce Type: new Abstract: Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and generalization across diverse market conditions. This research presents a hybrid stacked-generalization framework, TFT-ACB-XML, for BTC closing price prediction. The framework integrates two parallel base learners: a customized Temporal Fusion Transformer (TFT) and an Attention-Customized Bidirectional Long Short-Term Memory network (ACB), followed by an […]

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arXiv:2407.21082v2 Announce Type: replace-cross Abstract: This paper presents a modular approach to accelerate inference in large language models (LLMs) by adding early exit heads at intermediate transformer layers. Each head is trained in a self-supervised manner to mimic the main model’s predictions, allowing computation to stop early when a calibrated confidence threshold is reached. We evaluate several confidence metrics and show that entropy provides the most reliable separation between correct and incorrect predictions. Experiments on the Pythia model […]

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[Updated on 2023-01-24: add a small section on Distillation.] Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale. Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are […]

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