Published on December 18, 2025 5:53 PM GMT They say you’re supposed to choose your prior in advance. That’s why it’s called a “prior”. First, you’re supposed to say say how plausible different things are, and then you update your beliefs based on what you see in the world. For example, currently you are—I assume—trying to decide if you should stop reading this post and do something else with your life. If you’ve read this blog before, then […]

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Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of […]

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Large language models (LLMs) have achieved remarkable success in generating fluent and contextually appropriate text; however, their capacity to produce genuinely creative outputs remains limited. This paper posits that this limitation arises from a structural property of contemporary LLMs: when provided with rich context, the space of future generations becomes strongly constrained, and the generation process is effectively governed by near-deterministic dynamics. Recent approaches such as test-time scaling and context adaptation improve performance but do not fundamentally alter […]

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Table of Links Abstract and 1. Introduction 2. Financial Market Model and Worst-Case Optimization Problem 3. Solution to the Post-Crash Problem 4. Solution to the Pre-Crash Problem 5. A BSDE Characterization of Indifferences Strategies 6. The Markovian Case 7. Numerical Experiments Acknowledgments and References Appendix A. Proofs from Section 3 Appendix B. Proofs of BASDE Results from Section 5 Appendix C. Proofs of (CIR) Results from Section 6 Abstract. We study and solve the worst-case optimal portfolio problem […]

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arXiv:2601.21112v1 Announce Type: new Abstract: Frontier language model agents can exhibit misaligned behaviors, including deception, exploiting reward hacks, and pursuing hidden objectives. To control potentially misaligned agents, we can use LLMs themselves to monitor for misbehavior. In this paper, we study how information access affects LLM monitor performance. While one might expect that monitors perform better when they have access to more of the monitored agents’ reasoning and actions, we find that contemporary systems often perform better with […]

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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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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. ## Back to Basics: Database Design as Storytelling By @dataops [ 3 Min read ] Why great database design is really storytelling—and why ignoring relational fundamentals leads to poor performance AI can’t fix. Read More. Designing API Contracts for Legacy System Modernization By @jamescaron [ […]

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arXiv:2602.23551v1 Announce Type: new Abstract: Hyper-reduction methods have gained increasing attention for their potential to accelerate reduced order models for nonlinear systems, yet their comparative accuracy and computational efficiency are not well understood. Motivated by this gap, we evaluate a range of hyper-reduction techniques for nonlinear finite element models across benchmark problems of varying complexity, assessing the inevitable tradeoff between accuracy and speedup. More specifically, we consider interpolation methods based on the gappy proper orthogonal decomposition as well […]

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A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning. Existing distillation methods, which rely on L2-norm feature matching in raw feature space, are sensitive to noise and high-dimensional variability, often failing to preserve intrinsic task manifolds. To address this, we introduce SPREAD, a geometry-preserving framework that employs singular value […]

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