Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed at decision time. Many modern systems violate this assumption: after selecting an expert, one may also choose what additional information that expert should receive, such as retrieved documents, tool outputs, or escalation context. We study this problem and call it Learning-to-Defer with advice. We show that a broad family of natural separated surrogates, which learn […]

Ver mais

I’m working on an optimization problem with a complex environment. Environment is complex in inner working but has only one action input. The action can be either binary, or discrete, or continuous. If the environment is optimized on binary action the maximum reward will be less than when on discrete or continuous actions. PPO works when action is binary or discrete but not when it’s continuous. The input to the model needs to be a value between 0 […]

Ver mais

arXiv:2603.27395v1 Announce Type: cross Abstract: We propose a topological framework for the detection of Hopf bifurcations directly from time series, based on persistent homology applied to phase space reconstructions via Takens embedding within the framework of Topological Data Analysis. The central idea is that changes in the dynamical regime are reflected in the emergence or disappearance of a dominant one-dimensional homological features in the reconstructed attractor. To quantify this behavior, we introduce a simple and interpretable scalar topological […]

Ver mais

This study addresses the challenge of accurately identifying multi-task contention types in high-dimensional system environments and proposes a unified contention classification framework that integrates representation transformation, structural modeling, and a task decoupling mechanism. The method first constructs system state representations from high-dimensional metric sequences, applies nonlinear transformations to extract cross-dimensional dynamic features, and integrates multiple source information such as resource utilization, scheduling behavior, and task load variations within a shared representation space. It then introduces a graph-based modeling […]

Ver mais

arXiv:2602.16880v1 Announce Type: new Abstract: Uniform interpolation is a strong form of interpolation providing an interpretation of propositional quantifiers within a propositional logic. Pitts’ seminal work establishes this property for intuitionistic propositional logic relying on a sequent calculus in which na”ive backward proof-search terminates. This constructive approach has been adapted to a wide range of logics, including intuitionistic modal logics. Surprisingly, no intuitionistic modal logic with independent box and diamond has yet been shown to satisfy uniform interpolation. […]

Ver mais

arXiv:2603.08797v1 Announce Type: new Abstract: Applications in emerging domains such as XR are being built as compound inference systems, where multiple ML models are composed in the form of a task graph to service each request. Serving these compound systems efficiently raises two questions: how to apportion end-to-end latency and accuracy budgets between different tasks in a compound inference system, and how to allocate resources effectively for different models with varying resource requirements. We present JigsawServe, the first […]

Ver mais

After 20 dead versions and about 2 years of work, my RL agent (NASMU) passed its walk-forward backtest across 2020–2026. But the most interesting part wasn’t the results — it was what the model actually learned. The setup: – PPO + xLSTM (4 blocks), BTC/USDT 4h bars – 35 features distilled from López de Prado, Hilpisch, Kaabar, Chan and others – Triple Barrier labeling (TP/SL/Timeout) – HMM for regime detection (bull/bear/sideways) – Running on a Xeon E5-1650 v2 […]

Ver mais

arXiv:2602.16890v1 Announce Type: new Abstract: The introduction of generative artificial intelligence applications to the public has led to heated discussions about its potential impacts and risks for K-12 education. One particular challenge has been to decide what students should learn about AI, and how this relates to computational thinking, which has served as an umbrella for promoting and introducing computing education in schools. In this paper, we situate in which ways we should expand computational thinking to include […]

Ver mais