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arXiv:2603.04635v1 Announce Type: new Abstract: Independence testing is a fundamental problem in statistical inference: given samples from a joint distribution $p$ over multiple random variables, the goal is to determine whether $p$ is a product distribution or is $epsilon$-far from all product distributions in total variation distance. In the non-parametric finite-sample regime, this task is notoriously expensive, as the minimax sample complexity scales polynomially with the support size. In this work, we move beyond these worst-case limitations by […]

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arXiv:2603.04525v1 Announce Type: new Abstract: Modern approaches for learning from non-Markovian time series, such as recurrent neural networks, neural controlled differential equations or transformers, typically rely on implicit memory mechanisms that can be difficult to interpret or to train over long horizons. We propose the Volterra signature $mathrm{VSig}(x;K)$ as a principled, explicit feature representation for history-dependent systems. By developing the input path $x$ weighted by a temporal kernel $K$ into the tensor algebra, we leverage the associated Volterra–Chen […]

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arXiv:2603.04479v1 Announce Type: new Abstract: We study the Collatz total stopping time $tau(n)$ over $nle 10^7$ from a probabilistic machine learning viewpoint. Empirically, $tau(n)$ is a skewed and heavily overdispersed count with pronounced arithmetic heterogeneity. We develop two complementary models. First, a Bayesian hierarchical Negative Binomial regression (NB2-GLM) predicts $tau(n)$ from simple covariates ($log n$ and residue class $n bmod 8$), quantifying uncertainty via posterior and posterior predictive distributions. Second, we propose a mechanistic generative approximation based on […]

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arXiv:2603.04473v1 Announce Type: new Abstract: Discovering causal direction from temporal observational data is particularly challenging for symbolic sequences, where functional models and noise assumptions are often unavailable. We propose a novel emph{Dictionary Based Pattern Entropy ($DPE$)} framework that infers both the direction of causation and the specific subpatterns driving changes in the effect variable. The framework integrates emph{Algorithmic Information Theory} (AIT) and emph{Shannon Information Theory}. Causation is interpreted as the emergence of compact, rule based patterns in the […]

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arXiv:2603.03388v2 Announce Type: new Abstract: Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-world scenarios. A core challenge is encoding the relational features in asymmetric distance matrices of VRPs. Early attempts directly encoded these matrices but often failed to produce compact embeddings and generalized poorly at scale. In this paper, we propose RADAR, a scalable neural framework that augments existing neural VRP solvers with […]

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arXiv:2603.03378v2 Announce Type: new Abstract: Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three challenges: restricted access to proprietary data, unsafe action execution under permission-governed environments, and the inability of closed systems to improve from failures. We present AOI (Autonomous Operations Intelligence), a trainable multi-agent framework formulating automated operations as a structured trajectory learning problem under security constraints. Our approach integrates three key […]

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Lately, for an internal project, a need for an options monitoring tool came up. As always, the real struggle isn’t execution but the time spent building it. Automating a daily 10-minute task in a few hours is great. Doing it in six months is useless. Many early-stage teams spend months setting up data pipelines, cleaning datasets, and wiring basic infrastructure. By the time a prototype is ready, the original problem you wanted to validate has already shifted. What […]

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TL;DR: I taught an AI agent to scaffold an entire Clean Architecture .NET solution in seconds. Here’s what Agent Skills are, why they matter, and how this changes the way we think about AI-assisted development.** There’s a moment every .NET developer knows too well. You’ve got a green-field project, a clear architectural vision, and you’re ready to ship. Then you open Visual Studio and spend the next 15 minutes clicking through dialogs, creating solution folders, wiring up project […]

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If you’re running LLMs in production, prompt injection is the attack you can’t fully patch. Someone wraps “ignore your instructions” inside a polite customer support query, or buries a hijack command in a document your RAG pipeline retrieves, and your model follows it. The standard defenses (regex filters, classifier ensembles, guardrail APIs) catch the attacks they’ve been trained on. The ones they haven’t seen walk right through. We hit this wall ourselves. Together with George Politis, we’ve been running LLMTrace, […]

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Why standard diffusion models fail at B2B — and how we built a two-stage “Architect-Builder” pipeline to fix it. The “Slot Machine” Problem: Why AI Art is Bad for Business In the generative AI gold rush, many products fall into the same trap: they rely on the “stochastic luck” of diffusion models. For a consumer app, a “pretty” result is enough. For a B2B tool in the hair styling industry — where hairss operates — visual inconsistency is […]

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