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 […]

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The Secret The sun in the Free Zone didn’t just shine; it weighed. Kael walked through the Market Sector with his head down, clutching a plastic jug against his chest. The jug was empty. Around him, the “Market” was a cacophony of arguments. There were no price tags. Every transaction was a debate. “Three batteries?” a vendor screamed at a woman holding a can of beans. “Yesterday it was two! You’re gouging me!” “The batteries are dead!” the […]

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The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application of these methods to node classification tasks still faces a number of challenges. First, existing data enhancement techniques may lead to significant differences from the original view when generating new views, which may weaken the relevance of the view and affect the efficiency of model […]

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arXiv:2602.13184v1 Announce Type: cross Abstract: Unbinned likelihood fits aim at maximizing the information one can extract from experimental data, yet their application in realistic statistical analyses is often hindered by the computational cost of profiling systematic uncertainties. Additionally, current machine learning-based inference methods are typically limited to estimating scalar parameters in a multidimensional space rather than full differential distributions. We propose a general framework for Simulation-Based Inference (SBI) that efficiently profiles nuisance parameters while measuring multivariate Distributions of […]

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The data was fine. For now. That was the problem. Sharing something from my own work that stuck with me. Hope it resonates. I was not looking for it. I was in the middle of a data programme. SQL scripts, Power BI dashboards, process maps, the usual. Head down in the analysis, trying to make sense of what the systems actually held versus what people thought they held. Those two things are often quite different and the gap […]

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Hey all. I’ve been building a poker bot competition platform called Chipzen for almost a year and just opened the closed alpha. Posting here because this sub is the place whose technical pushback will tell me what I got wrong. Engine specifics: HU no-limit hold’em, 10K starting stack, 50/100 blinds with escalation, elimination format 1500ms per-decision budget – anything heavier than a few-million-info-set CFR distillation hits the wall, by design WebSocket protocol, JSON game-state on each act OSS […]

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arXiv:2602.03969v1 Announce Type: new Abstract: The emergence of large language models (LLMs) represents a significant technological shift within the scientific ecosystem, particularly within the field of artificial intelligence (AI). This paper examines structural changes in the AI research landscape using a dataset of arXiv preprints (cs.AI) from 2021 through 2025. Given the rapid pace of AI development, the preprint ecosystem has become a critical barometer for real-time scientific shifts, often preceding formal peer-reviewed publication by months or years. […]

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From “rug pulls” to corporate warfare, the rules of software ownership are being rewritten. But a new model—Post-SaaS—might finally offer a way out. For decades, the deal was simple: developers gave their time, and companies gave their code. It was an unwritten social contract built on trust. Then, in a few short months, the contract was shredded. First came the tremors. In August of 2023, HashiCorp announced that Terraform—the industry standard for infrastructure-as-code—was switching to a “Business Source […]

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Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, aka, graph flow model (GFM), has emerged due to its superior performance and flexible sampling. However, effectively aligning GFMs with complex human preferences or task-specific objectives remains a significant challenge. In this paper, we propose Graph-GRPO, an online reinforcement learning (RL) framework for training GFMs under verifiable rewards. Our method makes two key contributions: (1) We derive an analytical […]

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For more than a decade, growth hacking was the defining strategy of startup success. Founders experimented relentlessly with referral loops, paid acquisition tricks, virality mechanics, and funnel optimizations. The goal was simple: unlock explosive growth as quickly as possible. And for a time, this worked. Companies used clever growth hacking tactics to scale from zero to millions of users seemingly overnight. The startup ecosystem embraced the idea that the right hack could transform a product into the next […]

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