arXiv:2603.22488v1 Announce Type: new Abstract: Current architecture proposals within standards development organizations such as ETSI and 3GPP enable sensing capabilities in mobile networks; however, they do not include a repository for storing sensing data. Such a repository can be used for AI model training and to complement ongoing sensing service provisioning by improving efficiency and accuracy. One way of realizing this is through the fusion of historical sensing data with live sensing data. In this paper, we study […]

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Scaling reinforcement learning to tens of thousands of parallel environments requires overcoming the limited exploration capacity of a single policy. Ensemble-based policy gradient methods, which employ multiple policies to collect diverse samples, have recently been proposed to promote exploration. However, merely broadening the exploration space does not always enhance learning capability, since excessive exploration can reduce exploration quality or compromise training stability. In this work, we theoretically analyze the impact of inter-policy diversity on learning efficiency in policy […]

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This short article compiles a few notes I previously shared when readers ask how to get the most out of my building large language model from scratch books. I follow a similar approach when I read technical books myself. It is not meant as a universal recipe, but it may be a helpful starting point. For this particular book, I strongly suggest reading it in order since each chapter depends on the previous one. And for each chapter, […]

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How are you, hacker? 🪐 What’s happening in tech today, March 15, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, First Newsletter of the Homebrew Computer Club in 1975, and we present you with these top quality stories. From The OpenClaw Saga: How the Last Two Weeks Changed the Agentic AI World Forever to Create a Website Without Code: How Fabricate Turns Conversations Into Full-Stack Apps, let’s dive right in. The […]

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arXiv:2603.19633v1 Announce Type: cross Abstract: This work introduces a new approximate proximal sampler that operates solely with zeroth-order information of the potential function. Prior theoretical analyses have revealed that proximal sampling corresponds to alternating forward and backward iterations of the heat flow. The backward step was originally implemented by rejection sampling, whereas we directly simulate the dynamics. Unlike diffusion-based sampling methods that estimate scores via learned models or by invoking auxiliary samplers, our method treats the intermediate particle […]

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The same substations. The same cooling corridors. The same industrial shell, built to turn cheap power into compute. But inside a growing number of large mining sites in 2026, the shift is clear: ASICs are coming out, and GPUs are going in. This is not a cyclical adjustment. It is a structural repricing of compute. For years, Bitcoin (BTC) mining was the obvious way to monetize access to cheap electricity, fast deployment, and high-density capacity. Now AI is […]

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arXiv:2603.02259v1 Announce Type: new Abstract: Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain essential as increasingly powerful autonomous decision components are embedded within agent-based systems. While learned and generative models substantially expand system capability, their safety behavior is often entangled with training, making it opaque, difficult to audit, and costly to update after deployment. This paper formalizes the Alignment Flywheel as a governance-centric hybrid MAS architecture that decouples decision generation from […]

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Chain-of-thought reasoning, where language models expend additional computation by producing thinking tokens prior to final responses, has driven significant advances in model capabilities. However, training these reasoning models is extremely costly in terms of both data and compute, as it involves collecting long traces of reasoning behavior from humans or synthetic generators and further post-training the model via reinforcement learning. Are these costs fundamental, or can they be reduced through better algorithmic design? We show that autocurriculum, where […]

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arXiv:2602.17801v1 Announce Type: new Abstract: A Euclidean noncrossing Steiner $(1+epsilon)$-spanner for a point set $Psubsetmathbb{R}^2$ is a planar straight-line graph that, for any two points $a, b in P$, contains a path whose length is at most $1+epsilon$ times the Euclidean distance between $a$ and $b$. We construct a Euclidean noncrossing Steiner $(1+epsilon)$-spanner with $O(n/epsilon^{3/2})$ edges for any set of $n$ points in the plane. This result improves upon the previous best upper bound of $O(n/epsilon^{4})$ obtained nearly […]

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