technocracy

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. The Long Now of the Web: Inside the Internet Archive’s […]

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arXiv:2603.08763v1 Announce Type: new Abstract: 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 […]

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arXiv:2603.28777v1 Announce Type: new Abstract: No matter how much the world of computing changes, system design remains crucial. While most people try to learn it through quick tutorials or AI-generated summaries, there is no better way to master the field than by studying the original research papers. This book serves as a roadmap through those foundational texts, covering seminal papers in distributed systems, operating systems, and big data. It doesn’t just look at what these systems do; it […]

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arXiv:2604.01302v1 Announce Type: new Abstract: We study how to scale reasoning token budgets for competitive programming through two complementary approaches: training-time reinforcement learning (RL) and test-time parallel thinking. During RL training, we observe an approximately log-linear relationship between validation accuracy and the average number of generated reasoning tokens over successive checkpoints, and show two ways to shift this training trajectory: verification RL warmup raises the starting point, while randomized clipping produces a steeper trend in the observed regime. […]

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arXiv:2601.16988v1 Announce Type: new Abstract: The rapid expansion of scholarly publications across diverse disciplines has made it increasingly difficult to systematically evaluate how research contributes to the United Nations Sustainable Development Goals (SDGs). Domain classification of research articles done manually through research experts is extremely impractical because of the number of publications, expensive in time and may not be consistent when done by human beings. This paper proposes an automated and rule-based computational model of classifying research papers […]

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While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), its potential is fundamentally bounded by the base model’s existing output distribution. Optimizing the marginal distribution P(y) in the Pre-train Space addresses this bottleneck by encoding reasoning ability and preserving broad exploration capacity. Yet, conventional pre-training relies on static corpora for passive learning, leading to a distribution shift that hinders targeted reasoning enhancement. In this paper, we introduce PreRL (Pre-train Space […]

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arXiv:2604.15350v1 Announce Type: new Abstract: We discover that large language models exhibit emph{spectral phase transitions} in their hidden activation spaces when engaging in reasoning versus factual recall. Through systematic spectral analysis across textbf{11 models} spanning textbf{5 architecture families} (Qwen, Pythia, Phi, Llama, DeepSeek-R1), we identify textbf{seven} core phenomena: (1)~textbf{Reasoning Spectral Compression} — 9/11 models show significantly lower $alpha$ for reasoning ($p < 0.05$), with larger effects in stronger models; (2)~textbf{Instruction Tuning Spectral Reversal} — base models show reasoning […]

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arXiv:2602.15832v1 Announce Type: new Abstract: Existing user simulations, where models generate user-like responses in dialogue, often lack verification that sufficient user personas are provided, questioning the validity of the simulations. To address this core concern, this work explores the task of identifying relevant but unknown personas of the simulation target for a given simulation context. We introduce PICQ, a novel dataset of context-aware choice questions, annotated with unknown personas (e.g., ”Is the user price-sensitive?”) that may influence user […]

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