technocracy

arXiv:2602.09109v1 Announce Type: new Abstract: With the rapid growth of large language models (LLMs), a wide range of methods have been developed to distribute computation and memory across hardware devices for efficient training and inference. While existing surveys provide descriptive overviews of these techniques, systematic analysis of their benefits and trade offs and how such insights can inform principled methodology for designing optimal distributed systems remain limited. This paper offers a comprehensive review of collective operations and distributed […]

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arXiv:2603.20223v1 Announce Type: new Abstract: Immediate feedback is a foundational requirement of effective AI-mediated learning, yet the energy and latency costs of delivering it remain largely unexamined. This study investigates the latency-energy-learning trade-off in AI tutoring through an empirical comparison of two on-device inference configurations of Microsoft Phi-3 Mini (4k-instruct) on an NVIDIA T4 GPU: full-precision FP16 and 4-bit NormalFloat (NF4) quantisation. Both were evaluated under KV-cache-enabled inference across 500 educational prompts spanning five secondary school subject domains. […]

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Key Highlights: The removal of all third-party harnesses from Claude subscription plans by Anthropic reflects an important shift in how AI platforms are evolving. Developers will now have to pay separately to run agent workflows, essentially changing how these systems are used and scaled. Anthropic’s Reason behind Doing this Boris Cherny, head of Claude Code said in a statement: “We’ve been working hard to meet the increase in demand for Claude, and our subscriptions weren’t built for the […]

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Greedy algorithms are central to sparse approximation and stage-wise learning methods such as matching pursuit and boosting. It is known that the Power-Relaxed Greedy Algorithm with step sizes $m^{-α}$ may fail to converge when $α>1$ in general Hilbert spaces. In this work, we revisit this phenomenon from a sparse learning perspective. We study realizable regression problems with controlled feature coherence and derive explicit lower bounds on the residual norm, showing that over-decaying step-size schedules induce structural stagnation even […]

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arXiv:2601.21070v1 Announce Type: new Abstract: Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered […]

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arXiv:2601.19950v1 Announce Type: new Abstract: This paper introduces and analyzes emph{defensive rebalancing}, a novel mechanism for protecting constant-function market makers (CFMMs) from value leakage due to arbitrage. A emph{rebalancing} transfers assets directly from one CFMM’s pool to another’s, bypassing the CFMMs’ standard trading protocols. In any emph{arbitrage-prone} configuration, we prove there exists a rebalancing to an textit{arbitrage-free} configuration that strictly increases some CFMMs’ liquidities without reducing the liquidities of the others. Moreover, we prove that a configuration is […]

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Introduction: The Illusion of Simplicity You probably type go build or go run dozens of times every week without thinking much about what happens under the hood. On the surface, these commands feel almost magical: you press Enter, and suddenly your code is compiled, linked, and – sometimes – executed. But beneath that simplicity lies a carefully orchestrated system, optimized to make your life as a developer easier while also being fast and predictable for machines. Understanding how […]

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Finalist teams advance in the Amazon Nova AI Challenge: Trusted AI Track Top eight university teams move on to head-to-head finals focused on AI security for code generation. Conversational AI Staff writer June 24, 02:11 PM June 30, 02:49 PM Since November 2024, ten top university teams from around the world have competed in the inaugural Amazon Nova AI Challenge: Trusted AI Track, focused on strengthening security in AI coding assistants and developing new automated methods to red-team […]

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