technocracy

arXiv:2602.11304v1 Announce Type: new Abstract: Modern analyst agents must reason over complex, high token inputs, including dozens of retrieved documents, tool outputs, and time sensitive data. While prior work has produced tool calling benchmarks and examined factuality in knowledge augmented systems, relatively little work studies their intersection: settings where LLMs must integrate large volumes of dynamic, structured and unstructured multi tool outputs. We investigate LLM failure modes in this regime using crypto as a representative high data density […]

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The gap between AI insiders and everyone else is widening, and the spending, suspicion, and even new vocabulary are starting to show it. While OpenAI is busy buying up everything from finance apps to talk shows, a certain shoe company just rebranded as an AI infrastructure play, and Anthropic unveiled a model it says is too powerful to release publicly …but apparently not too […]

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arXiv:2602.11190v1 Announce Type: new Abstract: Time series forecasting is crucial for the World Wide Web and represents a core technical challenge in ensuring the stable and efficient operation of modern web services, such as intelligent transportation and website throughput. However, we have found that existing methods typically employ a strategy of embedding each time step as an independent token. This paradigm introduces a fundamental information bottleneck when processing long sequences, the root cause of which is that independent […]

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In late September, the United Kingdom’s Prime Minister Keir Starmer announced his government’s plans to introduce a new digital ID scheme in the country to take effect before the end of the Parliament (no later than August 2029). The scheme will, according to the Prime Minister, “cut the faff” in proving people’s identities by creating a virtual ID on personal devices with information like people’s name, date of birth, nationality or residency status, and photo to verify their […]

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arXiv:2603.02268v1 Announce Type: new Abstract: EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal Model), a masked autoencoder ablated along two axes — pretraining population and downstream adaptation — with architecture and preprocessing fixed. We compare a narrow-source EU/US corpus (TUH + PhysioNet) against a geographically diverse pool augmented with multi-center South […]

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arXiv:2602.07219v1 Announce Type: new Abstract: We study the approximation of the median of $d$ inputs using ReLU neural networks. We present depth-width tradeoffs under several settings, culminating in a constant-depth, linear-width construction that achieves exponentially small approximation error with respect to the uniform distribution over the unit hypercube. By further establishing a general reduction from the maximum to the median, our results break a barrier suggested by prior work on the maximum function, which indicated that linear width […]

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