technocracy

Published on January 4, 2026 10:11 PM GMT After five months of me (Buck) being slow at finishing up the editing on this, we’re finally putting out our inaugural Redwood Research podcast. I think it came out pretty well—we discussed a bunch of interesting and underdiscussed topics and I’m glad to have a public record of a bunch of stuff about our history. Tell your friends! Whether we do another one depends on how useful people find this […]

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I’ve been on both sides of the interview table. I’ve sweated through LeetCode at 11pm with a toddler finally asleep in the next room. I’ve also run interviews at big tech companies and watched smart engineers torpedo themselves, not because they couldn’t code, but because they forgot how to think out loud. This article is not about grinding 300 problems until your brain leaks. It’s about what actually happens inside the interview room, and how to not blow […]

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arXiv:2603.20286v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to extend their existing knowledge by dynamically incorporating external information. However, practical deployment is fundamentally constrained by the LLM’s finite context window, forcing a trade-off between information sufficiency and token consumption. Standard pipelines address this via a retrieve-then-select strategy, typically retaining only the top-k chunks based on relevance. Nevertheless, this approach is suboptimal: it inherently truncates critical bridging evidence located in the long tail of […]

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arXiv:2410.18844v2 Announce Type: replace-cross Abstract: Pure exploration in bandits formalises multiple real-world problems, such as tuning hyper-parameters or conducting user studies to test a set of items, where different safety, resource, and fairness constraints on the decision space naturally appear. We study these problems as pure exploration in multi-armed bandits with unknown linear constraints, where the aim is to identify an $r$-optimal and feasible policy as fast as possible with a given level of confidence. First, we propose […]

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arXiv:2601.03220v2 Announce Type: replace-cross Abstract: Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions, Shannon information and Kolmogorov complexity come up nearly empty-handed, in part because they assume observers with unlimited computational capacity and do not target the useful information content. In this […]

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arXiv:2604.07385v1 Announce Type: new Abstract: We present SauerkrautLM-Doom-MultiVec, a 1.3 million parameter model that plays the classic first-person shooter DOOM in real time, outperforming large language models up to 92,000x its size, including Nemotron-120B, Qwen3.5-27B, and GPT-4o-mini. Our model combines a ModernBERT encoder with hash embeddings, depth-aware token representations, and an attention pooling classification head to select game actions from ASCII frame representations at 31ms per decision. Trained on just 31,000 human gameplay demonstrations, it achieves 178 frags […]

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Modernization of enterprise technologies is no longer just about infrastructure improvement. It involves change across all levels-systems and processes, as well as people and culture. Many organizations continue to rely on legacy systems that once performed effectively but can no longer support increasing demands, growing data volumes, and evolving customer expectations. These systems are not only becoming more expensive to maintain, but they also lack scalability, and their security risks are increasing. The alternative is a shift toward […]

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arXiv:2506.05905v2 Announce Type: replace-cross Abstract: We consider the problem of sampling from a probability distribution $pi$. It is well known that this can be written as an optimisation problem over the space of probability distribution in which we aim to minimise the Kullback–Leibler divergence from $pi$. We consider several partial differential equations (PDEs) whose solution is a minimiser of the Kullback–Leibler divergence from $pi$ and connect them to well-known Monte Carlo algorithms. We focus in particular on PDEs […]

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