technocracy

Transformers have demonstrated a strong ability for in-context learning (ICL), enabling models to solve previously unseen tasks using only example input output pairs provided at inference time. While prior theoretical work has established conditions under which transformers can perform linear classification in-context, the empirical scaling behavior governing when this mechanism succeeds remains insufficiently characterized. In this paper, we conduct a systematic empirical study of in-context learning for Gaussian-mixture binary classification tasks. Building on the theoretical framework of Frei […]

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** These are ALL my ideas. LLM’s only used fo slight ‘polishing’. ** Been working on predicting sim-to-real transfer success BEFORE deploying to real hardware. The insight: successful transfers have a distinct “kinematic fingerprint” . Smooth, coordinated movements with margin for error. Failed transfers look jerky and brittle. We train a classifier on these signatures. Early results show 85-90% accuracy predicting which policies will work on real hardware, and 7x speedup when deploying to new platforms. The uncomfortable […]

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Intellectual property on the blockchain is supposed to be the future. Programmable licenses, instant provenance verification, transparent royalty splits – the pitch basically writes itself, but the developer tooling tells a different story entirely. Try registering an IP asset on Story Protocol today. You will format JSON metadata by hand. You will debug cryptic SDK error messages. You will burn testnet gas on failed transactions because nothing validated your inputs before hitting the chain. The whole process takes 15–30 minutes […]

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arXiv:2602.03165v1 Announce Type: cross Abstract: Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the target distribution is complex, such as multimodal distributions in highdimensional spaces, the efficiency of importance sampling critically depends on the choice of the proposal distribution. In this paper, we propose a novel adaptive scheme for the construction of efficient proposal distributions. Our algorithm promotes efficient exploration of the […]

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arXiv:2601.08833v1 Announce Type: new Abstract: Different from traditional Large Language Model (LLM) serving that colocates the prefill and decode stages on the same GPU, disaggregated serving dedicates distinct GPUs to prefill and decode workload. Once the prefill GPU completes its task, the KV cache must be transferred to the decode GPU. While existing works have proposed various KV cache transfer paths across different memory and storage tiers, there remains a lack of systematic benchmarking that compares their performance […]

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Stablecoins have come from nowhere to create a parallel financial world alongside traditional banking. These crypto tokens, usually pegged for a 1:1 exchange with fiat currencies and backed by liquid assets, aim to bring the best of blockchain to the table and add a dash of stability. Over the past few years, stablecoins have really taken off: we’re now talking about market capitalization in the hundreds of billions and trading volume in tens of trillions annually. They’re promising […]

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arXiv:2606.15442v1 Announce Type: new Abstract: We design a new unconstrained coordinate system where a $ptimes p$ symmetric positive definite (SPD) matrix $Theta$ is represented by a reverse telescoping map $Theta(x)=rm{RT}(x)$, with $x=(v,d,r)inmathbb{R}timesmathbb{R}^{(p-1)}timesmathbb{R}^{p(p-1)/2}$, representing respectively the log volume or log determinant; and the shape, as encoded by log relative diagonal scales and partial covariances among the nodes. This construction results in important properties not available in other charts, e.g., matrix logarithm, such as Jacobian depending on only the log-determinant. […]

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Neural models get the hype. But that lightning-fast word suggestion as you type? That’s not a neural model. It’s a lookup table. And it’s beating transformers on the metric that matters. I’ve spent a lot of time thinking about prediction under pressure. When I was working on Indian-language typing for early Kindle devices, we had a problem that no amount of ML enthusiasm could solve: the hardware simply didn’t care about our ambitions. We had roughly 512 MB […]

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