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Check out the (early) project and source code on GitHub.
This paper introduces a methodology for generating high-quality, diverse training data for Language Models (LMs) in complex problem-solving domains. Our approach, termed …
This paper provides complete analytical results for maximum likelihood estimation in series systems with masked failure data under exponential component lifetimes. Unlike numerical approaches, everything here has a closed form.
In series …
A C++17 header-only library implementing Computational Basis Transforms - a unified framework for understanding how FFT, logarithmic arithmetic, and Bayesian inference are all instances of the same pattern.
A modern C++ header-only library implementing disjoint interval sets as first-class mathematical objects with rigorous Boolean algebra operations.
A powerful symbolic expression toolkit for rule-based term rewriting with pattern matching, multiple input formats, and an interactive REPL.
What if we could compute on encrypted data while preserving algebraic structure? Not through expensive homomorphic encryption, but through a principled mathematical framework that unifies oblivious computing, Bernoulli types, and categorical …
What if a perfect hash function could simultaneously be: (1) cryptographically secure, (2) space-optimal, and (3) maximum-entropy encoded? This paper proves such a construction exists—and analyzes exactly what you sacrifice to get all three.