Dimension Reducers builds tools to formalize, stress-test, verify, and structure mathematical knowledge. They offer solutions for LLM training, automated refereeing, and retrieval that understands mathematical structure. Their platform includes tools for refereeing at scale, adversarial testing ("torture testing"), and structured Retrieval Augmented Generation (RAG).
Key products include DiRe-JAX (a dimensionality reduction library), arXiv Math Semantic Search, arXiv Proof Audit Database, Mathematics Torture Chamber, and a Lean 4 Formalization Pipeline. They also publish research and benchmarks in mathematical formalization and OCR, emphasizing semantic accuracy and robustness.
Key products include DiRe-JAX (a dimensionality reduction library), arXiv Math Semantic Search, arXiv Proof Audit Database, Mathematics Torture Chamber, and a Lean 4 Formalization Pipeline. They also publish research and benchmarks in mathematical formalization and OCR, emphasizing semantic accuracy and robustness.
This is an open, unconventional textbook covering mathematics, computing, and artificial intelligence from foundational principles. It's designed for practitioners seeking a deep understanding, moving beyond exam preparation and focusing on real-world application. The author, drawing from years of experience in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
This repository provides the official implementation of the STATIC (Sparse Transition-Accelerated Trie Index for Constrained decoding) framework, as described in Su et al., 2026. STATIC is a high-performance method for enforcing outputs to stay within a prespecified set during autoregressive decoding from large language models, designed for maximum efficiency on modern hardware accelerators like GPUs and TPUs.
