LargeQuant builds quantitative intelligence infrastructure for systems where the output has to be numerically right.

LargeQuant builds the runtime, evidence protocol, and evaluation infrastructure for Large Quantitative Models (LQMs) — governed computational systems that turn quantitative evidence into quantitative output through reproducible calculation, statistical learning, simulation, forecasting, and optimization.

Our architectural principle: language can invoke, compose, and explain a quantitative model. Language is never the numerical authority. Every execution on the Large Quant runtime produces a signed Evidence Packet (LQEP) binding the model version, input data, parameters, and output to a verifiable, reproducible record — not just a plausible-sounding answer.

LargeQuant provides:
— LQM Runtime: a governed execution substrate for deterministic, statistical, econometric, forecasting, risk, optimization, simulation, and learned quantitative models
— LQ Evidence Protocol (LQEP): Ed25519-signed, independently verifiable execution evidence
— LQBench: public benchmarks for quantitative fidelity, calibration, and reproducibility
— An open LQM Specification for the model registry format

FinanceGPT is our reference implementation, proving the architecture in financial services before it extends to other quantitative domains.

We believe "large" doesn't have to mean billions of parameters — it means the scope, rigor, and verifiability of the quantitative model system behind the answer.

Large Quantitative Models, Quantum Computing, Artificial Intelligence, and Data Science