Runs ~20 options strategies against live market data in shadow mode, records every hypothetical fill under worst/base/optimistic assumptions, and grades each with anytime-valid e-processes. Places no orders.

Repository for the paper "Auditing Pay-Per-Token in Large Language Models", AISTATS'26

This repository contains the code for the paper "Optimizing Social Utility in Sequential Experiments".

A kernel-userland protocol enforcing information-theoretic bounds on AI adaptivity leakage, benchmark gaming, and capability spillover.

Benchmark for statistically valid AI scientist systems, using audit-closed protocols, transparency logs, and sequential inference to prevent false discoveries in autonomous research agents.

Measure your agent harness, find where it wastes the model, and prove the fix worked. Harness-agnostic, agent-agnostic, zero dependencies. Reference implementation of HTP-1.