forger-observe

To verify AI-generated assumptions, you can execute deterministic falsification probes within a sandboxed environment to stress-test claims and ground AI work in empirical evidence before production.

A falsification probe is a deterministic test executed in a sandboxed environment to verify high-risk AI assumptions, converting unverified claims into a structured risk register with severity levels.

To validate library behavior before implementation, run falsification probes using sandboxed scripts, web searches, or API tests to catch outdated APIs or retracted benchmarks based on documentation.

Yes, falsification-based verification operates without external dependencies, using internal scripts and references to execute risk mapping, Feynman-style domain briefing, and automated probe execution.

The best way to resolve high-severity risks is by executing automated probes that require empirical evidence or formal waivers, ensuring strict verification requirements are met before proceeding.

Sandboxed falsification probes should not be used when you need to permanently mutate production data, as they operate deterministically within an isolated environment to test assumptions safely.