This first episode walks through a simple question: can a small, model-aware assist improve an “echo” without spending extra effort?

An echo is a forward run followed by a reverse run, where you try to land back on the start state. In this episode, I summarize a preregistered VDM experiment called Counterfactual Echo Gain (CEG). The point is not hype. It’s auditability: the measurement method is treated like an instrument, with pass/fail gates that must hold before we interpret outcomes.

You’ll hear:

- What the assist changes (and what it must not change)

- The gate checks (reversibility drift, monotonic dissipation checks, step-size accuracy, and equal-work matching)

- The outcome metric and what the result actually supports

If you want to verify it, find the record on Zenodo (includes the report, run ledger, telemetry, and figures).