Working paper · 2026 · confirmatory result pendingAgent Harness: Deterministic Control for Probabilistic Systems
A five-plane decomposition and a conditional invariant-preservation result
Separates proposal authority from effect authority in an agent harness, then proves that under complete mediation of a declared effect boundary and a sound commit gate, every reachable protected state preserves the declared invariant — regardless of what the model proposes. The corollary is the useful part: model stochasticity stops mattering to the safety argument once proposals carry no independent commit authority. Study pre-registered before analyzer code was written.
Working paper · 2026 · preprint pending releaseThe Return Base Rate
Captured knowledge is almost never reused in a production AI-agent system
The agent-memory literature measures how well agents use prior context on evaluations where the answer-bearing memory is guaranteed present. This measures the step upstream: in a live system, does captured knowledge get surfaced and reused at all? Over an append-only, hash-chained ledger, return is effectively zero. Decomposes that into a funnel, localizes the stage the instrument could not previously see, and pre-registers the intervention.