Verifier Ecology Theory: Packetized Self-Verification Under Residual Accountability

Verifier Ecology Theory (VET) is a structural theory for systems in which generating candidates is no longer the main bottleneck. The paper focuses on the harder problem of how a system can evaluate, repair, reject, preserve, certify, and update the tests, scores, proofs, monitors, benchmarks, assumptions, and procedures that it uses to judge its own outputs. The central idea is the verifier packet: a structured and revisable verification asset that records its origin, scope, procedure, certification conditions, destructive and narrowing boundaries, residual records, update or retirement conditions, and circulation status. Instead of treating a test, metric, proof, or evaluator as an isolated authority, VET studies an ecology of such packets and asks whether this ecology can keep its own failures visible and repairable. A key concept is residual accountability. Unknowns, failed assumptions, missing evidence, scope changes, translation losses, counterexamples, and safety gaps should not be silently erased. They should be recorded as residuals and routed into future question formation, counterexample search, boundary revision, packet repair, schema revision, quarantine, retirement, or justified preservation. The paper identifies verifier overclosure as a central failure mode. This occurs when a verification system appears to improve local accuracy, speed, or certification power while narrowing future question formation, deleting residuals, forcing unfamiliar cases into existing schemas, or making destructive and narrowing paths harder to detect. VET distinguishes this from ordinary metric failure, Goodhart effects, reward hacking, and benchmark saturation by treating residual preservation, packet lineage, schema revisability, and future verification capacity as explicit state variables. The theory does not assume an infallible verifier, a hidden “true” world variable, or a complete probability distribution over all future cases. It also does not reject experiments, statistics, formal methods, benchmarks, or probabilistic tools. Instead, these tools are treated as local packet procedures whose scope, assumptions, evidence sources, limits, residuals, and failure modes must be recorded. The paper develops a minimal process model based on observable histories, typed records, residual ledgers, verifier packets, certification profiles, reachability certificates, counter-packets, local authority decisions, and packet circulation between systems. It also gives machine-readable schema sketches, runtime-loop guidance, and audit protocols for implementing verifier ecology in autonomous research systems, AI evaluation pipelines, self-verifying agents, safety monitoring, scientific discovery workflows, and systems that must revise their own evaluators over time. Overall, VET proposes that open-ended progress in advanced AI systems depends not only on stronger generation, but also on disciplined growth of the verification ecology: preserving what remains uncertain, keeping certification scoped, separating evidence from authority, maintaining counterexample channels, and preventing verification itself from becoming a closed and brittle system.