This preprint introduces cECT (computable Embodied Constructor Theory) as a reliability-based meta-controller for multimodal agents and latent world models. The central problem addressed is the Capability Admission Gap: Large Multimodal Models (LMMs) can generate plausible plans, tool-use strategies, and action descriptions, but they lack an internal criterion for determining when such generated behavior corresponds to an actually executable and reliable capability. The paper formalizes a System-2 control layer over the System-1 generative substrate of LMMs. This layer is defined by an operational quadruple: Qₜ, Mₜ, ωₜ, and KPₜ. Qₜ functions as a latent world model or state predictor; Mₜ functions as an inertial action-bias state; ωₜ functions as a dynamic regulatory gain; and KPₜ functions as a Knowledge Potential, interpreted here as an evidence accumulator for reliable system-environment coupling. The key engineering contribution is the use of KPₜ as a task-admission gatekeeper. Rather than allowing an agent to treat a fluent plan, a single successful rollout, or a plausible tool-use sequence as evidence of real competence, the cECT controller admits a candidate task τ into the agent’s repertoire Pₜ only when preconditions, reliability, and KPₜ thresholds are satisfied. This reframes grounding in multimodal agents as validated transformation rather than representation matching. The paper reports an initial sufficiency experiment in a hidden-structure environment. In this configuration, the observable state was capped below 0. 46 before unlock, while the target task required stabilization around 0. 75 ± 0. 03. The cECT engine detected a latent gate, crossed the KP threshold at step 21, admitted the out-of-distribution task at step 22, and reproduced successful task admission across 20/20 validation reruns. The result is interpreted as sample-efficient autonomous task admission in fewer than 25 interactions under the tested configuration. The broader aim of the paper is to provide an auditable control architecture for agentic AI systems that distinguishes representation from reliable transformation. LMMs may generate candidate plans and actions, but a reliability-based System-2 controller is required to validate which transformations have actually become executable capabilities.
Dario Jesus Leon Mori (Fri,) studied this question.