Adaptive Latent State Reasoning for Task Conditioned Computation

Adaptive Latent-State Reasoning (ALSR) is a research agenda for systems that maintain a revisableinternal state and use it to choose what computation to perform next. Its motivating question iswhether spatial judgment, planning and reasoning under changing evidence benefit from task-relevantstate transformation rather than repeated expansion of a verbal trace. Existing work already implementscontinuous reasoning, writable memory, adaptive depth and latent communication. The remainingscientific question is therefore conditional: when does a particular state representation, updateinterface or controller improve decision quality at a competitive total cost? We propose an evaluationcontract separating retained computation, target selection and representation, specify a relationalcorrection study, and define outcomes that would weaken the latent-specific hypothesis. The proposedcontribution is a synthesis and an experimental agenda. No ALSR implementation, measured efficiencygain or experimental result is reported.