Information Organization and Evidence Efficiency in Policy Induction

How does information organization affect the amount of evidence foundation models need to induce policies from chronological state-action observations? We investigate whether organization inspired by interfaces designed to support human judgment improves this evidence efficiency. Using public market records and study-defined hidden policies, we construct chronological state-action observations in two text representations: flat tables (FLAT) and panels inspired by professional trading interfaces (PANELS). Models infer four policy settings from supplied candidates within a common rule structure. At each evidence budget, both representations present the same observations in the same chronological sequence. We introduce Evidence-to-Criterion (E2C), the first tested evidence budget at which an induced policy meets a prespecified action-reproduction criterion shared across representations on separate evaluation states. We summarize the cumulative criterion-attainment rate across evidence budgets using the area under the curve (AUC). On this task, FLAT achieves significantly higher E2C AUC than PANELS for both evaluated foundation models. The findings extend input-representation comparisons to evidence efficiency in policy induction and support evaluating information organization alongside content when designing inputs for foundation models.