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Information Location under Matched Predictive Opportunity

Year: 2026
Type: preprint
Abstract: Model rankings are difficult to interpret when benchmark tasks differ both in how predictable the target is and in where the predictive signal is located. We construct a finite-support synthetic testbed with a known binary posterior and the same public semantic facts available to every learner. In three core conditions, an identical two-point posterior marginal is determined by either a current warning, dependency topology, or the order of earlier warnings; a fourth topology-history interaction condition matches Bayes Brier opportunity but not the full posterior marginal. Across 2,304 development runs of specified tabular, static-graph, size-control, and graph-plus-GRU systems, rankings are not uniform: the temporal system has better mean size-integrated normalized regret than the static system in both ordered-history conditions, while the topology and interaction contrasts reverse direction across opportunity levels. Large tabular-static gaps also appear in the current-state control, where topology and ordered history do not determine the posterior, so rankings cannot be read as direct measures of structural-information use. Exact expected-risk evaluation removes sampled evaluation-label noise. In selected post-hoc replays, learned content pathways lose prediction-relevant distinctions although the underlying input information remains available; one selected failure is rescued by a persistent learning-rate reduction, whereas a one-step reduction and an analogous intervention in another selected trajectory are not. A planned confirmatory stage was not run because its precision target was infeasible within the replication cap. The results motivate separating predictive opportunity, posterior-determining information location, and stable learnability.
DOI: 10.5281/zenodo.23297060
Language: English
Open Access status: green
OpenAlex ID: W7221456889
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