Assigning Measurement Effort to a Value-Aware Evidence Search

Authors

  • Calvin Butler Author
  • Mason Hayes Author
  • Benjamin Chapman Author

Abstract

Value-aware model learning and knowledge-graph path search can emphasize different evidence. Evaluation should retain the paths selected and the state-action regions emphasized by the policy. It then independently checks the terminal action and counts search assessment costs. A favorable global fit or large expansion count is incomplete evidence for a useful value-directed workflow. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.

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Published

2026-09-03

Versions

Issue

Vol. 2 No. 9 (2026): September 2026

Section

Articles