Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection

A recent paper on arXiv (2608.05085) highlights a fundamental limitation in automated systems for scientific discovery that prioritize short-term scores, such as expected information gain per cost unit or learned plausibility scores. The authors contend that certain actions are beneficial, as they build epistemic capabilities (like instruments, assays, pipelines, simulators, or abstractions) that provide value not through immediate information but by facilitating future actions. When obtaining a confident answer necessitates a sequence of such constructions, a planner that evaluates actions solely based on information available within a limited timeframe fails to recognize the initial construction's value, as it does not yield immediate information and is overshadowed by any measurement that provides even minimal information. The paper presents goal-directed discovery as a planning challenge and offers a method for estimating costs to goals, allowing for the assessment of capability-gating actions. This research is significant at the nexus of AI and scientific discovery, underscoring the importance of long-term planning in automated experimentation.