Preprint · August 2026
Causal Observability for Active Reinforcement Learning
When should a reinforcement-learning system acquire additional information before acting?
Contribution
Causal Observability Optimization treats coarse observation, boundary observation, probing, transfer, continuation, and abstention as cost-sensitive meta-actions. It asks which observation regime preserves the intervention-relevant distinctions needed for a declared decision.
Evidence
- A Lean-checked finite result on the sequential control value of observation refinement.
- Typed replay infrastructure for connecting decisions to inspectable experimental traces.
- Controlled partially observable structural causal model validation, with MiniGrid as an external protocol check.
Research infrastructure
CogTrace is the private research infrastructure behind this work: typed event streams, replayable observation views, and claim-evidence manifests for auditable agent experiments.