Epistemic Regret Minimization: Gold-Blind Causal Process Validation

A correct causal answer can be supported by an invalid derivation. We separate premise reliability, WHY-to-WHAT support, and answer correctness, and study Epistemic Regret Minimization (ERM), a gold-blind test-time controller over a frozen target model's exposed argument. Our strongest test is a prospectively frozen, disjoint 200-case CLadder L2 replication. Among 69 initial errors, case-specific versus same-critic, same-query-class shuffled critique changes recovery by +29.0 pp for Claude (95% CI [15.4, 41.3], Holm p = 2.64 × 10^-4), +20.3 pp for Gemini ([5.0, 34.4], p = .0313), and -10.1 pp for GPT-4o ([-19.5, -2.5], p = .0313); GPT-4o also reduces binary false flips on 131 correct controls from 9 to 0. Yet every critique arm lowers raw final accuracy from the unrevised 65.5% baseline to 33.5 to 53.0%, mainly through abstention, so this identifies the behavioral relevance of case-specific critique rather than an accuracy gain. On an earlier, label-skewed CausalT5K panel, the baseline-suite ERM arm recovers 81/139 GPT-4 Turbo errors versus 63/139 for outcome-only reprompting (p = .0104); a separate execution of the same cue-free causal condition recovers 68/139. Benchmark gold is absent from critic and repair prompts and enters only after trajectories freeze. Gold-blind judge calibration and complementary stress tests probe evaluator circularity and robustness. Claims concern auditable public arguments, not hidden chain-of-thought or weight-level learning.