Version 3. 1 — April 2026. Reproducibility upgrade. The benchmark runner now computes the formal Discovery Confidence Score (Eq. 5) at write time, storing per-run components (A, S, R) and the composite DCS in each result JSON artifact. Table 1 is regenerated end-to-end from these embedded fields using the companion script aggregateₜable1ᵥ3₁. py. The full reproducibility chain — benchmark runner → per-run JSON → aggregator → Table 1 — is now scripted and available in the PIR repository. All 20 tasks retain 100% discovery rate. Numerical DCS values are at or above their v3 counterparts (mean Δ = +0. 029, max +0. 083, no regressions). Mean DCS across all 20 tasks: 0. 840 (v3: 0. 818). Additional changes in v3. 1: - Accuracy (A) is now scale-invariant: A = max (0, 1 − RMSE/std (y) ) - Simplicity uses sympy. countₒps for canonical complexity measurement- Residual randomness (R) uses genuine autocorrelation over lags 1–5- Fixed a grammar-upgrade regression in planarᵣobotfkₓ/y (ᵢsᵣobotₜask predicate was over-broad, routing FK through the Jacobian compound-angle library) - Jacobian noise robustness sweep (σ ∈ 0. 10, 0. 20) completed: 100% DR confirmed across all four Jacobian tasks Design constants unchanged: α=0. 7, β=0. 3, γ=0. 2, γJEPA=0. 2, Langevin T=500. Supersedes Version 3 (DOI: 10. 5281/zenodo. 19428230).
Muhammad Amar Hanif (Fri,) studied this question.