bundle/v0/CLAIM_MAPPING.md

Official claim mapping

The scored claim set is pinned in configs/official_claims_lock.json. The live judge merges {**legacy, **anchored}, so the anchored six-claim record replaces the historical three-claim record for OpenReview K1EPPO9t2c. The C1, C2, and C3 labels inside the frozen 14,000-task plan are internal evidence-route labels only. They are not judge-facing claims and changing them would invalidate the active run identity.

Claim	Official anchored claim	Primary evidence	Conservative verdict rule
A1	The ambiguity-set geometry is learned as a bilevel OT-DRO problem.	Validated end-to-end portfolio and absolute-regression main suites, raw upper/lower-level lineage, solver feasibility, and per-iteration geometry.	Verify only when both main suites are complete with no capped or worsening-stop tasks.
A2	Theorem 5.1 gives convergence to a critical point under its stated conditions.	Enumerated source-anchored assumptions, a hash-bound proof artifact, and a separately bound independent-reviewer receipt.	Verification is hard-disabled until that complete contract exists. Finite trajectories and self-declared receipts never verify the theorem.
A3	Algorithm 1 differentiates through nonsmooth conic OT-DRO programs.	Source/formula audit plus nonsmooth and active-set boundary evidence for conservative implicit differentiation without continuous-differentiability assumptions.	The current six smooth finite-difference checks can support at most partial verification. They cannot verify A3.
A4	Portfolio relative improvement increases as sample size decreases.	Exact k=2, J=30, n_b=20, gamma=0.05, beta=0.1 binding and the paper's exact Figure 2 relative-improvement estimand.	The frozen plan does not bind k, J, or the paper formula, so A4 remains inconclusive. Available slope analysis is extension-only sensitivity evidence.
A5	The shaped ambiguity set retains the specified high-probability coverage.	portfolio_gaussian_main coverage at every sample size, aggregated by distribution before bootstrapping.	Verify only when every lower 95% confidence bound reaches the predeclared 1 - beta = 0.9 target.
A6	Ten independent regression trials consistently reduce average loss versus baseline OT-DRO.	regression_absolute_main out-of-sample relative improvement at every sample size and for each of the ten distributions.	Verify only when every sample-size lower confidence bound is positive and every one of the ten distribution means is positive.

The high-dimensional, discrete, Gaussian-mixture, squared-loss, and coverage-penalty-ablation suites remain valuable robustness analyses. They are reported under extensions and cannot create additional scored claims.