# Claim-to-Artifact Manifest

All paths below are **repo-root-relative**.

- Long-form technical report: `docs/paper_combined/main.tex`
- NeurIPS 9-page main-track submission: `docs/paper_neurips/main.tex`
  (when present; see `docs/paper_neurips/` for the packaged submission)

Numeric headline audit: `docs/paper_combined/CLAIM_INVENTORY.md`.

## Section 5: Does Hybridization Help?

| Claim | Artifact (repo-root-relative) | Regeneration |
|-------|-------------------------------|--------------|
| Hybrid vs PINN-only (Heat1D d=-8.3, Wave2D d=-3.6) | `experiments/results/rq3_10seed/results.json`, `experiments/results/rq4_10seed/results.json` | `experiments/scripts/rq3_model_comparison.py`, `experiments/scripts/rq4_wave2d_model_comparison.py` |
| KdV hybrid (d=-0.38), AllenCahn (d=-3.12) | `experiments/results/rq1e_kdv_10seed/results.json`, `experiments/results/rq1f_allencahn_10seed/results.json` | `experiments/scripts/rq1e_kdv_hybrid.py`, `experiments/scripts/rq1f_allencahn_hybrid.py` |
| PINO comparison (2 PDEs) | `experiments/results/rq_pino_heat1d_10seed/results.json`, `experiments/results/rq_pino_advdiff/results.json` | `experiments/scripts/rq_pino_comparison.py`, `experiments/scripts/rq_pino_advdiff.py` |
| OOD evaluation (coefficient, domain, IC/domain shifts) | `experiments/results/rq6_ood/results.json`, `experiments/results/rq6_ood_heat1d/results.json`, `experiments/results/rq6_ood_advdiff/results.json`, `experiments/results/rq6b_ood_domain/results.json`, `experiments/results/rq6c_ood_ic_shift/results.json` | `experiments/scripts/rq6_ood_*.py` |
| HyPINO official native-suite smoke (context only; not same-PDE head-to-head) | `experiments/results/paper_combined/hypino_official_eval/results.txt` | `experiments/scripts/paper_combined/evaluate_hypino_official.py` |
| HyPINO zero-shot Heat1D adapter smoke (compatibility only; not same-budget baseline) | `experiments/results/paper_combined/hypino_heat1d_adapter/results.json` | `experiments/scripts/paper_combined/evaluate_hypino_heat1d_adapter.py` |
| HyPINO target-PINN adaptation probe (Heat1D + AdvDiff1D, 500 Adam steps, 3 seeds) | `experiments/results/paper_combined/hypino_adaptation_2pde/results.json` | `experiments/scripts/paper_combined/finetune_hypino_heat1d.py`, `experiments/scripts/paper_combined/finetune_hypino_advdiff1d.py`, `experiments/scripts/paper_combined/aggregate_external_probes.py` |
| PINNacle executable subset smoke (Burgers1D/Wave1D/Poisson1D/Helmholtz2D/Heat2D-Multiscale, 200 iterations each) | `experiments/results/paper_combined/pinnacle_subset_5task/results.json` | `experiments/scripts/paper_combined/run_pinnacle_subset.py`, `experiments/scripts/paper_combined/aggregate_external_probes.py` |
| No-dominance (13 PDEs) | `experiments/results/paper_combined/routing_evaluation/results.json` | `experiments/scripts/paper_a_routing/run_policy_suite.py` |

## Section 6: Does Causal Loss Help?

| Claim | Artifact (repo-root-relative) | Regeneration |
|-------|-------------------------------|--------------|
| 2x2 stage ablation (9 PDEs, 360 runs) | `experiments/results/paper_combined/stage_ablation/*.json` | `experiments/scripts/paper_c_causal/stage_ablation.py` |
| Statistical tests (BH-FDR, 27 tests) | `experiments/results/paper_combined/statistical_tests.json` | `experiments/scripts/statistical_analysis.py` |
| Gradient flow (cos~1.0, 4 PDEs) | `experiments/results/paper_c/gradient_flow_*/gradient_flow_results.json` | `experiments/scripts/paper_c_causal/gradient_flow_analysis.py` |
| Temporal error (ratio 0.98-672x) | `experiments/results/paper_c/temporal_error_analysis/results.json` | `experiments/scripts/paper_c_causal/temporal_error_analysis.py` |
| Causal weight collapse | `experiments/results/paper_combined/adaptive_epsilon/results.json` | `experiments/scripts/paper_combined/test_adaptive_epsilon.py` |
| Wang-style single-stage causal comparison (4 PDEs) | `experiments/results/paper_combined/wang_comparison/*_results.json` | `experiments/scripts/paper_c_causal/wang_comparison.py` |
| Adaptive epsilon mitigation | `experiments/results/paper_combined/adaptive_epsilon/results.json` | `experiments/scripts/paper_combined/test_adaptive_epsilon.py` |

## Section 7: Physics Weight Tradeoff

| Claim | Artifact (repo-root-relative) | Regeneration |
|-------|-------------------------------|--------------|
| Wave2D lambda sweep (5 values) | `experiments/results/rq5_wave2d_lambda_sweep/results.json` | `experiments/scripts/rq5_wave2d_lambda_sweep.py` |
| Burgers1D lambda sweep | `experiments/results/rq2b/results.json` | `experiments/scripts/rq2b_burgers_lambda_sweep.py` |

## Section 8: Policy Selection from Early Dynamics

| Claim | Artifact (repo-root-relative) | Regeneration |
|-------|-------------------------------|--------------|
| Routing evaluation (13 PDEs, 450 runs) | `experiments/results/paper_combined/routing_evaluation/results.json` | `experiments/scripts/paper_a_routing/evaluate_routing_full.py` |
| Scaling analysis (5→9→13 PDEs) | `experiments/results/paper_combined/routing_evaluation/scaling_ablation.json` | `experiments/scripts/paper_a_routing/scaling_and_ablation.py` |
| Feature ablation (nested CV) | `experiments/results/paper_combined/routing_evaluation/scaling_ablation.json` | Same script (Part 2) |
| Diagnostic bound (33/52 = 63.5% overall; 33/39 = 84.6% for k ∈ {10,50,100}; universal failure at k=20; fails on Burgers1D and Burgers1D-lownu at every defined k) | `experiments/results/paper_combined/routing_evaluation/regret_bound_validation.json` | `experiments/scripts/paper_combined/validate_regret_bound.py` |
| Meta-router (GBR+PDE and RF+PDE-top5: 84.6%, 11/13 held-out PDEs; full RF+PDE: 61.5%) | `experiments/results/paper_combined/meta_router/results.json` | `experiments/scripts/paper_combined/evaluate_meta_router.py` |
| Held-out generalization (3 PDEs) | `experiments/results/paper_combined/routing_evaluation/holdout.json` | `experiments/scripts/paper_a_routing/holdout_validation.py` |
| Family-macro accuracy (LOPO aggregated by 8 families): physics-final 81.2% / GBR 68.8% / RF 54.2% / Ridge 45.8% | `experiments/results/paper_combined/routing_evaluation/results.json` | `experiments/scripts/paper_a_routing/family_holdout_analysis.py` |
| Bandit comparison (SH, LinUCB) | `experiments/results/paper_a/bandit_evaluation/results.json` | `experiments/scripts/paper_a_routing/evaluate_bandits.py` |
| Accuracy-regret dissociation (77x) | `experiments/results/paper_combined/routing_evaluation/results.json` + `experiments/results/rq_early_predictor_l2/results.json` | `experiments/scripts/paper_a_routing/evaluate_routing_full.py` |

## Figures

All figure sources live under `docs/paper_combined/figures/` (repo-root-relative).

| Figure | Regeneration |
|--------|--------------|
| `fig_no_dominance.pdf` | `experiments/scripts/paper_a_routing/generate_figures.py` |
| `fig_routing_accuracy.pdf` | Same |
| `fig_feature_importance.pdf` | Same |
| `fig_budget_savings.pdf` | Same |
| `fig_scaling.pdf` | `experiments/scripts/paper_a_routing/scaling_and_ablation.py` |
| `fig_feature_ablation.pdf` | Same |
| `fig_dissociation.pdf` | `experiments/scripts/paper_a_routing/generate_dissociation_fig.py` |
| `fig_ntk_proxy.pdf` | `experiments/scripts/paper_a_routing/ntk_proxy_analysis.py` |
| `fig_regret_bound.pdf` | `experiments/scripts/paper_combined/validate_regret_bound.py` |
| `fig_stage_ablation_heatmap.pdf` | `experiments/scripts/paper_c_causal/generate_figures.py` |
| `fig_pretrain_effect.pdf` | Same |
| `fig_interaction_plot.pdf` | Same |
| `fig_runtime_tradeoff.pdf` | Same |
| `fig_gradient_alignment.pdf` | `experiments/scripts/paper_c_causal/gradient_flow_analysis.py` |
| `fig_gradient_combined.pdf` | `experiments/scripts/paper_c_causal/generate_combined_gradient_fig.py` |
| `fig_causal_weights.pdf` | `experiments/scripts/paper_c_causal/causal_weight_evolution.py` |
| `fig_temporal_error.pdf` | `experiments/scripts/paper_c_causal/temporal_error_analysis.py` |
| `fig_epsilon_trajectories.pdf` | `experiments/scripts/paper_combined/test_adaptive_epsilon.py` |
| `fig_weight_collapse.pdf` | Same |

## Table generation provenance (hand-curated vs auto-generated)

The paper_combined manuscript contains roughly twenty tables. Their provenance
with respect to the released JSON artifacts is as follows.

- **Auto-generated (from JSON via `experiments/scripts/generate_paper_tables.py` or
  `experiments/scripts/statistical_analysis.py`)**: `tab:stats-core` statistics
  rows, `tab:causal-ablation` per-PDE effect rows, and the raw fragments in
  `experiments/analysis/paper_tables.tex` and `experiments/analysis/statistical_tests.md`.
- **Hand-curated from JSON numeric values (values copy-pasted from the
  corresponding `results.json`; verified in `CLAIM_INVENTORY.md` and by
  `scripts/verify_claims.py`)**: `tab:routing`, `tab:meta_router`,
  `tab:feature-importance`, `tab:scaling`, `tab:bestpolicy`, `tab:lopo-family`,
  `tab:adaptive-epsilon`, OOD taxonomy table, Wang single-stage comparison
  table, HyPINO adaptation table, and the PINNacle subset table.

Hand-curated tables intentionally remain hand-curated for this submission: the
values match the inventory, each row cites a specific artifact path, and a
combined-paper-wide table generator is deferred (out of scope for the reject-risk
reduction pass). Any future numeric change must be reflected simultaneously in
the JSON artifact, the paper table, and `CLAIM_INVENTORY.md`.

## Notes on scope (per `REVISION.md`)

- All experiments are in the **synthetic exact-solution regime**; Stage 1 supervised data comes from analytical solutions. No claim is made about noisy, sparse, or real-measurement regimes.
- External baselines (HyPINO, PINNacle, Wang-style causal, adaptive weighting) are **contextual positioning** except for the internal PINO comparison (Heat1D + AdvDiff1D) and the Wang-style 4-PDE probe, which are direct same-pipeline probes but not broad head-to-head reproductions.
- The routing suite is 13 PDE configurations across 8 families; results are conditioned on that composition.
- `pinn_only` denotes the PINN backbone under the **same supervised-data access** as the hybrid, not a standard data-free PINN. It isolates architecture, not data regime.