feat: add initial p1 validation artifacts

baselines/measured_sweep.py

@@ -0,0 +1,214 @@
+"""Measured sweep over the 4-knob parameter space.
+
+Validates ranges, crash zones, feasibility regions, and identifies
+candidate reset seeds for the repaired low-dimensional family.
+
+Usage:
+    uv run python baselines/measured_sweep.py
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from itertools import product
+from pathlib import Path
+
+import numpy as np
+
+from fusion_lab.models import LowDimBoundaryParams
+from server.contract import N_FIELD_PERIODS
+from server.environment import PARAMETER_DELTAS, PARAMETER_RANGES
+from server.physics import build_boundary_from_params, evaluate_boundary
+
+SWEEP_RANGES: dict[str, tuple[float, float]] = PARAMETER_RANGES
+
+
+def linspace_inclusive(low: float, high: float, n: int) -> list[float]:
+    return [round(float(v), 4) for v in np.linspace(low, high, n)]
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Run a measured low-fidelity sweep over the repaired 4-knob family."
+    )
+    parser.add_argument(
+        "--grid-points",
+        type=int,
+        default=3,
+        help="Number of evenly spaced points per parameter range.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("baselines/sweep_results"),
+        help="Directory where the JSON artifact should be written.",
+    )
+    return parser.parse_args()
+
+
+def run_sweep(*, grid_points: int) -> list[dict]:
+    if grid_points < 2:
+        raise ValueError("--grid-points must be at least 2.")
+    grids = {
+        name: linspace_inclusive(lo, hi, grid_points) for name, (lo, hi) in SWEEP_RANGES.items()
+    }
+
+    configs = list(
+        product(
+            grids["aspect_ratio"],
+            grids["elongation"],
+            grids["rotational_transform"],
+            grids["triangularity_scale"],
+        )
+    )
+    total = len(configs)
+    print(f"Sweep: {total} configurations, estimated {total * 0.6:.0f}s")
+
+    results: list[dict] = []
+    t0 = time.monotonic()
+
+    for i, (ar, elong, rt, ts) in enumerate(configs):
+        params = LowDimBoundaryParams(
+            aspect_ratio=ar,
+            elongation=elong,
+            rotational_transform=rt,
+            triangularity_scale=ts,
+        )
+        boundary = build_boundary_from_params(params, n_field_periods=N_FIELD_PERIODS)
+        metrics = evaluate_boundary(boundary, fidelity="low")
+
+        results.append(
+            {
+                "aspect_ratio": ar,
+                "elongation": elong,
+                "rotational_transform": rt,
+                "triangularity_scale": ts,
+                "crashed": metrics.evaluation_failed,
+                "failure_reason": metrics.failure_reason,
+                "feasible": metrics.constraints_satisfied,
+                "p1_feasibility": metrics.p1_feasibility,
+                "p1_score": metrics.p1_score,
+                "max_elongation": metrics.max_elongation,
+                "aspect_ratio_out": metrics.aspect_ratio,
+                "average_triangularity": metrics.average_triangularity,
+                "edge_iota_over_nfp": metrics.edge_iota_over_nfp,
+                "vacuum_well": metrics.vacuum_well,
+            }
+        )
+
+        if (i + 1) % 50 == 0 or i + 1 == total:
+            elapsed = time.monotonic() - t0
+            rate = (i + 1) / elapsed
+            eta = (total - i - 1) / rate
+            print(
+                f"  [{i + 1}/{total}] "
+                f"{elapsed:.1f}s elapsed, {eta:.1f}s remaining, "
+                f"{rate:.1f} eval/s"
+            )
+
+    return results
+
+
+def analyze(results: list[dict]) -> dict:
+    total = len(results)
+    crashed = [r for r in results if r["crashed"]]
+    evaluated = [r for r in results if not r["crashed"]]
+    feasible = [r for r in evaluated if r["feasible"]]
+
+    print(f"\n{'=' * 60}")
+    print("SWEEP SUMMARY")
+    print(f"{'=' * 60}")
+    print(f"Total:     {total}")
+    print(f"Evaluated: {len(evaluated)} ({len(evaluated) / total * 100:.1f}%)")
+    print(f"Crashed:   {len(crashed)} ({len(crashed) / total * 100:.1f}%)")
+    print(f"Feasible:  {len(feasible)} ({len(feasible) / total * 100:.1f}% of evaluated)")
+
+    # Per-parameter breakdown
+    for param in ["aspect_ratio", "elongation", "rotational_transform", "triangularity_scale"]:
+        print(f"\n--- {param} ---")
+        values = sorted(set(r[param] for r in results))
+        for v in values:
+            subset = [r for r in results if r[param] == v]
+            n_crash = sum(1 for r in subset if r["crashed"])
+            n_feas = sum(1 for r in subset if not r["crashed"] and r["feasible"])
+            n_eval = sum(1 for r in subset if not r["crashed"])
+            avg_feas = (
+                np.mean([r["p1_feasibility"] for r in subset if not r["crashed"]])
+                if n_eval > 0
+                else float("nan")
+            )
+            print(
+                f"  {v:.4f}: crash={n_crash}/{len(subset)} "
+                f"feasible={n_feas}/{n_eval} "
+                f"avg_feasibility={avg_feas:.4f}"
+            )
+
+    # Top feasible results
+    if feasible:
+        print("\n--- TOP FEASIBLE (by score) ---")
+        by_score = sorted(feasible, key=lambda r: r["p1_score"], reverse=True)
+        for r in by_score[:10]:
+            print(
+                f"  AR={r['aspect_ratio']:.2f} elong={r['elongation']:.2f} "
+                f"rt={r['rotational_transform']:.2f} ts={r['triangularity_scale']:.2f} | "
+                f"score={r['p1_score']:.6f} feas={r['p1_feasibility']:.6f} "
+                f"elong_out={r['max_elongation']:.4f} tri={r['average_triangularity']:.4f}"
+            )
+
+    # Candidate reset seeds: near-feasible but not yet feasible
+    near_feasible = sorted(
+        [r for r in evaluated if not r["feasible"] and r["p1_feasibility"] < 0.5],
+        key=lambda r: r["p1_feasibility"],
+    )
+    print("\n--- CANDIDATE RESET SEEDS (near-feasible, not yet feasible) ---")
+    for r in near_feasible[:10]:
+        print(
+            f"  AR={r['aspect_ratio']:.2f} elong={r['elongation']:.2f} "
+            f"rt={r['rotational_transform']:.2f} ts={r['triangularity_scale']:.2f} | "
+            f"feas={r['p1_feasibility']:.6f} "
+            f"tri={r['average_triangularity']:.4f} iota={r['edge_iota_over_nfp']:.4f}"
+        )
+
+    # Delta reachability: from each near-feasible seed, can 6 steps reach feasibility?
+    print("\n--- DELTA REACHABILITY ---")
+    for param, deltas in PARAMETER_DELTAS.items():
+        lo, hi = PARAMETER_RANGES[param]
+        span = hi - lo
+        max_reach = deltas["large"] * 6
+        print(
+            f"  {param}: range=[{lo}, {hi}] span={span:.2f} "
+            f"max_6_steps={max_reach:.2f} "
+            f"coverage={max_reach / span * 100:.0f}%"
+        )
+
+    analysis = {
+        "total": total,
+        "evaluated": len(evaluated),
+        "crashed": len(crashed),
+        "feasible": len(feasible),
+        "crash_rate": len(crashed) / total,
+        "feasibility_rate": len(feasible) / max(len(evaluated), 1),
+    }
+    return analysis
+
+
+def main() -> None:
+    args = parse_args()
+    results = run_sweep(grid_points=args.grid_points)
+
+    out_dir = args.output_dir
+    out_dir.mkdir(exist_ok=True)
+    timestamp = time.strftime("%Y%m%dT%H%M%SZ", time.gmtime())
+    out_path = out_dir / f"measured_sweep_{timestamp}.json"
+
+    analysis = analyze(results)
+
+    with open(out_path, "w") as f:
+        json.dump({"analysis": analysis, "results": results}, f, indent=2)
+    print(f"\nResults saved to {out_path}")
+
+
+if __name__ == "__main__":
+    main()

baselines/sweep_results/measured_sweep_20260308T050043Z.json

@@ -0,0 +1,1308 @@
+{
+  "analysis": {
+    "total": 81,
+    "evaluated": 63,
+    "crashed": 18,
+    "feasible": 0,
+    "crash_rate": 0.2222222222222222,
+    "feasibility_rate": 0.0
+  },
+  "results": [
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.2992442990666656,
+      "p1_score": 0.0,
+      "max_elongation": 3.16130576121601,
+      "aspect_ratio_out": 2.9974947988779532,
+      "average_triangularity": -0.3772870380738901,
+      "edge_iota_over_nfp": 0.2102267102800003,
+      "vacuum_well": -0.7692596885396071
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.55,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.4090894053777488,
+      "p1_score": 0.0,
+      "max_elongation": 4.152094100549045,
+      "aspect_ratio_out": 2.921555348457181,
+      "average_triangularity": -0.47193262838245775,
+      "edge_iota_over_nfp": 0.17727317838667536,
+      "vacuum_well": -0.9367956816738985
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.7,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.5042302640839985,
+      "p1_score": 0.0,
+      "max_elongation": 5.535803883650764,
+      "aspect_ratio_out": 2.8456158980364137,
+      "average_triangularity": -0.5427662288172865,
+      "edge_iota_over_nfp": 0.14873092077480043,
+      "vacuum_well": -1.1406175925392996
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.55,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.24542592385222362,
+      "p1_score": 0.0,
+      "max_elongation": 4.121228842521552,
+      "aspect_ratio_out": 2.997494798877953,
+      "average_triangularity": -0.3772870380738882,
+      "edge_iota_over_nfp": 0.3164284175757522,
+      "vacuum_well": -0.8497809691028027
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.55,
+      "triangularity_scale": 0.55,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.12086983493481807,
+      "p1_score": 0.0,
+      "max_elongation": 5.454537868016638,
+      "aspect_ratio_out": 2.921555348457181,
+      "average_triangularity": -0.4719326283824573,
+      "edge_iota_over_nfp": 0.26373904951955457,
+      "vacuum_well": -1.022090883293349
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.55,
+      "triangularity_scale": 0.7,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.28449084880096587,
+      "p1_score": 0.0,
+      "max_elongation": 7.3486347080873395,
+      "aspect_ratio_out": 2.845615898036414,
+      "average_triangularity": -0.5427662288172859,
+      "edge_iota_over_nfp": 0.21465274535971024,
+      "vacuum_well": -1.2227198660107412
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.9,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.24542592385222062,
+      "p1_score": 0.0,
+      "max_elongation": 5.506917831126787,
+      "aspect_ratio_out": 2.9974947988779492,
+      "average_triangularity": -0.3772870380738897,
+      "edge_iota_over_nfp": 0.4111851229739778,
+      "vacuum_well": -0.8487582268420396
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.9,
+      "triangularity_scale": 0.55,
+      "crashed": true,
+      "failure_reason": "Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.",
+      "feasible": false,
+      "p1_feasibility": 1000000.0,
+      "p1_score": 0.0,
+      "max_elongation": 10.0,
+      "aspect_ratio_out": 0.0,
+      "average_triangularity": 0.0,
+      "edge_iota_over_nfp": 0.0,
+      "vacuum_well": 0.0
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.2,
+      "rotational_transform": 1.9,
+      "triangularity_scale": 0.7,
+      "crashed": true,
+      "failure_reason": "Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.",
+      "feasible": false,
+      "p1_feasibility": 1000000.0,
+      "p1_score": 0.0,
+      "max_elongation": 10.0,
+      "aspect_ratio_out": 0.0,
+      "average_triangularity": 0.0,
+      "edge_iota_over_nfp": 0.0,
+      "vacuum_well": 0.0
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.3756331947507288,
+      "p1_score": 0.0,
+      "max_elongation": 3.415282869637771,
+      "aspect_ratio_out": 2.9873706820500727,
+      "average_triangularity": -0.38276544931263445,
+      "edge_iota_over_nfp": 0.18731004157478134,
+      "vacuum_well": -0.7354702188161674
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.55,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.4698235036344714,
+      "p1_score": 0.0,
+      "max_elongation": 4.397190759490295,
+      "aspect_ratio_out": 2.907634687818849,
+      "average_triangularity": -0.47640349211453187,
+      "edge_iota_over_nfp": 0.15905294890965857,
+      "vacuum_well": -0.8791543482410266
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.7,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.5428078225003115,
+      "p1_score": 0.0,
+      "max_elongation": 5.745675404632478,
+      "aspect_ratio_out": 2.827898693587622,
+      "average_triangularity": -0.5463927121513807,
+      "edge_iota_over_nfp": 0.13715765324990656,
+      "vacuum_well": -1.0487526494457182
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.55,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.23446910137473032,
+      "p1_score": 0.0,
+      "max_elongation": 4.409780538549243,
+      "aspect_ratio_out": 2.9873706820500683,
+      "average_triangularity": -0.38276544931263484,
+      "edge_iota_over_nfp": 0.2846591548163929,
+      "vacuum_well": -0.7976954392526402
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.55,
+      "triangularity_scale": 0.55,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.19785195828765914,
+      "p1_score": 0.0,
+      "max_elongation": 5.717017037692497,
+      "aspect_ratio_out": 2.907634687818846,
+      "average_triangularity": -0.47640349211453215,
+      "edge_iota_over_nfp": 0.24064441251370225,
+      "vacuum_well": -0.9370717167513004
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.55,
+      "triangularity_scale": 0.7,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.31745507935684175,
+      "p1_score": 0.0,
+      "max_elongation": 7.524424461976794,
+      "aspect_ratio_out": 2.827898693587622,
+      "average_triangularity": -0.5463927121513805,
+      "edge_iota_over_nfp": 0.20476347619294746,
+      "vacuum_well": -1.0979141176158662
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.9,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.2344691013747291,
+      "p1_score": 0.0,
+      "max_elongation": 5.828745051952865,
+      "aspect_ratio_out": 2.98737068205007,
+      "average_triangularity": -0.38276544931263545,
+      "edge_iota_over_nfp": 0.37697102463283866,
+      "vacuum_well": -0.7940349587136619
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.9,
+      "triangularity_scale": 0.55,
+      "crashed": true,
+      "failure_reason": "Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.",
+      "feasible": false,
+      "p1_feasibility": 1000000.0,
+      "p1_score": 0.0,
+      "max_elongation": 10.0,
+      "aspect_ratio_out": 0.0,
+      "average_triangularity": 0.0,
+      "edge_iota_over_nfp": 0.0,
+      "vacuum_well": 0.0
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.5,
+      "rotational_transform": 1.9,
+      "triangularity_scale": 0.7,
+      "crashed": true,
+      "failure_reason": "Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.",
+      "feasible": false,
+      "p1_feasibility": 1000000.0,
+      "p1_score": 0.0,
+      "max_elongation": 10.0,
+      "aspect_ratio_out": 0.0,
+      "average_triangularity": 0.0,
+      "edge_iota_over_nfp": 0.0,
+      "vacuum_well": 0.0
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.8,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.4,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.41572876893682525,
+      "p1_score": 0.0,
+      "max_elongation": 3.643871627883724,
+      "aspect_ratio_out": 2.9727492396200192,
+      "average_triangularity": -0.3900787346843175,
+      "edge_iota_over_nfp": 0.1752813693189524,
+      "vacuum_well": -0.7062026867261749
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.8,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.55,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.511263141576597,
+      "p1_score": 0.0,
+      "max_elongation": 4.6308076495645185,
+      "aspect_ratio_out": 2.8875302044775286,
+      "average_triangularity": -0.4823961090018443,
+      "edge_iota_over_nfp": 0.1466210575270209,
+      "vacuum_well": -0.8495335820014714
+    },
+    {
+      "aspect_ratio": 3.2,
+      "elongation": 1.8,
+      "rotational_transform": 1.2,
+      "triangularity_scale": 0.7,
+      "crashed": false,
+      "failure_reason": "",
+      "feasible": false,
+      "p1_feasibility": 0.575134593927855,
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+      "crashed": true,
+      "failure_reason": "Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.Thread 0:\n\tFATAL ERROR in thread=0. The solver failed during the first iterations. This may happen if the initial boundary is poorly shaped or if it isn't spectrally condensed enough.",
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+    }
+  ]
+}

docs/P1_MANUAL_PLAYTEST_LOG.md

@@ -0,0 +1,53 @@
+# P1 Manual Playtest Log
+
+Scope:
+
+- initial low-fidelity manual sanity check from the current default reset seed
+- focus: can a human read the observation, choose a plausible move, and see a legible reward change?
+
+Episode A: repair toward feasibility
+
+Start state:
+
+- seed: `0`
+- params: `aspect_ratio=3.6`, `elongation=1.4`, `rotational_transform=1.5`, `triangularity_scale=0.55`
+- low-fidelity feasibility: `0.050653`
+- low-fidelity score: `0.0`
+- constraints satisfied: `false`
+
+Step 1:
+
+- action: increase `rotational_transform` by `medium`
+- expectation: improve iota without changing triangularity much
+- result: feasibility stayed effectively flat at `0.050653`
+- reward: `-0.1`
+- interpretation: legible but weak; this move alone does not solve the boundary issue
+
+Step 2:
+
+- action: increase `triangularity_scale` by `medium`
+- expectation: push the boundary over the triangularity threshold
+- result: low-fidelity feasibility moved to `0.0`
+- result: low-fidelity score moved to `0.291660`
+- constraints satisfied: `true`
+- reward: `+3.1533`
+- interpretation: good reward behavior; the feasibility crossing was clearly positive and easy to understand
+
+Episode B: move the wrong way
+
+Start state:
+
+- same default reset seed
+
+Step 1:
+
+- action: decrease `triangularity_scale` by `medium`
+- expectation: worsen triangularity and move away from feasibility
+- result: feasibility worsened to `0.107113`
+- reward: `-0.3823`
+- interpretation: good negative signal; the environment penalized an obviously bad move without needing a complicated reward term
+
+Current conclusion:
+
+- Reward V0 is legible on the low-fidelity repair path around the default reset seed
+- the most useful next manual check is still a real `submit` trace, but low-fidelity shaping is already understandable by a human

docs/P1_MEASURED_SWEEP_NOTE.md

@@ -0,0 +1,41 @@
+# P1 Measured Sweep Note
+
+Artifact:
+
+- `baselines/sweep_results/measured_sweep_20260308T050043Z.json`
+
+Run shape:
+
+- repaired 4-knob family
+- low-fidelity verifier only
+- `3 x 3 x 3 x 3 = 81` configurations
+
+Summary:
+
+- total configurations: `81`
+- evaluated successfully: `63`
+- crashed: `18`
+- feasible: `0`
+- crash rate: `22.2%`
+
+Most useful result:
+
+- the best coarse near-boundary point was:
+  - `aspect_ratio=3.8`
+  - `elongation=1.8`
+  - `rotational_transform=1.55`
+  - `triangularity_scale=0.55`
+  - `p1_feasibility=0.035208`
+
+What the sweep tells us:
+
+- the repaired family does have a usable near-boundary band
+- coarse global resolution is still too blunt to capture the small low-fidelity feasible pocket seen in targeted local probes
+- `rotational_transform=1.55` performed better than `1.2`, while `1.9` produced a concentrated crash zone
+- `triangularity_scale=0.55` was the best coarse setting overall; `0.7` improved triangularity but also increased crash rate
+
+Actionable conclusion:
+
+- keep the current ranges and deltas for now
+- do not freeze a new default reset seed from the coarse sweep alone
+- use the coarse sweep plus local probes to drive the first tracked fixtures and manual playtesting

server/data/p1/FIXTURE_SANITY.md

@@ -0,0 +1,30 @@
+# P1 Fixture Sanity
+
+This folder now contains three low-fidelity-calibrated `P1` fixtures:
+
+- `boundary_default_reset.json`
+- `bad_low_iota.json`
+- `lowfi_feasible_local.json`
+
+Calibration source:
+
+- coarse measured sweep artifact: `baselines/sweep_results/measured_sweep_20260308T050043Z.json`
+- targeted local probes around the default reset seed
+
+Current interpretation:
+
+- `boundary_default_reset.json`
+  - near-boundary reset reference
+  - infeasible, but close enough that short repair episodes are realistic
+- `bad_low_iota.json`
+  - clearly bad infeasible case
+  - demonstrates that edge iota can be the blocking constraint even when triangularity is already acceptable
+- `lowfi_feasible_local.json`
+  - low-fidelity feasible local target
+  - reachable from the default reset band with two intuitive knob increases
+
+What is still pending:
+
+- paired high-fidelity submit measurements for each tracked fixture
+- low-fi vs high-fi ranking comparison note
+- decision on whether any reset seed should be changed from the current default

server/data/p1/README.md

@@ -13,8 +13,15 @@ These fixtures are for verifier and reward sanity checks.
 ## Status
 
 - [ ] known-good or near-winning fixture added
-- [ ] near-boundary fixture added
-- [ ] clearly infeasible fixture added
-- [ ] fixture sanity note written
+- [x] near-boundary fixture added
+- [x] clearly infeasible fixture added
+- [x] fixture sanity note written
+
+Current tracked files:
+
+- `boundary_default_reset.json`
+- `bad_low_iota.json`
+- `lowfi_feasible_local.json`
+- `FIXTURE_SANITY.md`
 
 Do not copy the old `ai-sci-feasible-designs` harness here. Reuse only the specific JSON artifacts needed for the fresh `P1` environment.

server/data/p1/bad_low_iota.json

@@ -0,0 +1,27 @@
+{
+  "name": "bad_low_iota",
+  "status": "low_fidelity_calibrated",
+  "notes": [
+    "Clearly infeasible calibration case from the coarse measured sweep.",
+    "The dominant failure mode is low edge_iota_over_nfp, not triangularity.",
+    "High-fidelity submit spot check is still pending."
+  ],
+  "params": {
+    "aspect_ratio": 3.2,
+    "elongation": 1.8,
+    "rotational_transform": 1.2,
+    "triangularity_scale": 0.7
+  },
+  "low_fidelity": {
+    "evaluation_failed": false,
+    "constraints_satisfied": false,
+    "p1_score": 0.0,
+    "p1_feasibility": 0.575134593927855,
+    "max_elongation": 5.983792904658967,
+    "aspect_ratio": 2.802311169335037,
+    "average_triangularity": -0.5512332332730122,
+    "edge_iota_over_nfp": 0.12745962182164347,
+    "vacuum_well": -1.0099648537211192
+  },
+  "high_fidelity": null
+}

server/data/p1/boundary_default_reset.json

@@ -0,0 +1,27 @@
+{
+  "name": "boundary_default_reset",
+  "status": "low_fidelity_calibrated",
+  "notes": [
+    "Matches the current default reset seed.",
+    "Useful as a near-boundary starting point for short repair episodes.",
+    "High-fidelity submit spot check is still pending."
+  ],
+  "params": {
+    "aspect_ratio": 3.6,
+    "elongation": 1.4,
+    "rotational_transform": 1.5,
+    "triangularity_scale": 0.55
+  },
+  "low_fidelity": {
+    "evaluation_failed": false,
+    "constraints_satisfied": false,
+    "p1_score": 0.0,
+    "p1_feasibility": 0.0506528382250242,
+    "max_elongation": 6.13677115978351,
+    "aspect_ratio": 3.31313049868072,
+    "average_triangularity": -0.4746735808874879,
+    "edge_iota_over_nfp": 0.2906263991807532,
+    "vacuum_well": -0.7537878932672235
+  },
+  "high_fidelity": null
+}

server/data/p1/lowfi_feasible_local.json

@@ -0,0 +1,27 @@
+{
+  "name": "lowfi_feasible_local",
+  "status": "low_fidelity_calibrated",
+  "notes": [
+    "Local repair target reached from the default reset band by increasing rotational_transform and triangularity_scale.",
+    "Useful as a low-fidelity feasibility reference for Reward V0 sanity checks.",
+    "High-fidelity submit spot check is still pending."
+  ],
+  "params": {
+    "aspect_ratio": 3.6,
+    "elongation": 1.4,
+    "rotational_transform": 1.6,
+    "triangularity_scale": 0.6
+  },
+  "low_fidelity": {
+    "evaluation_failed": false,
+    "constraints_satisfied": true,
+    "p1_score": 0.29165951078327634,
+    "p1_feasibility": 0.0,
+    "max_elongation": 7.375064402950513,
+    "aspect_ratio": 3.2870514531062405,
+    "average_triangularity": -0.5002923204919443,
+    "edge_iota_over_nfp": 0.30046082924426193,
+    "vacuum_well": -0.7949586699110935
+  },
+  "high_fidelity": null
+}