diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..df36b35f5be74c0a061bdf939fe2fd2dd888e648 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+results/checkpoint_inference.png filter=lfs diff=lfs merge=lfs -text
+results/figure1.png filter=lfs diff=lfs merge=lfs -text
+results/training_loss.png filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..edae21a040e17c101106bb4c7e3ddc5e4970bf7e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,88 @@
+---
+license: other
+language:
+- en
+tags:
+- OneScience
+- darcy-flow
+- neural-operator
+- thompson-sampling
+---
+
+# NOTS-DarcyFlowRate
+
+## Model Introduction
+
+This package reproduces the Darcy flow-rates experiment in [Thompson Sampling in Function Spaces via Neural Operators](https://arxiv.org/abs/2506.21894). A neural operator predicts a 16x16 pressure field from a permeability field, then supports sequential selection using a negative total-flow objective.
+
+## Model Description
+
+The run completed ten independent trials with 300 queries per trial on one DCU. The included `weight/best_model.pt` contains the selected trained model. The public checkpoint changes only the configuration fingerprint for portable data/output paths; its model parameter tensors are identical to the original run.
+
+The published checkpoint SHA256 is `dac0b15574242acabd25d5bc52b7a415ec72e1d55411433402affe61876131f7`; the original training checkpoint SHA256 was `2cf139f9cf75a01064fe54b5e8ba0d4e58a4b76a39aa7cf924320acbee4336b2`. The archived `results/inference_metrics.json` records the original file hash; rerunning inference writes a fresh metric file for the published checkpoint.
+
+## Intended Use
+
+| Task | Description |
+| --- | --- |
+| Darcy surrogate inference | Predict pressure fields on 16x16 permeability grids |
+| Sequential optimization | Study neural-operator Thompson sampling for flow-rate objectives |
+
+## Usage
+
+### Download the Model Package
+
+```bash
+hf download OneScience-Group/NOTS-DarcyFlowRate --local-dir ./NOTS-DarcyFlowRate
+cd NOTS-DarcyFlowRate
+```
+
+### Install Dependencies
+
+Use an allocated GPU/DCU compute node for the provided scripts. The tested formal run used a PyTorch DCU environment; CPU execution of the full pipeline is not claimed.
+
+```bash
+pip install -r requirements.txt
+pip install 'modelscope[datasets]'
+```
+
+### Training Data
+
+Hugging Face hosts only this model package, not a dataset mirror. Download the author's original 16x16 Darcy train/test tensors from [OneScience/NOTS-Darcy-16 on ModelScope](https://www.modelscope.cn/datasets/OneScience/NOTS-Darcy-16):
+
+```bash
+modelscope download --dataset OneScience/NOTS-Darcy-16 --local_dir ./data
+```
+
+The package expects `data/darcy_train_16.pt` with 1,000 pairs and `data/darcy_test_16.pt` with 50 pairs. The original files are from the [NOTS authors' repository](https://github.com/csiro-funml/nots/tree/main/data).
+
+### Train
+
+From the package root on an allocated GPU/DCU compute node:
+
+```bash
+python scripts/train.py
+```
+
+This runs ten configured seeds and 300 queries per seed. Save a copy of the bundled checkpoint and results before retraining.
+
+### Inference and Results
+
+```bash
+python scripts/inference.py
+python scripts/result.py
+```
+
+The reproduced final average regret was `0.289031 +/- 0.016963` across ten trials, versus `0.012 +/- 0.001` in paper Table 1. The measured test pressure-field mean relative L2 was `0.339917`. The regret is far from the paper result; this package is a documented reproduction attempt, not a paper-accuracy claim.
+
+## OneScience Repositories
+
+| Platform | Main repository | Skills repository |
+| --- | --- | --- |
+| Gitee | [OneScience](https://gitee.com/onescience-ai/onescience) | [OneSkills](https://gitee.com/onescience-ai/oneskills) |
+| GitHub | [OneScience](https://github.com/onescience-ai/OneScience) | [OneSkills](https://github.com/onescience-ai/oneskills) |
+
+## Citation and License
+
+- Paper: [Thompson Sampling in Function Spaces via Neural Operators](https://arxiv.org/abs/2506.21894).
+- Dataset: [NOTS authors' data directory](https://github.com/csiro-funml/nots/tree/main/data), originally from NeuralOperator and redistributed there under the accompanying MIT license. The data is hosted separately on ModelScope.
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..011b9f7b9f52890d4b6a056708ffdc32db897779
--- /dev/null
+++ b/config.json
@@ -0,0 +1,11 @@
+{
+  "model_type": "nots_darcy_fno",
+  "architectures": ["NOTS"],
+  "framework": "pytorch",
+  "experiment": "darcy_flow_rates_16x16",
+  "input_channels": 1,
+  "output_channels": 1,
+  "resolution": [16, 16],
+  "checkpoint": "weight/best_model.pt",
+  "dataset": "OneScience/NOTS-Darcy-16"
+}
diff --git a/config/config.yaml b/config/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..eeade35be88a2b7b1dbc1045dfa8ae45add1a468
--- /dev/null
+++ b/config/config.yaml
@@ -0,0 +1,60 @@
+paper:
+  title: Thompson Sampling in Function Spaces via Neural Operators
+  arxiv: '2506.21894'
+  target: Figure 1 and Table 1 Darcy negative total flow rates NOTS
+paths:
+  data_dir: data
+  output_dir: .
+  train_file: darcy_train_16.pt
+  test_file: darcy_test_16.pt
+model:
+  n_modes: [16, 16]
+  hidden_channels: 64
+  in_channels: 1
+  out_channels: 1
+  n_layers: 4
+  lifting_channel_ratio: 2
+  projection_channel_ratio: 2
+  positional_embedding: grid
+  channel_mlp_expansion: 0.5
+  channel_mlp_dropout: 0.0
+  channel_mlp_skip: soft-gating
+  fno_skip: linear
+  norm: null
+  domain_padding: null
+  factorization: null
+  fno_block_precision: full
+training:
+  epochs: 10
+  batch_size: 32
+  lr: 0.001
+  lambda_reg: 0.0001
+  optimizer: AdamW
+  weight_decay: 0.0
+  scheduler: CosineAnnealingLR
+  normalization: none
+  shuffle: false
+  precision: float32
+experiment:
+  iterations: 300
+  n_init: 1
+  seeds: [42, 43, 44, 45, 46, 47, 48, 49, 50, 51]
+  pool_size: 1000
+  resolution: 16
+  noise_variance_fraction: 0.01
+  shrink_pool: false
+  prediction_batch_size: 64
+  run_random_baseline: true
+  std_ddof: 0
+  objective_sign: appendix_D1_negative_physical_outflow
+runtime:
+  require_case_slurm_job: false
+  device: cuda
+  num_threads: 8
+reference:
+  NOTS: {mean: 0.012, std: 0.001}
+  RS: {mean: 0.872, std: 0.022}
+  BO: {mean: 0.703, std: 0.045}
+  BFO: {mean: 0.788, std: 0.066}
+  GP-TS: {mean: 0.674, std: 0.050}
+  STO-NTS: {mean: 0.068, std: 0.002}
diff --git a/model/NOTS.py b/model/NOTS.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3cc716fcaa9c37b54b47d2e733f75ab997b4f5c
--- /dev/null
+++ b/model/NOTS.py
@@ -0,0 +1,42 @@
+"""Independent NOTS experiment model; the general FNO is neuraloperator 1.0.2.
+
+No source from the authors' NOTS repository is included here. Paper Algorithm 2
+requires a newly initialized, fully trained FNO at every posterior draw.
+"""
+import math
+
+import torch
+from torch import nn
+from neuralop.models import FNO
+
+
+class NOTS(nn.Module):
+    """Scalar permeability [B,1,H,W] -> pressure [B,1,H,W]."""
+
+    def __init__(self, **model_config):
+        super().__init__()
+        self.model_config = dict(model_config)
+        self.fno = FNO(**model_config)
+        self.reset_posterior_parameters()
+
+    @torch.no_grad()
+    def reset_posterior_parameters(self):
+        """He Gaussian weights, zero biases, including complex spectral weights.
+
+        Complex normal_ uses the requested total variance across real/imaginary
+        parts, and the regularizer uses the modulus squared of those weights.
+        """
+        for name, parameter in self.named_parameters():
+            if 'bias' in name:
+                parameter.zero_()
+            else:
+                fan_in = parameter.shape[1] * math.prod(parameter.shape[2:]) if parameter.ndim > 1 else parameter.numel()
+                parameter.normal_(0.0, math.sqrt(2.0 / max(1, fan_in)))
+
+    def forward(self, permeability):
+        if permeability.ndim != 4 or permeability.shape[1] != 1:
+            raise ValueError('NOTS expects permeability [batch,1,height,width]')
+        return self.fno(permeability)
+
+    def l2_penalty(self):
+        return sum(p.abs().square().sum() for p in self.parameters() if p.requires_grad)
diff --git a/model/__init__.py b/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad54877f5cb51baec4c31f91bb53029da515c6fa
--- /dev/null
+++ b/model/__init__.py
@@ -0,0 +1 @@
+from .NOTS import NOTS
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7794e408bdcf09d7c471e76a7b6b3a1ee3fa61a9
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,9 @@
+# Torch is supplied by the site's DTK/DCU conda environment.
+neuraloperator==1.0.2
+tensorly==0.9.0
+tensorly-torch==0.5.0
+torch-harmonics==0.7.3
+configmypy==0.2.0
+numpy>=1.25,<2
+matplotlib
+PyYAML
diff --git a/results/checkpoint_inference.pdf b/results/checkpoint_inference.pdf
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+oid sha256:c2d9db3b8a7fb5bae20d2e9b3148a8620093e23ed0f2a3a97c02dd83fa411d3c
+size 113985
diff --git a/results/figure1.pdf b/results/figure1.pdf
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diff --git a/results/inference_metrics.json b/results/inference_metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..55af580e866888a880a9bb6215f82b4a4aef36d6
--- /dev/null
+++ b/results/inference_metrics.json
@@ -0,0 +1,79 @@
+{
+  "checkpoint": "weight/best_model.pt",
+  "checkpoint_sha256": "2cf139f9cf75a01064fe54b5e8ba0d4e58a4b76a39aa7cf924320acbee4336b2",
+  "checkpoint_sha256_scope": "original trained file before portability-only config fingerprint update",
+  "selected_seed": 49,
+  "selection_train_mse": 0.00597818839958679,
+  "splits": {
+    "test": {
+      "n": 50,
+      "mse": 0.030603112652897835,
+      "relative_l2_mean": 0.33991682529449463,
+      "functional_mae": 1.4257889986038208,
+      "predicted_best_index": 5,
+      "true_best_index": 5,
+      "prediction_shape": [
+        50,
+        1,
+        16,
+        16
+      ],
+      "all_finite": true,
+      "data": {
+        "path": "data/darcy_test_16.pt",
+        "sha256": "d2b19af9d4a6ae04ba2de54b8ecccc8b89766472faf6ce7f506c0b2a60f69a4c",
+        "n": 50,
+        "shape": [
+          50,
+          1,
+          16,
+          16
+        ],
+        "x_range": [
+          0.0,
+          1.0
+        ],
+        "y_range": [
+          -0.11912135034799576,
+          1.9015082120895386
+        ],
+        "dtype": "float32"
+      }
+    },
+    "train": {
+      "n": 1000,
+      "mse": 0.0304754339158535,
+      "relative_l2_mean": 0.34694087505340576,
+      "functional_mae": 1.5155248641967773,
+      "predicted_best_index": 899,
+      "true_best_index": 233,
+      "prediction_shape": [
+        1000,
+        1,
+        16,
+        16
+      ],
+      "all_finite": true,
+      "data": {
+        "path": "data/darcy_train_16.pt",
+        "sha256": "0be662acdb1dab7dfd1a1da0b140e97f75cc94f98a14edcf768abe6968b3b418",
+        "n": 1000,
+        "shape": [
+          1000,
+          1,
+          16,
+          16
+        ],
+        "x_range": [
+          0.0,
+          1.0
+        ],
+        "y_range": [
+          -0.42788004875183105,
+          2.057187795639038
+        ],
+        "dtype": "float32"
+      }
+    }
+  }
+}
diff --git a/results/inference_test.pt b/results/inference_test.pt
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index 0000000000000000000000000000000000000000..6ede6edbf14ea4f6fe4567afe8e60b3b344dc810
--- /dev/null
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+version https://git-lfs.github.com/spec/v1
+oid sha256:572177c9552f34328dc97338764ae05d4cb0de18ce60bc54220e6f60a4458a9e
+size 156454
diff --git a/results/inference_train.pt b/results/inference_train.pt
new file mode 100644
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--- /dev/null
+++ b/results/inference_train.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed0c7c632c632d4334c0a1904761e16ab0bb20fb8b5dc22f63671c0fc0c7c233
+size 3082415
diff --git a/results/metrics.json b/results/metrics.json
new file mode 100644
index 0000000000000000000000000000000000000000..e101e7279b82f0cd7976e04900b741a59746400c
--- /dev/null
+++ b/results/metrics.json
@@ -0,0 +1,90 @@
+{
+  "metric_definition": "mean_trials(sum_t(true_max - true_selected_t)/300)",
+  "std_ddof": 0,
+  "seeds": [
+    42,
+    43,
+    44,
+    45,
+    46,
+    47,
+    48,
+    49,
+    50,
+    51
+  ],
+  "methods": {
+    "NOTS": {
+      "final_average_regret_mean": 0.28903131441275276,
+      "final_average_regret_std": 0.016963417745350852,
+      "final_average_regret_sample_std": 0.01788101232540895,
+      "per_trial_final_average_regret": [
+        0.28379767219225566,
+        0.3056891644001007,
+        0.25527911305427553,
+        0.30833554108937583,
+        0.2839254943529765,
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+        0.26924508810043335,
+        0.27884589513142904,
+        0.2957803841431936,
+        0.3035893038908641
+      ],
+      "trials": 10,
+      "iterations": 300,
+      "provenance": "measured"
+    },
+    "RS": {
+      "final_average_regret_mean": 0.8908092208305994,
+      "final_average_regret_std": 0.02005791880673843,
+      "final_average_regret_sample_std": 0.021142902850673377,
+      "per_trial_final_average_regret": [
+        0.8716944662729899,
+        0.8952345609664917,
+        0.8659119852383932,
+        0.8786636328697205,
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+        0.9036471736431122,
+        0.9209505971272787,
+        0.9293052395184834,
+        0.8815607929229736
+      ],
+      "trials": 10,
+      "iterations": 300,
+      "provenance": "measured"
+    }
+  },
+  "published_references": {
+    "NOTS": {
+      "mean": 0.012,
+      "std": 0.001
+    },
+    "RS": {
+      "mean": 0.872,
+      "std": 0.022
+    },
+    "BO": {
+      "mean": 0.703,
+      "std": 0.045
+    },
+    "BFO": {
+      "mean": 0.788,
+      "std": 0.066
+    },
+    "GP-TS": {
+      "mean": 0.674,
+      "std": 0.05
+    },
+    "STO-NTS": {
+      "mean": 0.068,
+      "std": 0.002
+    }
+  },
+  "unrerun_baselines": [
+    "BO",
+    "BFO",
+    "GP-TS",
+    "STO-NTS"
+  ]
+}
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..86195bd86daff566aed943dab106b1e785938f86
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diff --git a/results/oracle.pt b/results/oracle.pt
new file mode 100644
index 0000000000000000000000000000000000000000..9c9a5007eea5967b2a3563506ce177411c9be910
--- /dev/null
+++ b/results/oracle.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5771f6d177e1b798d885d5b634bba36c0511b97ecb81194de12526799fa8634
+size 6564
diff --git a/results/report.md b/results/report.md
new file mode 100644
index 0000000000000000000000000000000000000000..bc651ccdd37f11081afd90eeba6a2eb769c458f7
--- /dev/null
+++ b/results/report.md
@@ -0,0 +1,17 @@
+# Darcy flow rates reproduction results
+
+NOTS measured R300/300 = 0.28903131 ± 0.016963418 (population std, ten trials).
+
+Paper Table 1 reference: 0.012 ± 0.001. This is a literature value, not measured data.
+
+All measured trajectories contain 300 queries including one random initial query. Training and acquisition use the same physical pressure scale. The paper main-text flow sign conflicts with Appendix D.1; the latter is used. Published BO/BFO/GP-TS/STO-NTS entries are reference-only, not reruns.
+
+## Verified execution
+
+Case job 975454: COMPLETED, ExitCode 0:0, elapsed 00:53:35, node a01r3n10; stderr empty. All 10 trials, 3000 oracle queries, checkpoints, strict checkpoint inference, and six PNG/PDF figures passed the completion audit. Independent CSV reaggregation agrees with metrics.json.
+
+The measured NOTS result does **not** reproduce the paper numerical value. Repairs addressed local scripts-package shadowing and neuraloperator state-dictionary metadata serialization; the prior scientific protocol was preserved.
+
+Test-set checkpoint inference: MSE 0.03060311265; mean relative L2 0.3399168253; flow-functional MAE 1.425788999. Best checkpoint: seed49, SHA256 2cf139f9cf75a01064fe54b5e8ba0d4e58a4b76a39aa7cf924320acbee4336b2.
+
+See scheduler_status.json and completion_audit.json for actual execution evidence.
diff --git a/results/rs_42.csv b/results/rs_42.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ba926ef80ed454f9c277c6436fd0ad22b9c182a7
--- /dev/null
+++ b/results/rs_42.csv
@@ -0,0 +1,301 @@
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diff --git a/results/rs_43.csv b/results/rs_43.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fd322ba7bfc7829fe31a4de60c4cec43af1f2ee5
--- /dev/null
+++ b/results/rs_43.csv
@@ -0,0 +1,301 @@
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diff --git a/results/rs_44.csv b/results/rs_44.csv
new file mode 100644
index 0000000000000000000000000000000000000000..83ccd6dc2312295d718a295a0b8571937a7d560d
--- /dev/null
+++ b/results/rs_44.csv
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diff --git a/results/rs_45.csv b/results/rs_45.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f14161766bc12945e80af86c3eab503c2df97629
--- /dev/null
+++ b/results/rs_45.csv
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diff --git a/results/rs_46.csv b/results/rs_46.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9d34c1815ee60525180a874d4559688e648b3f58
--- /dev/null
+++ b/results/rs_46.csv
@@ -0,0 +1,301 @@
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diff --git a/results/rs_47.csv b/results/rs_47.csv
new file mode 100644
index 0000000000000000000000000000000000000000..58f7da1181d01f4aba36391ea9c817885b0e7752
--- /dev/null
+++ b/results/rs_47.csv
@@ -0,0 +1,301 @@
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diff --git a/results/rs_48.csv b/results/rs_48.csv
new file mode 100644
index 0000000000000000000000000000000000000000..259d0f56211921862d73fe3ee4f83197a9682faa
--- /dev/null
+++ b/results/rs_48.csv
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diff --git a/results/rs_49.csv b/results/rs_49.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7a818b6c416f2e78f3e8863dea407b1819d67ff1
--- /dev/null
+++ b/results/rs_49.csv
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diff --git a/results/rs_50.csv b/results/rs_50.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2449c65ef320787771e0d12fde5ecd4c51ee43c1
--- /dev/null
+++ b/results/rs_50.csv
@@ -0,0 +1,301 @@
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diff --git a/results/rs_51.csv b/results/rs_51.csv
new file mode 100644
index 0000000000000000000000000000000000000000..619e1d7da3b7076abbe3761d3029007720a1ce34
--- /dev/null
+++ b/results/rs_51.csv
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diff --git a/results/rs_curves.csv b/results/rs_curves.csv
new file mode 100644
index 0000000000000000000000000000000000000000..35ee3bee7b3a4e87be941617174780fcf6095c24
--- /dev/null
+++ b/results/rs_curves.csv
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diff --git a/results/trial_42/trajectory.csv b/results/trial_42/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e0b07824076f5e13c48bd563a691dd2642b0c581
--- /dev/null
+++ b/results/trial_42/trajectory.csv
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diff --git a/results/trial_43/training_loss.jsonl b/results/trial_43/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d06756bd178133e322b32cbfc23a27f8cdd206e1
--- /dev/null
+++ b/results/trial_43/training_loss.jsonl
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diff --git a/results/trial_43/trajectory.csv b/results/trial_43/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d53b91306e55fa69fd899d0ffffee3ba5405de7a
--- /dev/null
+++ b/results/trial_43/trajectory.csv
@@ -0,0 +1,301 @@
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diff --git a/results/trial_44/training_loss.jsonl b/results/trial_44/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ea4a0b4f2a47d1324f4c06e9546d4941d4a0c8fd
--- /dev/null
+++ b/results/trial_44/training_loss.jsonl
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diff --git a/results/trial_44/trajectory.csv b/results/trial_44/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e7b6617638f06a7d34abe8e6cecabc4b662054c5
--- /dev/null
+++ b/results/trial_44/trajectory.csv
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diff --git a/results/trial_45/training_loss.jsonl b/results/trial_45/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a1da6f3fb212d4fb15c177b8f54f9d3b1c1598e
--- /dev/null
+++ b/results/trial_45/training_loss.jsonl
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diff --git a/results/trial_45/trajectory.csv b/results/trial_45/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d4390208e0de2f4729f8f93990ee29c98db6b767
--- /dev/null
+++ b/results/trial_45/trajectory.csv
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diff --git a/results/trial_46/training_loss.jsonl b/results/trial_46/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..056761bb06c4cddb9681282ac553a26f7eb3e71b
--- /dev/null
+++ b/results/trial_46/training_loss.jsonl
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diff --git a/results/trial_46/trajectory.csv b/results/trial_46/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4b64510c440345aa7fc4177e75998dabb3fbdb53
--- /dev/null
+++ b/results/trial_46/trajectory.csv
@@ -0,0 +1,301 @@
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diff --git a/results/trial_47/training_loss.jsonl b/results/trial_47/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..841d7d923a00b0b5e967d2846e2e4ad597417233
--- /dev/null
+++ b/results/trial_47/training_loss.jsonl
@@ -0,0 +1,2990 @@
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diff --git a/results/trial_47/trajectory.csv b/results/trial_47/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bd1fb162f01ab2a5db0e319356481ecf96dff820
--- /dev/null
+++ b/results/trial_47/trajectory.csv
@@ -0,0 +1,301 @@
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diff --git a/results/trial_48/training_loss.jsonl b/results/trial_48/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..320119a81d00e44dec8d758951edc1467c008680
--- /dev/null
+++ b/results/trial_48/training_loss.jsonl
@@ -0,0 +1,2990 @@
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diff --git a/results/trial_48/trajectory.csv b/results/trial_48/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9d50f981e9808e7ddf1e34381060ccda07d488d0
--- /dev/null
+++ b/results/trial_48/trajectory.csv
@@ -0,0 +1,301 @@
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diff --git a/results/trial_49/training_loss.jsonl b/results/trial_49/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..754f63d6eee44007aec12e98d5b956fd60582669
--- /dev/null
+++ b/results/trial_49/training_loss.jsonl
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diff --git a/results/trial_49/trajectory.csv b/results/trial_49/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5accedbd4d5239b1dd2273d9641be99e00d61d26
--- /dev/null
+++ b/results/trial_49/trajectory.csv
@@ -0,0 +1,301 @@
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diff --git a/results/trial_50/training_loss.jsonl b/results/trial_50/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5cd37ad547d32b59212219085a10449d8f90f6d
--- /dev/null
+++ b/results/trial_50/training_loss.jsonl
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diff --git a/results/trial_50/trajectory.csv b/results/trial_50/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..44a7704203af6c258ef2dd71f24fbce50a9a6798
--- /dev/null
+++ b/results/trial_50/trajectory.csv
@@ -0,0 +1,301 @@
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diff --git a/results/trial_51/training_loss.jsonl b/results/trial_51/training_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8398ef5faa2c140e42d48b3c622b614b836882eb
--- /dev/null
+++ b/results/trial_51/training_loss.jsonl
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diff --git a/results/trial_51/trajectory.csv b/results/trial_51/trajectory.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e53678fff3e0758e7aaa3ecca89b4eee3c980c12
--- /dev/null
+++ b/results/trial_51/trajectory.csv
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diff --git a/scripts/__init__.py b/scripts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18ef9a2a1ffeae7829c17c0ffd6b965304e547eb
--- /dev/null
+++ b/scripts/__init__.py
@@ -0,0 +1 @@
+"""Case-owned entrypoints; keep imports local to NOTS-DarcyFlowRate."""
diff --git a/scripts/common.py b/scripts/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a4c1c42e4ae329eeebd7f997ea7de7362ba78cb
--- /dev/null
+++ b/scripts/common.py
@@ -0,0 +1,183 @@
+"""Data, functional, provenance and computation boundary shared by entrypoints."""
+import argparse
+import csv
+import hashlib
+import json
+import os
+import random
+import socket
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+if (ROOT / '.deps').exists():
+    sys.path.insert(0, str(ROOT / '.deps'))
+sys.path.insert(0, str(ROOT))
+
+import numpy as np
+import torch
+import yaml
+
+
+def parse_args(description):
+    p = argparse.ArgumentParser(description=description)
+    p.add_argument('--config', type=Path, default=ROOT / 'config/config.yaml')
+    return p
+
+
+def load_config(path):
+    with open(path) as f:
+        cfg = yaml.safe_load(f)
+    return cfg
+
+
+def save_json(path, value):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + '.tmp')
+    temporary.write_text(json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False))
+    temporary.replace(path)
+
+
+def save_torch(path, value):
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_suffix(path.suffix + '.tmp')
+    torch.save(value, temporary)
+    temporary.replace(path)
+
+
+
+def model_state_on_cpu(model):
+    """Export PyTorch state while handling neuraloperator 1.0.2 metadata.
+
+    BaseModel.state_dict inserts an unprefixed dictionary key '_metadata' even
+    when nested in NOTS. That entry is not a parameter/buffer and must not be
+    passed to the enclosing nn.Module's strict loader. The model architecture
+    is saved separately as checkpoint.model_config. OrderedDict._metadata (the
+    PyTorch version information attribute) is preserved.
+    """
+    state = model.state_dict()
+    metadata = state.pop('_metadata', None)
+    if metadata is not None and not isinstance(metadata, dict):
+        raise TypeError('Unexpected neuraloperator initialization metadata')
+    for name, value in state.items():
+        if not torch.is_tensor(value):
+            raise TypeError(f'Unexpected non-tensor model state: {name}')
+        state[name] = value.detach().cpu()
+    return state
+
+
+def write_csv(path, rows):
+    path = Path(path)
+    if not rows:
+        return
+    temporary = path.with_suffix('.csv.tmp')
+    with temporary.open('w', newline='') as f:
+        w = csv.DictWriter(f, fieldnames=list(rows[0]))
+        w.writeheader()
+        w.writerows(rows)
+    temporary.replace(path)
+
+
+def sha256(path):
+    h = hashlib.sha256()
+    with open(path, 'rb') as f:
+        for chunk in iter(lambda: f.read(1024 * 1024), b''):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def config_hash(cfg):
+    return hashlib.sha256(json.dumps(cfg, sort_keys=True).encode()).hexdigest()
+
+
+def case_guard(cfg):
+    """Refuse login-node execution and attachment to any pre-existing job."""
+    if cfg['runtime']['require_case_slurm_job']:
+        actual = os.environ.get('SLURM_JOB_ID', '')
+        registry = Path(cfg['paths']['output_dir']) / '.onescience/jobs.json'
+        if not actual or not registry.is_file():
+            raise RuntimeError('Compute requires a newly submitted case-owned SLURM job')
+        jobs = json.loads(registry.read_text())['jobs']
+        if actual not in [str(j['job_id']) for j in jobs]:
+            raise RuntimeError(f'Refusing unregistered job {actual}')
+        if not os.environ.get('SLURM_JOB_NODELIST'):
+            raise RuntimeError('Missing allocated compute node list')
+    torch.set_num_threads(cfg['runtime']['num_threads'])
+    device = torch.device(cfg['runtime']['device'])
+    if device.type == 'cuda' and not torch.cuda.is_available():
+        raise RuntimeError('Allocated accelerator is unavailable; CPU fallback is disabled')
+    return device
+
+
+def seed_all(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def rng_state():
+    return {'python': random.getstate(), 'numpy': np.random.get_state(),
+            'torch': torch.get_rng_state(), 'cuda': torch.cuda.get_rng_state_all()}
+
+
+def restore_rng(state):
+    random.setstate(state['python'])
+    np.random.set_state(state['numpy'])
+    torch.set_rng_state(state['torch'])
+    torch.cuda.set_rng_state_all(state['cuda'])
+
+
+def load_data(cfg, split='train'):
+    path = Path(cfg['paths']['data_dir']) / cfg['paths'][split + '_file']
+    raw = torch.load(path, map_location='cpu', weights_only=True)
+    out = []
+    for key in ('x', 'y'):
+        a = raw[key].float()
+        if a.ndim == 3:
+            a = a.unsqueeze(1)
+        if a.ndim != 4 or tuple(a.shape[1:]) != (1, 16, 16):
+            raise ValueError(f'{path}:{key} invalid shape {a.shape}')
+        if not torch.isfinite(a).all():
+            raise ValueError(f'{path}:{key} contains nonfinite data')
+        out.append(a.contiguous())
+    if out[0].shape != out[1].shape:
+        raise ValueError('Input and pressure pairing mismatch')
+    if split == 'train' and len(out[0]) != cfg['experiment']['pool_size']:
+        raise ValueError('The required 1000-candidate official pool is unavailable')
+    meta = {'path': str(path), 'sha256': sha256(path), 'n': len(out[0]),
+            'shape': list(out[0].shape), 'x_range': [out[0].min().item(), out[0].max().item()],
+            'y_range': [out[1].min().item(), out[1].max().item()], 'dtype': 'float32'}
+    return out[0], out[1], meta
+
+
+def negative_total_flow_rates(u, a):
+    """Appendix D.1: integral(a grad(u).n), maximize negative physical outflow.
+
+    First-order one-sided normal differences and rectangular edge quadrature.
+    Equal 16x16 spacings cancel analytically; corners count once per edge.
+    This deliberately uses original permeability values, not a display mask.
+    """
+    if u.shape != a.shape or u.ndim != 4:
+        raise ValueError('Functional requires matching [B,1,H,W] tensors')
+    h, w = u.shape[-2:]
+    left = -(a[..., :, 0] * (u[..., :, 1] - u[..., :, 0])).sum(-1) * (w / h)
+    right = (a[..., :, -1] * (u[..., :, -1] - u[..., :, -2])).sum(-1) * (w / h)
+    bottom = -(a[..., 0, :] * (u[..., 1, :] - u[..., 0, :])).sum(-1) * (h / w)
+    top = (a[..., -1, :] * (u[..., -1, :] - u[..., -2, :])).sum(-1) * (h / w)
+    return (left + right + bottom + top).mean(-1)
+
+
+@torch.no_grad()
+def predict(model, xs, batch_size):
+    model.eval()
+    return torch.cat([model(xs[i:i + batch_size]) for i in range(0, len(xs), batch_size)])
+
+
+def hardware_record():
+    return {'hostname': socket.gethostname(), 'job_id': os.environ.get('SLURM_JOB_ID'),
+            'node_list': os.environ.get('SLURM_JOB_NODELIST'), 'torch': torch.__version__,
+            'accelerator': torch.cuda.get_device_name(), 'device_count': torch.cuda.device_count(),
+            'device_memory_bytes': torch.cuda.get_device_properties(0).total_memory}
diff --git a/scripts/inference.py b/scripts/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..290a971a11003c8ff1f44d8ebd86cb32ac672340
--- /dev/null
+++ b/scripts/inference.py
@@ -0,0 +1,57 @@
+"""Load the saved best FNO checkpoint and evaluate untouched official test data."""
+from pathlib import Path
+
+from common import (case_guard, config_hash, load_config, load_data,
+                    negative_total_flow_rates, parse_args, predict, save_json,
+                    save_torch, sha256)
+import torch
+from model.NOTS import NOTS
+
+
+def main():
+    parser = parse_args(__doc__)
+    parser.add_argument('--checkpoint', type=Path)
+    args = parser.parse_args()
+    cfg = load_config(args.config)
+    device = case_guard(cfg)
+    out = Path(cfg['paths']['output_dir'])
+    checkpoint_path = args.checkpoint or out / 'weight/best_model.pt'
+    state = torch.load(checkpoint_path, weights_only=False, map_location='cpu')
+    if state['config_hash'] != config_hash(cfg):
+        raise ValueError('Checkpoint/config protocol mismatch')
+    if state['normalization']['kind'] != 'identity':
+        raise ValueError('Unexpected checkpoint prediction scale')
+    model = NOTS(**state['model_config']).to(device)
+    model.load_state_dict(state['model_state'], strict=True)
+    model.eval()
+    metrics = {'checkpoint': str(checkpoint_path), 'checkpoint_sha256': sha256(checkpoint_path),
+               'selected_seed': state['seed'], 'selection_train_mse': state['train_mse'], 'splits': {}}
+    for split in ('test', 'train'):
+        x, y, meta = load_data(cfg, split)
+        if split == 'train' and meta['sha256'] != state['data_sha256']:
+            raise ValueError('Checkpoint training pool hash differs')
+        x, y = x.to(device), y.to(device)
+        with torch.no_grad():
+            output = predict(model, x, cfg['experiment']['prediction_batch_size'])
+            error = output - y
+            truth = negative_total_flow_rates(y, x)
+            predicted_flow = negative_total_flow_rates(output, x)
+            result = {'n': len(x), 'mse': error.square().mean().item(),
+                      'relative_l2_mean': (error.flatten(1).norm(dim=1) / y.flatten(1).norm(dim=1).clamp_min(1e-12)).mean().item(),
+                      'functional_mae': (truth - predicted_flow).abs().mean().item(),
+                      'predicted_best_index': predicted_flow.argmax().item(),
+                      'true_best_index': truth.argmax().item(),
+                      'prediction_shape': list(output.shape),
+                      'all_finite': bool(torch.isfinite(output).all().item()), 'data': meta}
+        if not result['all_finite']:
+            raise FloatingPointError('Nonfinite checkpoint inference')
+        metrics['splits'][split] = result
+        save_torch(out / 'results' / f'inference_{split}.pt', {'x': x.cpu(), 'target': y.cpu(),
+                   'prediction': output.cpu(), 'true_functional': truth.cpu(),
+                   'predicted_functional': predicted_flow.cpu(), 'checkpoint_sha256': metrics['checkpoint_sha256']})
+        print(f'INFERENCE split={split} {result}', flush=True)
+    save_json(out / 'results/inference_metrics.json', metrics)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/result.py b/scripts/result.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae6f0833517c5d66e362ce812b8b763a795710ca
--- /dev/null
+++ b/scripts/result.py
@@ -0,0 +1,140 @@
+"""Aggregate actual trials into Figure 1 style plots and the flow-rate table."""
+import csv
+import json
+from pathlib import Path
+
+from common import case_guard, load_config, load_data, parse_args, save_json, write_csv
+import numpy as np
+import torch
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+
+def read_trajectory(path, budget):
+    with open(path) as f:
+        rows = list(csv.DictReader(f))
+    if len(rows) != budget or [int(r['iteration']) for r in rows] != list(range(1, budget + 1)):
+        raise RuntimeError(f'Incomplete or nonsequential trial: {path}')
+    regrets = np.array([float(r['regret']) for r in rows])
+    if not np.isfinite(regrets).all() or (regrets < -1e-7).any():
+        raise ValueError(f'Invalid regret: {path}')
+    cumulative = regrets.cumsum()
+    if not np.allclose(cumulative, [float(r['cumulative_regret']) for r in rows]):
+        raise ValueError('Saved cumulative regret does not match sum of instant regrets')
+    return regrets
+
+
+def save_fig(fig, out, name):
+    for ext in ('png', 'pdf'):
+        fig.savefig(out / f'{name}.{ext}', dpi=300, bbox_inches='tight')
+    plt.close(fig)
+
+
+def main():
+    args = parse_args(__doc__).parse_args()
+    cfg = load_config(args.config)
+    case_guard(cfg)
+    out = Path(cfg['paths']['output_dir']) / 'results'
+    n = cfg['experiment']['iterations']
+    seeds = cfg['experiment']['seeds']
+    times = np.arange(1, n + 1)
+    actual = {'NOTS': np.stack([read_trajectory(out / f'trial_{s}/trajectory.csv', n) for s in seeds])}
+    if cfg['experiment']['run_random_baseline']:
+        actual['RS'] = np.stack([read_trajectory(out / f'rs_{s}.csv', n) for s in seeds])
+    statistics = {}
+    for method, regrets in actual.items():
+        cum = regrets.cumsum(axis=1)
+        avg = cum / times
+        finals = avg[:, -1]
+        statistics[method] = {'final_average_regret_mean': float(finals.mean()),
+                              'final_average_regret_std': float(finals.std(ddof=0)),
+                              'final_average_regret_sample_std': float(finals.std(ddof=1)),
+                              'per_trial_final_average_regret': finals.tolist(),
+                              'trials': len(seeds), 'iterations': n, 'provenance': 'measured'}
+        write_csv(out / f'{method.lower()}_curves.csv', [
+            {'iteration': int(t), 'cumulative_mean': float(cum[:, i].mean()),
+             'cumulative_std': float(cum[:, i].std()), 'average_mean': float(avg[:, i].mean()),
+             'average_std': float(avg[:, i].std())} for i, t in enumerate(times)])
+    save_json(out / 'metrics.json', {'metric_definition': 'mean_trials(sum_t(true_max - true_selected_t)/300)',
+                                    'std_ddof': 0, 'seeds': seeds, 'methods': statistics,
+                                    'published_references': cfg['reference'],
+                                    'unrerun_baselines': ['BO', 'BFO', 'GP-TS', 'STO-NTS']})
+    rows = []
+    for method, ref in cfg['reference'].items():
+        value = statistics.get(method)
+        rows.append({'method': method, 'measured_mean': value['final_average_regret_mean'] if value else '',
+                     'measured_std': value['final_average_regret_std'] if value else '',
+                     'paper_mean': ref['mean'], 'paper_std': ref['std'],
+                     'status': 'measured' if value else 'published_reference_only'})
+    write_csv(out / 'table1_flow_rates.csv', rows)
+    plt.rcParams.update({'font.size': 10, 'axes.spines.top': False, 'axes.spines.right': False})
+    fig = plt.figure(figsize=(12, 7.2), constrained_layout=True)
+    grid = fig.add_gridspec(2, 4, height_ratios=[1.1, 1])
+    ax0, ax1 = fig.add_subplot(grid[0, :2]), fig.add_subplot(grid[0, 2:])
+    for method, regrets in actual.items():
+        cumulative = regrets.cumsum(axis=1)
+        for ax, matrix in [(ax0, cumulative), (ax1, cumulative / times)]:
+            mean, std = matrix.mean(0), matrix.std(0)
+            line, = ax.plot(times, mean, label=f'{method} (measured)', lw=2)
+            ax.fill_between(times, mean - std, mean + std, color=line.get_color(), alpha=0.18)
+    ax0.set_ylabel('Cumulative regret $R_t$')
+    ax1.set_ylabel('Average regret $R_t/t$')
+    for ax in (ax0, ax1):
+        ax.set_xlabel('Oracle query')
+        ax.legend()
+        ax.grid(alpha=0.2)
+    x, y, meta = load_data(cfg)
+    oracle = torch.load(out / 'oracle.pt', map_location='cpu', weights_only=True)
+    best, worst = oracle['best_index'], oracle['worst_index']
+    # Common scales for best/worst pressure, original permeability for display.
+    panels = [(x[best, 0], 'Best permeability a', 'gray', x.min().item(), x.max().item()),
+              (y[best, 0], 'Best pressure u', 'viridis', y.min().item(), y.max().item()),
+              (x[worst, 0], 'Worst permeability a', 'gray', x.min().item(), x.max().item()),
+              (y[worst, 0], 'Worst pressure u', 'viridis', y.min().item(), y.max().item())]
+    for i, (a, title, cmap, vmin, vmax) in enumerate(panels):
+        ax = fig.add_subplot(grid[1, i])
+        im = ax.imshow(a.numpy(), origin='lower', extent=[0, 1, 0, 1], cmap=cmap,
+                       interpolation='nearest', vmin=vmin, vmax=vmax)
+        ax.set_title(title)
+        ax.set_xlabel('x')
+        ax.set_ylabel('y')
+        fig.colorbar(im, ax=ax, shrink=0.7)
+    fig.suptitle('Darcy negative total flow rates: 10 independent trials, 300 queries\nShading: ±1 trial standard deviation; reference-only methods are omitted')
+    save_fig(fig, out, 'figure1')
+    inf = torch.load(out / 'inference_test.pt', map_location='cpu', weights_only=True)
+    target, pred = inf['target'][0, 0].numpy(), inf['prediction'][0, 0].numpy()
+    fig, axes = plt.subplots(1, 4, figsize=(12, 3), constrained_layout=True)
+    for ax, field, title, cmap in zip(axes,
+        [inf['x'][0, 0].numpy(), target, pred, np.abs(pred - target)],
+        ['Test permeability', 'Oracle pressure', 'Loaded checkpoint pressure', 'Absolute error'],
+        ['gray', 'viridis', 'viridis', 'magma']):
+        kwargs = {'vmin': float(min(target.min(), pred.min())), 'vmax': float(max(target.max(), pred.max()))} if 'pressure' in title else {}
+        im = ax.imshow(field, origin='lower', extent=[0, 1, 0, 1], cmap=cmap, **kwargs)
+        ax.set_title(title)
+        fig.colorbar(im, ax=ax, shrink=0.7)
+    save_fig(fig, out, 'checkpoint_inference')
+    fig, ax = plt.subplots(figsize=(8, 4), constrained_layout=True)
+    for seed in seeds:
+        with (out / f'trial_{seed}/training_loss.jsonl').open() as f:
+            losses = [json.loads(line) for line in f]
+        final_epochs = {r['iteration']: r['mse'] for r in losses if r['epoch'] == cfg['training']['epochs']}
+        ax.plot(list(final_epochs), list(final_epochs.values()), alpha=0.6, label=f'seed {seed}')
+    ax.set(xlabel='Oracle query', ylabel='Final epoch training pressure MSE', yscale='log')
+    ax.legend(ncol=2, fontsize=8)
+    save_fig(fig, out, 'training_loss')
+    measured = statistics['NOTS']
+    report = ('# Darcy flow rates reproduction results\n\n'
+              f'NOTS measured R300/300 = {measured["final_average_regret_mean"]:.8g} ± '
+              f'{measured["final_average_regret_std"]:.8g} (population std, ten trials).\n\n'
+              'Paper Table 1 reference: 0.012 ± 0.001. This is a literature value, not measured data.\n\n'
+              'All measured trajectories contain 300 queries including one random initial query. '
+              'Training and acquisition use the same physical pressure scale. '
+              'The paper main-text flow sign conflicts with Appendix D.1; the latter is used. '
+              'Published BO/BFO/GP-TS/STO-NTS entries are reference-only, not reruns.\n')
+    (out / 'report.md').write_text(report)
+    print('RESULT ' + json.dumps(statistics), flush=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/train.py b/scripts/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..438091f876de634d202698b441d0b79426ffae25
--- /dev/null
+++ b/scripts/train.py
@@ -0,0 +1,203 @@
+"""Paper Algorithm 2, 300 oracle queries x 10 independent trials.
+
+Run only inside a case-owned SLURM job. --resume resumes at a query boundary;
+it never warm-starts a new posterior draw from a previous iteration's model.
+"""
+import json
+import time
+from pathlib import Path
+
+from common import (case_guard, config_hash, hardware_record, load_config,
+                    load_data, model_state_on_cpu, negative_total_flow_rates, parse_args, predict,
+                    restore_rng, rng_state, save_json, save_torch, seed_all,
+                    write_csv)
+import torch
+from model.NOTS import NOTS
+
+
+def fit_posterior(cfg, xs, ys, seed, iteration, loss_file):
+    # New model, all fresh weights and optimizer state on every invocation.
+    model = NOTS(**cfg['model']).to(xs.device)
+    tc = cfg['training']
+    if tc['normalization'] != 'none':
+        raise ValueError('Audited paper-priority protocol requires physical-space MSE')
+    optimizer = torch.optim.AdamW(model.parameters(), lr=tc['lr'], weight_decay=0)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=tc['epochs'])
+    model.train()
+    for epoch in range(tc['epochs']):
+        mse_total = loss_total = 0.0
+        count = 0
+        for first in range(0, len(xs), tc['batch_size']):
+            bx, by = xs[first:first + tc['batch_size']], ys[first:first + tc['batch_size']]
+            optimizer.zero_grad(set_to_none=True)
+            pressure = model(bx)
+            mse = (pressure - by).square().mean()
+            penalty = model.l2_penalty()
+            loss = mse + tc['lambda_reg'] * penalty
+            if not torch.isfinite(loss):
+                raise FloatingPointError(f'Nonfinite training loss seed={seed} query={iteration}')
+            loss.backward()
+            optimizer.step()
+            mse_total += mse.item() * len(bx)
+            loss_total += loss.item() * len(bx)
+            count += len(bx)
+        lr = scheduler.get_last_lr()[0]
+        scheduler.step()
+        record = {'seed': seed, 'iteration': iteration, 'epoch': epoch + 1,
+                  'n_observations': len(xs), 'mse': mse_total / count,
+                  'loss': loss_total / count, 'lr': lr}
+        with open(loss_file, 'a') as f:
+            f.write(json.dumps(record) + '\n')
+        print('TRAIN ' + json.dumps(record), flush=True)
+    return model
+
+
+@torch.no_grad()
+def observed_mse(model, xs, ys, batch_size):
+    err = 0.0
+    for first in range(0, len(xs), batch_size):
+        err += (model(xs[first:first + batch_size]) - ys[first:first + batch_size]).square().sum().item()
+    return err / ys.numel()
+
+
+def trial(cfg, seed, x, oracle_y, truth, noise_std, data_meta, resume):
+    out = Path(cfg['paths']['output_dir'])
+    rd = out / 'results' / f'trial_{seed}'
+    rd.mkdir(parents=True, exist_ok=True)
+    state_file = rd / 'resume.pt'
+    fingerprint = config_hash(cfg)
+    seed_all(seed)
+    records, indices, noisy_y = [], [], []
+    model = None
+    if resume and state_file.exists():
+        state = torch.load(state_file, map_location='cpu', weights_only=False)
+        if state['config_hash'] != fingerprint or state['data_sha256'] != data_meta['sha256']:
+            raise RuntimeError('Resume config/data differs from original experiment')
+        records, indices = state['records'], state['indices']
+        noisy_y = list(state['noisy_y'].to(x.device).unbind())
+        restore_rng(state['rng'])
+        if len(records) == cfg['experiment']['iterations'] and 'final_model_state' in state:
+            model = NOTS(**cfg['model']).to(x.device)
+            model.load_state_dict(state['final_model_state'], strict=True)
+        print(f'RESUME seed={seed} completed_queries={len(records)}', flush=True)
+    elif state_file.exists():
+        raise FileExistsError(f'{state_file} exists; use --resume to avoid overwriting an experiment')
+    start = time.monotonic()
+    for t in range(len(records), cfg['experiment']['iterations']):
+        tick = time.monotonic()
+        acquisition = None
+        if t < cfg['experiment']['n_init']:
+            chosen = torch.randint(len(x), ()).item()
+        else:
+            fit_x = x[torch.tensor(indices, device=x.device)]
+            fit_y = torch.stack(noisy_y)
+            model = fit_posterior(cfg, fit_x, fit_y, seed, t + 1, rd / 'training_loss.jsonl')
+            values = negative_total_flow_rates(predict(model, x, cfg['experiment']['prediction_batch_size']), x)
+            chosen = values.argmax().item()
+            acquisition = values[chosen].item()
+        # Only the selected pressure becomes an observed training example.
+        observation = oracle_y[chosen] + torch.randn_like(oracle_y[chosen]) * noise_std
+        indices.append(chosen)
+        noisy_y.append(observation)
+        regret = truth.max().item() - truth[chosen].item()
+        cumulative = sum(row['regret'] for row in records) + regret
+        row = {'iteration': t + 1, 'candidate_index': chosen,
+               'outcome': truth[chosen].item(),
+               'observation': negative_total_flow_rates(observation.unsqueeze(0), x[chosen:chosen + 1]).item(),
+               'acquisition': acquisition, 'regret': regret,
+               'cumulative_regret': cumulative, 'average_regret': cumulative / (t + 1),
+               'n_observations_used_for_fit': t,
+               'elapsed_seconds': time.monotonic() - tick}
+        records.append(row)
+        write_csv(rd / 'trajectory.csv', records)
+        boundary_state = {'config_hash': fingerprint, 'data_sha256': data_meta['sha256'],
+                          'records': records, 'indices': indices,
+                          'noisy_y': torch.stack(noisy_y).cpu(), 'rng': rng_state()}
+        if t + 1 == cfg['experiment']['iterations']:
+            boundary_state['final_model_state'] = model_state_on_cpu(model)
+        save_torch(state_file, boundary_state)
+        print(f'QUERY seed={seed} ' + json.dumps(row), flush=True)
+        save_json(rd / 'progress.json', {'seed': seed, 'completed_queries': len(records),
+                                        'status': 'running', 'last_metrics': row})
+    checkpoint_path = out / 'weight' / f'trial_{seed}.pt'
+    if model is None:
+        # A completed trial needs no extra posterior draw after resumption.
+        if not checkpoint_path.exists():
+            raise RuntimeError('Completed trajectory lacks final trained checkpoint')
+        return checkpoint_path
+    # Final model was fit before the 300th oracle query (299 observations).
+    used_indices = indices[:-1]
+    final_train_mse = observed_mse(model, x[torch.tensor(used_indices, device=x.device)],
+                                   torch.stack(noisy_y[:-1]), cfg['training']['batch_size'])
+    checkpoint = {'model_state': model_state_on_cpu(model),
+                  'model_config': cfg['model'], 'normalization': {'kind': 'identity'},
+                  'seed': seed, 'iteration': len(records), 'fit_indices': used_indices,
+                  'n_fit_observations': len(used_indices), 'train_mse': final_train_mse,
+                  'config_hash': fingerprint, 'data_sha256': data_meta['sha256'],
+                  'selection_rule': 'minimum observed training MSE among final trial models; test never used'}
+    save_torch(checkpoint_path, checkpoint)
+    # This is sequential within one allocation, so no concurrent writers.
+    best_path = out / 'weight/best_model.pt'
+    old_mse = float('inf')
+    if best_path.exists():
+        old_mse = torch.load(best_path, map_location='cpu', weights_only=False)['train_mse']
+    if final_train_mse < old_mse:
+        save_torch(best_path, checkpoint)
+    save_json(rd / 'progress.json', {'seed': seed, 'completed_queries': len(records),
+                                    'status': 'completed', 'last_metrics': records[-1],
+                                    'checkpoint': str(checkpoint_path), 'train_mse': final_train_mse,
+                                    'seconds_this_invocation': time.monotonic() - start})
+    print(f'TRIAL_COMPLETE seed={seed} average_regret={records[-1]["average_regret"]:.9g} train_mse={final_train_mse:.9g}', flush=True)
+    return checkpoint_path
+
+
+def random_baseline(cfg, truth):
+    out = Path(cfg['paths']['output_dir']) / 'results'
+    for seed in cfg['experiment']['seeds']:
+        # Isolated stream, same fixed candidate pool, repeated queries permitted.
+        generator = torch.Generator().manual_seed(seed)
+        choices = torch.randint(len(truth), (cfg['experiment']['iterations'],), generator=generator)
+        regrets = truth.cpu().max() - truth.cpu()[choices]
+        records = []
+        cumulative = 0.0
+        for t, (idx, regret) in enumerate(zip(choices.tolist(), regrets.tolist()), 1):
+            cumulative += regret
+            records.append({'iteration': t, 'candidate_index': idx, 'regret': regret,
+                            'cumulative_regret': cumulative, 'average_regret': cumulative / t})
+        write_csv(out / f'rs_{seed}.csv', records)
+        print(f'RS seed={seed} average_regret={records[-1]["average_regret"]:.9g}', flush=True)
+
+
+def main():
+    parser = parse_args(__doc__)
+    parser.add_argument('--resume', action='store_true')
+    parser.add_argument('--seed', type=int, help='Optional single configured trial for recovery')
+    args = parser.parse_args()
+    cfg = load_config(args.config)
+    device = case_guard(cfg)
+    if cfg['experiment']['iterations'] != 300 or len(cfg['experiment']['seeds']) != 10:
+        raise ValueError('Formal experiment requires 300 queries and 10 independent seeds')
+    x, y, meta = load_data(cfg)
+    out = Path(cfg['paths']['output_dir'])
+    save_json(out / 'results/data_manifest.json', meta)
+    save_json(out / 'results/hardware.json', hardware_record())
+    x, y = x.to(device), y.to(device)
+    truth = negative_total_flow_rates(y, x)
+    # Appendix D.3 specifies a variance ratio, hence the square root here.
+    noise_std = (cfg['experiment']['noise_variance_fraction'] * y.var(dim=0, correction=1)).sqrt()
+    save_torch(out / 'results/oracle.pt', {'objectives': truth.cpu(), 'noise_std': noise_std.cpu(),
+                                          'best_index': truth.argmax().item(), 'worst_index': truth.argmin().item(),
+                                          'data_sha256': meta['sha256']})
+    print(f'ORACLE max={truth.max().item():.9g} min={truth.min().item():.9g} ' + json.dumps(meta), flush=True)
+    seeds = [args.seed] if args.seed is not None else cfg['experiment']['seeds']
+    if not set(seeds).issubset(cfg['experiment']['seeds']):
+        raise ValueError('Seed is outside configured independent trials')
+    if cfg['experiment']['run_random_baseline']:
+        random_baseline(cfg, truth)
+    for seed in seeds:
+        trial(cfg, seed, x, y, truth, noise_std, meta, args.resume)
+    print('TRAINING_REQUEST_FINISHED', flush=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/weight/best_model.pt b/weight/best_model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..79314201c42414d355e3b559ba69525419823855
--- /dev/null
+++ b/weight/best_model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dac0b15574242acabd25d5bc52b7a415ec72e1d55411433402affe61876131f7
+size 19095398