nur-dev commited on 25 days ago

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Publish reproducibility artifacts

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.gitattributes +1 -34
ENVIRONMENT.json +11 -0
LICENSE.md +33 -0
README.md +60 -0
SHA256SUMS.json +351 -0
analyze.py +118 -0
data/controlled/manifest.json +1165 -0
data/controlled/worlds.json +1 -0
data/controlled/worlds.safetensors +3 -0
data/hydraulic/prepared.json +1 -0
data/hydraulic/prepared.safetensors +3 -0
data/temperature/prepared.json +1 -0
data/temperature/prepared.safetensors +3 -0
finite_stats.py +25 -0
models/hydraulic/n128_s20751/additive_diag.json +1 -0
models/hydraulic/n128_s20751/additive_diag.safetensors +3 -0
models/hydraulic/n128_s20751/additive_full.json +1 -0
models/hydraulic/n128_s20751/additive_full.safetensors +3 -0
models/hydraulic/n128_s20751/factorized_classifier.json +1 -0
models/hydraulic/n128_s20751/factorized_classifier.safetensors +3 -0
models/hydraulic/n128_s20751/factorized_kernel.json +1 -0
models/hydraulic/n128_s20751/factorized_kernel.safetensors +3 -0
models/hydraulic/n128_s20751/factorized_lda.json +1 -0
models/hydraulic/n128_s20751/factorized_lda.safetensors +3 -0
models/hydraulic/n128_s20751/factorized_logistic.json +1 -0
models/hydraulic/n128_s20751/factorized_logistic.safetensors +3 -0
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models/hydraulic/n128_s20751/factorized_nb.safetensors +3 -0
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models/hydraulic/n128_s20751/factorized_neural.safetensors +3 -0
models/hydraulic/n128_s20751/factorized_ridge.json +1 -0
models/hydraulic/n128_s20751/factorized_ridge.safetensors +3 -0
models/hydraulic/n128_s20751/joint_gaussian.json +1 -0
models/hydraulic/n128_s20751/joint_gaussian.safetensors +3 -0
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models/hydraulic/n128_s20751/joint_neural.safetensors +3 -0
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models/hydraulic/n128_s20752/factorized_kernel.safetensors +3 -0
models/hydraulic/n128_s20752/factorized_lda.json +1 -0
models/hydraulic/n128_s20752/factorized_lda.safetensors +3 -0
models/hydraulic/n128_s20752/factorized_logistic.json +1 -0
models/hydraulic/n128_s20752/factorized_logistic.safetensors +3 -0
models/hydraulic/n128_s20752/factorized_nb.json +1 -0
models/hydraulic/n128_s20752/factorized_nb.safetensors +3 -0

.gitattributes CHANGED Viewed

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1	*.safetensors filter=lfs diff=lfs merge=lfs -text
2	+ *.csv.gz filter=lfs diff=lfs merge=lfs -text

ENVIRONMENT.json ADDED Viewed

	@@ -0,0 +1,11 @@

+{
+  "python": "3.13.11 | packaged by Anaconda, Inc. | (main, Dec 10 2025, 21:28:48) [GCC 14.3.0]",
+  "pytorch": "2.6.0+cu124",
+  "cuda": "12.4",
+  "gpu": "NVIDIA H200",
+  "platform": "Linux-5.15.0-1063-nvidia-x86_64-with-glibc2.35",
+  "format": "safetensors tensors with JSON structure; no executable pickle",
+  "export_is_lossless": true,
+  "original_files_modified": false,
+  "external_gas_raw_data_included": false
+}

LICENSE.md ADDED Viewed

	@@ -0,0 +1,33 @@

+# Release licenses
+The licensing below applies to this prepared release; it does not relicense unrelated files in the Union-Mind workspace.
+## Original code: MIT
+Copyright (c) 2026 Nurgali Kadyrbek, Madina Mansurova, Balzhan Akhmetova, Birlik Sagitzhanov and Roza Sabirova.
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+## Original models, generated data, metrics and documentation: CC BY 4.0
+These assets are licensed under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/legalcode). Attribute the five article authors and preserve the source notices. The synthetic probability laws are researcher-generated; the physical recordings remain attributable to their original creators.
+## Third-party sensor features: CC BY 4.0
+Processed hydraulic and temperature-modulated sensor arrays are adaptations of the UCI datasets identified in `THIRD_PARTY_NOTICES.md`. The original creators retain attribution. No ownership of their original recordings is claimed.
+## Gas-drift source restriction
+No raw or processed UCI 224 gas-drift records are redistributed. Reproduction code retrieves that source separately. Its legacy description limits use to research despite newer repository CC BY metadata; that notice remains applicable to any separately obtained copy. This release does not grant rights beyond those offered by the source creators.
+# Source attribution
+| Source | Creators | DOI | Included modifications |
+|---|---|---|---|
+| Condition monitoring of hydraulic systems | Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze | 10.24432/C5CW21 | Four within-cycle means per physical sensor, categorical condition indices, masks and fitted models; CC BY 4.0 |
+| Gas sensor array temperature modulation | Javier Burgués | 10.24432/C5S302 | Four within-episode resistance means, reference-derived condition labels, day/binding masks and fitted models; CC BY 4.0 |
+| Gas sensor array drift | Alexander Vergara | 10.24432/C5RP6W | Aggregate evaluation metrics and source adapters only; no source records; preserve legacy research-only notice when retrieving data |

README.md ADDED Viewed

	@@ -0,0 +1,60 @@

+---
+language:
+- en
+library_name: pytorch
+license: other
+license_name: mit-code-and-cc-by-4-0-research-artifacts
+license_link: LICENSE.md
+tags:
+- bayesian-inference
+- tabular-classification
+- compositional-generalization
+- safetensors
+---
+# Predictive representations for compact Bayesian decision making
+Reproduction assets for *Predictive Representations for Compact Bayesian Decision Making: Controlled Compression and Compositional Sensor Diagnosis*.
+Includes all 132 sensor-model checkpoints (11 methods, two label budgets, three seeds, two datasets), processed features and split indices, generated probability laws and samples, complete reference scores, fixed protocols and executable code. Checkpoints use paired JSON and Safetensors files. No pretrained language model or executable pickle checkpoint is required.
+## Run
+Python 3.11+ and a CUDA-enabled PyTorch installation are required. The recorded environment is PyTorch 2.6.0/CUDA 12.4 on an NVIDIA H200; timings depend on hardware.
+```bash
+python -m pip install -r requirements.txt
+python reproduce.py --mode check --output checksum_report.json
+python reproduce.py --mode all --device cuda:0 --output replay_report.json
+python analyze.py --device cuda:0 --output statistics.json
+python paper/build_figures.py
+```
+Replay checks all 132 checkpoints, the controlled primary comparison and the four-day sign-flip result. Analysis recomputes paired uncertainty from the supplied reference scores. Figure generation writes all article figures and tables under paper/.
+Optional independent refitting, source reconstruction and sensitivity checks:
+```bash
+python reproduce.py --mode refit --device cuda:0 --output refit_report.json
+python rebuild_sources.py --device cuda:0 --cache raw_downloads --output source_rebuild.json
+python rebuild_external.py --device cuda:0 --output external_reproduction
+python rebuild_controlled.py --device cuda:0 --output controlled_reproduction
+python paper/sensitivity_analysis.py --device cuda:0 --output sensitivity.json
+```
+Raw-source reconstruction downloads the official archives and verifies their digests. External and full controlled reruns can take substantially longer than checkpoint replay. Use a new output directory for each rerun. Original arrays and numerical modules are unchanged.
+## Contents and scope
+- models/: every fitted sensor comparison, including adverse results.
+- data/: processed sensor inputs, labels, splits, generated laws and sample streams.
+- results/: reference predictions, complete external metrics and controlled frontiers.
+- protocols/ and provenance/: fixed recipes, source definitions, eligibility failures and measured compute.
+- unionmind/: numerical implementations; use the standalone commands above.
+- SHA256SUMS.json: integrity hashes for the distributed files.
+The controlled compression gain is 0.1013 utility. The external compression improvement criterion fails. The four-day temporal comparison is secondary because one source day fails its clock check; exact one-sided day-level p=0.0625. The 128-label hydraulic reversal, calibration failure and lack of complete-cost superiority are retained. These artifacts do not establish operational root-cause reliability.
+## Data and license
+Code is MIT; original models, generated data and research outputs are CC BY 4.0. Hydraulic features derive from Helwig, Pignanelli and Schuetze (UCI DOI 10.24432/C5CW21); temperature-modulated sensor features derive from Burgues (10.24432/C5S302). Gas-drift recordings (Vergara, 10.24432/C5RP6W) are retrieved separately and are not redistributed; preserve the source's legacy research-only notice. Full attribution and terms are in LICENSE.md.

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+  "results/external/all_metrics.csv.gz": "0071a3d87dba03913a2e442a7e2b752ea86d64da0c7e7ddf9fa5a6717820f522",
+  "results/external/decision.json": "d356df0d93452d86af1c5074c7dc1137a1e8841f277d1f21caec2c9ea49bd203",
+  "results/external/overall.json": "2a0fed264f6118a659eb36ff17cfec9bf03812deeee889f7bc3d91925dfe6b9c",
+  "results/external/paired_comparisons.json": "f2a368a09611e047f94312ebed8e82e8669e12c0c228162f6fe6903e3fd06cb8",
+  "results/hydraulic/predictions.json": "190d3f225d309a98d6b00abc1d3b4e2f11c63706aaa94b585476cc2484b9f25b",
+  "results/hydraulic/predictions.safetensors": "2409700b0e6bbde39954f423dae9198c017a53066797801a62d421b5f6f404a8",
+  "results/sensitivity_analysis/frozen_diagnostics.json": "434dd65be1ab604360f5d7fdf03ef18ab1ef13e8fcd00245620e6574d4f57a37",
+  "results/temperature/predictions.json": "39bd384897cd9e4a6cefb65d06cde29aec83023cb7eb42515cfd34900ab07c97",
+  "results/temperature/predictions.safetensors": "1760854a676b82df524bfa9e4eb1b539b1a2754e05e92e5d0e8229719958f8f3",
+  "unionmind/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "unionmind/continuous_decision/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "unionmind/continuous_decision/classifiers.py": "ebb1bfcd9d72469368ffc14cbb847faeb53a69d19abc9b684546f3791d61e20b",
+  "unionmind/continuous_decision/factor_acquisition.py": "771f560388ba9a24adadd7fd84d5bb90ff5f52e16ffc92e121a0aa4ba0a91b51",
+  "unionmind/continuous_decision/factor_analysis.py": "0fb267e3c0572db48f4a2ca41fc4777ec082039c8312f66a75005355837c3fb8",
+  "unionmind/continuous_decision/factor_study.py": "beb76b2cc09aaa2b796e8bf69f0235fb9b0c33823ce1e33ea8170d56b60ba109",
+  "unionmind/continuous_decision/factorial.py": "54241b65b32042937346369414d750fe58042f0eae20f25c391a74a480e065f0",
+  "unionmind/continuous_decision/interval_value.py": "cd3c0158a377c97fa9f4dc2f56c2a9597840a68bb07d37782b42265f727e4fe9",
+  "unionmind/continuous_decision/learned_controls.py": "81e3419c8f20422ed495fab8b43fa264dceb8503d1a3ad71cdc70b4c32b0d6f3",
+  "unionmind/continuous_decision/source.py": "1c011b7608637c73652224573caf152cefc3297c2f94599b8f88816ded829601",
+  "unionmind/day_transfer.py": "af750c0f6f1c19923eb3e90acf9edaa56a998d9ae460b2d18002edfc0e33d774",
+  "unionmind/decision_sufficiency/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "unionmind/decision_sufficiency/analysis.py": "eeb72ce872762b4af5f19c3d9d39013c05c407f32cb007733f4005fd107377ef",
+  "unionmind/decision_sufficiency/benchmark.py": "de9d93e521ee59882f23015fdc8e3912c8b765a412952ba4d16eb5a812722ed9",
+  "unionmind/decision_sufficiency/core.py": "fa2f7405a8d0dc6ec96ebae1bb2a2b810d03ef1fd2877f5ebef7df1722992cee",
+  "unionmind/decision_sufficiency/study.py": "dcb7d8303fe11e9e1c27c1e8b9a141f4571f49f1c1611a1b3572537e4ce7ec19",
+  "unionmind/decision_sufficiency/worlds.py": "b2dd3a0db509161982f8159caeba35f8af5212aef0e3685ac56b702361b15d5e",
+  "unionmind/external_sufficiency/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "unionmind/external_sufficiency/analysis.py": "94040f14991c1784122d19f62ececc3f1533641b7b7738966c058b2749033a98",
+  "unionmind/external_sufficiency/benchmark.py": "a7999a53c7a6ca187912af7b47cfaa5128df3c47610a695e0163131ac05ca5cb",
+  "unionmind/external_sufficiency/data.py": "20275fb0a49a3835c286076ffad5a59312081ef7d1993b9706b873de6d2c3976",
+  "unionmind/external_sufficiency/runtime.py": "fa19ce1b7fba9e6953e9e4081d4b6001ed55c33edc48a05960d5a13ae6aad61d",
+  "unionmind/external_sufficiency/study.py": "7053ad47d088807ecf30af59fccb672dca706eca0d498ecc90a7131d677222c6",
+  "unionmind/predictive_transfer/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "unionmind/predictive_transfer/data.py": "259202796bf921db2beaa75166809e455e44aaddef73da96bdec970067abfbd6",
+  "unionmind/supported_observations/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+  "unionmind/supported_observations/data.py": "d359f27c4cd23482ed4ef8326d3026c10b19caf9ba75b3da677249f5d9c9e43f"
+}

analyze.py ADDED Viewed

	@@ -0,0 +1,118 @@

+"""Recompute registered paired statistics from the distributed reference scores."""
+from pathlib import Path
+import argparse,contextlib,hashlib,io,json,tempfile,time
+import numpy as np
+import pandas as pd
+import torch
+from reproduce import ROOT,load,verify
+from finite_stats import sign_flip_pvalues
+from unionmind.decision_sufficiency.analysis import paired_interval
+from unionmind.continuous_decision.factor_study import MODELS
+def controlled(path):
+    cfg=json.loads((ROOT/'protocols/decision_sufficiency_protocol_01.json').read_text())
+    frame=pd.read_csv(path)
+    primary=frame[(frame.model==cfg['primary']['model'])&(frame.query_budget>0)]
+    values=primary.groupby(['world','family','method']).utility.mean().unstack('method')
+    expected=json.loads((ROOT/'results/controlled/decision.json').read_text())
+    rows=[]
+    for control in cfg['primary']['compact_controls']+['identity']:
+        delta=(values['query_joint']-values[control]).to_numpy()
+        ci=paired_interval(delta,values.index.get_level_values('family').to_numpy(),
+            cfg['primary']['bootstrap_seed'],cfg['primary']['bootstrap_draws'],
+            .05/len(cfg['primary']['compact_controls']) if control!='identity' else .05)
+        old=next(r for r in expected['compact_comparisons'] if r['control']==control)
+        assert abs(delta.mean()-old['mean_gain'])<1e-10
+        assert np.max(np.abs(np.array(ci)-old['paired_interval']))<1e-10
+        rows.append(dict(control=control,gain=float(delta.mean()),interval=ci))
+    return dict(worlds=len(values),comparisons=rows)
+def hydraulic(device):
+    records=load('results/hydraulic/predictions',device)['results']
+    old=json.loads((ROOT/'provenance/hydraulic/opened_hydraulic_summary.json').read_text())
+    rng=torch.Generator(device=device).manual_seed(20931);output=[]
+    for n in [64,128]:
+        rows=[r for r in records if r['samples']==n];mask=rows[0]['unseen']
+        groups,inverse=torch.unique(rows[0]['groups'][mask],return_inverse=True)
+        counts=torch.bincount(inverse).double();values=counts.new_zeros((len(groups),len(MODELS)))
+        for i,name in enumerate(MODELS):
+            score=torch.stack([r['metrics']['top3'] for r in rows if r['model']==name]).mean(0)
+            values[:,i].scatter_add_(0,inverse,score[mask])
+        sample=torch.randint(len(groups),(20000,len(groups)),device=device,generator=rng)
+        weights=counts.new_zeros((20000,len(groups))).scatter_add_(1,sample,torch.ones_like(sample,dtype=counts.dtype))
+        boot=weights@values/(weights@counts)[:,None];point=values.sum(0)/counts.sum()
+        for candidate in ['additive_full','factorized_neural']:
+            ci=MODELS.index(candidate)
+            for j,name in enumerate(MODELS):
+                if ci==j:continue
+                interval=torch.quantile(boot[:,ci]-boot[:,j],boot.new_tensor([.05/80,1-.05/80])).tolist()
+                row=dict(samples=n,candidate=candidate,control=name,gain=float(point[ci]-point[j]),simultaneous_interval=interval)
+                reference=next(r for r in old['comparisons'] if all(r[k]==row[k] for k in ['samples','candidate','control']))
+                assert abs(row['gain']-reference['gain'])<1e-10
+                assert np.max(np.abs(np.array(interval)-reference['simultaneous_interval']))<1e-9
+                output.append(row)
+    return dict(exploratory=True,comparisons=output)
+def temporal(device):
+    records=load('results/temperature/predictions',device)['results']
+    old=json.loads((ROOT/'provenance/temperature/summary.json').read_text())
+    rng=torch.Generator(device=device).manual_seed(20891);output=[]
+    for n in [64,128]:
+        rows=[r for r in records if r['samples']==n];mask=rows[0]['unseen'];day=rows[0]['days'][mask]
+        matrix=torch.stack([torch.stack([r['metrics']['top3'] for r in rows if r['model']==name]).mean(0)[mask] for name in MODELS],-1)
+        points=[];boots=[]
+        for d in [9,10,11,12]:
+            x=matrix[day==d];points.append(x.mean(0))
+            indices=torch.randint(len(x),(20000,len(x)),device=device,generator=rng)
+            boots.append(x[indices].mean(1))
+        days=torch.stack(points);point=days.mean(0);boot=torch.stack(boots).mean(0)
+        sampled=torch.randint(4,(20000,4),device=device,generator=rng);day_boot=days[sampled].mean(1)
+        for candidate in ['additive_full','factorized_neural']:
+            ci=MODELS.index(candidate)
+            for j,name in enumerate(MODELS):
+                if ci==j:continue
+                interval=torch.quantile(boot[:,ci]-boot[:,j],boot.new_tensor([.05/80,1-.05/80])).tolist()
+                cluster=torch.quantile(day_boot[:,ci]-day_boot[:,j],boot.new_tensor([.05/80,1-.05/80])).tolist()
+                delta=days[:,ci]-days[:,j];p1,p2=sign_flip_pvalues(delta)
+                row=dict(samples=n,candidate=candidate,control=name,gain=float(point[ci]-point[j]),
+                    fixed_day_simultaneous_interval=interval,day_cluster_simultaneous_interval=cluster,
+                    day_gains=delta.tolist(),exact_day_p_one_sided=float(p1),exact_day_p_two_sided=float(p2))
+                reference=next(r for r in old['comparisons'] if all(r[k]==row[k] for k in ['samples','candidate','control']))
+                assert abs(row['gain']-reference['gain'])<1e-10
+                assert np.max(np.abs(np.array(interval)-reference['fixed_day_simultaneous_interval']))<1e-9
+                output.append(row)
+    return dict(secondary=True,complete_source_cohort=False,target_days=4,comparisons=output)
+def external(device):
+    from unionmind.external_sufficiency import data,study,analysis
+    with tempfile.TemporaryDirectory(prefix='external-statistics-') as tmp:
+        out=Path(tmp);(out/'evaluation').mkdir()
+        (out/'evaluation/metrics.csv.gz').symlink_to(ROOT/'results/external/all_metrics.csv.gz')
+        cfg=json.loads((ROOT/'protocols/external_sufficiency_protocol_01.json').read_text());cfg['device']=device
+        protocol=out/'protocol.json';protocol.write_text(json.dumps(cfg))
+        for module in [data,study,analysis]:module.ROOT=out;module.PROTOCOL=protocol
+        paths=[protocol,ROOT/'results/external/all_metrics.csv.gz']
+        data.write(out/'preconfirmation_lock.json',dict(purpose='Reproduction of statistics, not a new confirmation lock',
+            files={str(p):hashlib.sha256(p.read_bytes()).hexdigest() for p in paths}))
+        with contextlib.redirect_stdout(io.StringIO()):analysis.run()
+        report=json.loads((out/'analysis/paired_comparisons.json').read_text())
+        expected=json.loads((ROOT/'results/external/paired_comparisons.json').read_text())
+        for a,b in zip(report['comparisons'],expected['comparisons']):
+            assert a['model']==b['model'] and a['representation']==b['representation']
+            assert abs(a['gain']-b['gain'])<1e-10
+            assert np.max(np.abs(np.array(a['simultaneous_95_interval'])-b['simultaneous_95_interval']))<1e-9
+        return report
+def main():
+    parser=argparse.ArgumentParser(description=__doc__);parser.add_argument('--device',default='cuda:0')
+    parser.add_argument('--output',default='statistics.json');a=parser.parse_args()
+    assert a.device.startswith('cuda') and torch.cuda.is_available()
+    torch.cuda.set_device(a.device);torch.backends.cuda.matmul.allow_tf32=False
+    started=time.perf_counter();verify()
+    report=dict(controlled=controlled(ROOT/'results/controlled/primary_predictions.csv.gz'),
+        hydraulic=hydraulic(a.device),temporal=temporal(a.device),external=external(a.device))
+    torch.cuda.synchronize();report['seconds']=time.perf_counter()-started
+    report['all_archived_primary_intervals_reproduced']=True
+    Path(a.output).write_text(json.dumps(report,indent=2)+'\n')
+    print(json.dumps(dict(all_archived_primary_intervals_reproduced=True,seconds=report['seconds']),indent=2))
+if __name__=='__main__':main()

data/controlled/manifest.json ADDED Viewed

	@@ -0,0 +1,1165 @@

+{
+  "role": "confirmation",
+  "worlds": [
+    {
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+      "family": "independent",
+      "seed": 18501,
+      "held_mechanism": false
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+      "family": "orientation_alias",
+      "seed": 68532,
+      "held_mechanism": true
+    }
+  ],
+  "generated_worlds": 192,
+  "supplied_model_to_estimator": false,
+  "independent_external_authorship": false,
+  "generated_after_code_lock": true,
+  "device": "cuda:2",
+  "generator_seconds": 0.2140521870169323,
+  "file_sha256": "fdf596340f2d0dbbd1ba2d0fbb9bbc7fd2ea272e890b82552384334060ce9e1f",
+  "protocol_sha256": "5a334fe26903403ff48eb71e7b0eb548d139af50c97b430252db0cf71b2c1f59"
+}

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finite_stats.py ADDED Viewed

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+"""Finite reference distributions for verification, never model selection."""
+import torch
+def inverse_empirical_cdf(values,probabilities):
+    """Quantiles of a finite uniform law; no interpolation between support points."""
+    assert values.ndim==1 and probabilities.ndim==1 and len(values)>0
+    assert values.device.type=='cuda' and probabilities.device==values.device
+    assert torch.all((probabilities>=0)&(probabilities<=1))
+    ordered=values.sort().values
+    indices=torch.ceil(probabilities*len(values)).long().clamp(1,len(values))-1
+    return ordered[indices]
+def sign_flip_pvalues(daily_differences):
+    """Enumerate the exact sign-symmetric null for independent paired days."""
+    assert daily_differences.ndim==1 and daily_differences.device.type=='cuda'
+    n=len(daily_differences)
+    code=torch.arange(2**n,device=daily_differences.device)
+    bit=torch.arange(n,device=daily_differences.device)
+    signs=(((code[:,None]>>bit)&1)*2-1).to(daily_differences.dtype)
+    simulated=(signs*daily_differences).mean(1);observed=daily_differences.mean()
+    one=(simulated>=observed-1e-12).double().mean()
+    two=(simulated.abs()>=observed.abs()-1e-12).double().mean()
+    return one,two

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