Release Laxmi 0.1.0 experimental controller package

.gitattributes

@@ -33,3 +33,4 @@ 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
+runtime/laxmi_readout_runtime_capsule-0.1.0-py3-none-macosx_11_0_arm64.whl filter=lfs diff=lfs merge=lfs -text

BENCHMARK.md

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+# Experimental release benchmark proposal
+
+29 September 2026. This is a proposal; no new comparative benchmark, training or Jev/Laya API calls have been run.
+Comparator identities provisionally inferred: TypeSafe Jev and Convai Innovations
+Laya; identities taken from primary project documentation. Freeze versions and adapters before runs.
+
+## Three distinct reports
+
+1. Installation/replay: verify each of the four retained candidates from the exact
+   staged release, against approved examples. Report platform, command, hashes,
+   restore/prediction parity and failures. This is release usability, not accuracy.
+2. Exposed development: retain the September 16 negative result unchanged. Score
+   all four candidates and available simple baselines on identical admitted cases.
+   Do not describe reused examples, new seeds or descendant configurations as an
+   independent acceptance set.
+3. Prospective task comparison: only after live input reconstruction and observation
+   boundaries are qualified, freeze a new case matrix and save each forecast before
+   either branch executes. Label within-family development separately from future
+   independent implementation transfer. Do not block an honest experimental release
+   on completion of this new scientific study.
+
+## Shared question
+
+Under identical pre-action evidence and declared conditions, does the proposed
+guard edit alter recovery/completion at the second scheduled opportunity (H2)?
+Record each branch's head state, phase, terminal and completion presence. Use an
+explicit technical-effect target; a changed outcome is not automatically harm.
+H1 and H4/H8 are separately reported horizons, not pooled extra independent cases.
+
+Initially score shared per-branch endpoint questions and, where supported, their
+probabilities. Derive a paired change label only through a frozen adapter. Separate
+per-branch marginals do not determine the probability that counterfactual branches
+differ: a joint/coupling assumption or directly predicted effect is required.
+Never multiply marginal probabilities silently to invent that probability.
+
+## Comparator arms and equal evidence
+
+- All four retained Laxmi candidates; no selection using the comparison test set.
+- Persistence/current-state baseline; majority/prior baseline fitted on development
+  data only; applicable source rules with explicit refusal outside their premises.
+- Exact execution reference for outcome labels. A comparator that executes the
+  tested future is an oracle, not a same-information pre-action predictor.
+- Frozen Jev version through its official interface, when access/cost is resolved.
+- Laya base checkpoint and typed-decisions checkpoint as separate zero-shot arms.
+  A later domain-fitted Laya arm is separate and shares training examples/budget
+  with other fitted candidates; do not mix fitted and zero-shot scores.
+
+For each arm derive a canonical pre-action evidence packet from source, state,
+history, budget, edit and declared conditions. Exclude future fault firing, outcomes,
+case names revealing labels, truth files and runtime secrets. Native typed inputs
+and external text serialization must represent the same facts. Publish adapter
+hashes and information losses. Record token limits and truncation; refuse a fair
+comparison if required evidence cannot fit. No external transmission of uncleared
+source/data. Output schemas differ; score only their shared supported questions.
+Do not assert Jev/Laya joint forecasts or count-head support without testing it.
+
+## Case construction and truth
+
+Freeze case definitions and sampling before predictions. Include supported no-op,
+effect-producing edits, timing/acknowledgement conditions and missing-observation
+cases. Conditions beyond a frozen candidate's contract belong in an admission/
+refusal test, not its predictive-accuracy denominator. New conditions require a
+separate candidate contract. Counterfactual branches share starting snapshots and
+declared exogenous schedules; save actual executed conditions and failures.
+
+Ground truth comes from native branch observations, not another model's answers.
+Unobserved invocation/endpoints remain censored, never fabricated zero. Count and
+report censoring per arm/case. Keep related trajectories/templates/implementation
+ancestries together when splitting and computing uncertainty. No row-level random
+split across descendants. Label domain limits and absent transfer evidence.
+
+Choose sample size after a pilot estimates prevalence, paired differences and
+effective independent groups. A small engineering screen can find bugs; it cannot
+justify strong accuracy or safety claims. Freeze a finite run/cost cap before
+collection and do not expand sampling opportunistically after inspecting scores.
+
+## Scoreboard
+
+- Coverage/admission/refusal: all attempted cases and reasons; accuracy among
+  answered cases plus risk versus coverage. Failures must not disappear.
+- Effect classification: balanced accuracy, per-class recall/precision, misses of
+  actual effects and false alarms. Avoid calling technical effects harmful without
+  a reviewer-use contract.
+- Endpoints: per-field accuracy and exact joint accuracy only for models that
+  actually provide the joint task. Report each horizon separately.
+- Probabilities: Brier/log loss for the shared supported event, reliability plots,
+  and raw versus development-calibrated results separately. Fit calibration only
+  on development data; small-set ECE is not a calibration guarantee.
+- Controlled pairs: both branches correct, effect direction, no-op invariance;
+  add option order/key changes and semantically equivalent serializations to
+  detect presentation shortcuts.
+- Resources: end-to-end p50/p95 including serialization, network for APIs, device,
+  dtype, batch size, cold/warm timing, peak memory and actual paid cost. Local
+  inference uses hardware/electricity and is not economically free.
+- Uncertainty: paired differences with intervals grouped by independent ancestry
+  or episode, disclosed group/sample counts and prevalence; no unjustified winner
+  from point estimates alone. Predeclare the primary metric and secondary metrics.
+
+## Publication and iteration
+
+First release: publish replay verification and existing negative development
+results, with this prospective comparison clearly marked not run. After a run,
+publish safe cases if rights permit, evaluator/adapters, version pins, raw safe
+predictions, aggregate results and failure/censoring counts. Preserve release tags.
+Public benchmark cases become development material after exposure. Retain a fresh
+sealed test set for later acceptance; do not repeatedly optimize a public leaderboard
+and call the result independent generalization. Reviewer-use pass/fail limits remain
+to be agreed separately; no thresholds are inferred from current failures.
+
+## Primary comparison references
+
+https://typesafe.ai/blog/introducing-system-one-models-and-jev
+https://huggingface.co/convaiinnovations/laya
+https://github.com/NandhaKishorM/laya
+
+These describe external interfaces and author-reported results. They do not measure
+Jev/Laya on Laxmi inputs or establish a cross-project performance ranking.

CHANGELOG.md

@@ -0,0 +1,7 @@
+# Changelog
+
+## 0.1.0 — 29 September 2026
+
+First public experimental controller package. Retains all four original September 15–16 candidates without selecting a winner or changing weights. Includes the negative development result, a smaller reviewed source closure, Apache 2.0 attribution, and a retained synthetic H1 installation/replay example.
+
+The installed entry wrapper checks Python 3.12 compatibility instead of requiring an exact interpreter compiler/build string. The release still advertises only the verified macOS ARM64 installation scope. Original checkpoint source locks remain unchanged. No new training or prospective predictive-quality evaluation is claimed.

CITATION.cff

@@ -0,0 +1,17 @@
+cff-version: 1.2.0
+message: "If you use Laxmi in research, please cite this project."
+type: software
+title: "Laxmi: Experimental Software-State Prediction"
+authors:
+  - family-names: Prabhakar
+    given-names: Navneet
+repository-code: "https://huggingface.co/Executespec/laxmi-controller-experimental"
+url: "https://huggingface.co/Executespec/laxmi-controller-experimental"
+license: Apache-2.0
+abstract: >-
+  An experimental research project exploring predictions of software-state
+  consequences from observed state, history, actions and proposed code changes.
+  Controlled experiments and reproducibility infrastructure do not establish
+  general software-engineering capability or production suitability.
+version: "0.1.0"
+date-released: 2026-09-29

LICENSE

@@ -0,0 +1,202 @@
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LICENSE_SCOPE.md

@@ -0,0 +1,38 @@
+# Licensing scope
+
+Copyright 2026 Navneet Prabhakar.
+
+Navneet Prabhakar's original Laxmi source code, documentation, configuration,
+schemas and authored test fixtures are licensed under Apache License 2.0,
+unless an individual file explicitly states different terms. LICENSE contains
+the unmodified license text; NOTICE records project attribution. Future accepted
+contributions follow the license's contribution terms unless separately agreed.
+
+Apache 2.0 is also the selected license for original Laxmi model weights shipped
+in an explicitly identified release after provenance and redistribution review.
+Every model release must identify its exact weights and applicable terms. The
+presence of a local checkpoint does not alone establish its release eligibility.
+
+The repository license does not relicense third-party source, dependencies,
+models, tokenizers, copied documents or other third-party material. Their existing
+terms and required notices remain applicable. Incorporating or linking a dependency
+does not claim its authorship or change its license.
+
+Acquired corpora, datasets, repository clones, training/evaluation records,
+operational archives and generated evidence under data/, results/, archive/ and
+the corresponding code_review/ directories are not granted redistribution rights
+by this repository license alone. Individual released datasets or artifact bundles
+require an explicit scope manifest and their applicable terms. Original source or
+documentation in those directories may be expressly included in such a manifest.
+Private inputs, credentials and operational records are not public release assets.
+
+## Attribution and citation
+
+Redistributors must comply with Apache 2.0's license and applicable notice
+preservation requirements. The NOTICE file provides Laxmi's attribution. It adds
+no requirement to display a badge, advertise the author or obtain permission for
+permitted uses. CITATION.cff makes a requested research citation convenient;
+citation is not an additional condition of the license.
+
+Navneet Prabhakar remains Laxmi's original creator. Contributor lists should
+reflect later accepted contributions if the project gains other contributors.

NOTICE

@@ -0,0 +1,12 @@
+Laxmi
+Copyright 2026 Navneet Prabhakar
+
+Created and maintained by Navneet Prabhakar.
+Project: https://github.com/navneetprabhakar/laxmi
+
+Laxmi's original contributions are licensed under the Apache License,
+Version 2.0. See LICENSE and LICENSE_SCOPE.md for applicable scope.
+
+Third-party libraries, models and other materials retain their respective
+copyrights, licenses and attribution requirements. This notice does not
+claim authorship of those materials or imply their authors' endorsement.

README.md

@@ -0,0 +1,108 @@
+---
+license: apache-2.0
+library_name: pytorch
+tags:
+  - software-state-prediction
+  - experimental
+  - negative-results
+  - custom-model
+---
+
+# Laxmi — experimental software-state prediction
+
+**Predict the consequences of a software change before executing it.**
+
+Created and maintained by **Navneet Prabhakar**, creator and sole contributor to Laxmi. Third-party libraries retain their authorship and licenses.
+
+Laxmi investigates whether observed software state, history, an action and a proposed code change can predict what happens next. This first release makes a bounded controller experiment inspectable and runnable. It is not a chat model, a general code-review assistant or an autonomous software engineer.
+
+> **Version 0.1.0 · Experimental research release**
+>
+> The consumer works on the verified installation scope. The latest model repair failed its improvement test. All four candidates are retained; none is promoted as an accepted model.
+
+## The experiment
+
+A controller records progress and retries failed operations. An edit to its recovery logic can delay completion or repeat work. The research question is whether pre-action evidence can predict that consequence.
+
+```text
+State + history + source/edit + declared conditions
+                         ↓
+                  Save a prediction
+                         ↓
+              Execute a controlled experiment
+                         ↓
+             Compare with observed outcomes
+```
+
+The released consumer forecasts admitted synthetic controller conditions. It can observe, predict, compare proposals, save/restore state and score separately recorded outcomes. It never dispatches native controller actions. Recording a forecast with this consumer alone does not authenticate prospective ordering.
+
+## Results, including the failures
+
+| Seed | Original H1 exact outcomes | Normalized H1 exact outcomes | Changed pairs both correct, either readout |
+|---|---:|---:|---:|
+| 17 | 8 / 24 | 8 / 24 | 0 / 6 |
+| 43 | 4 / 24 | 4 / 24 | 0 / 6 |
+
+Readout normalization did not improve the task. These are exposed development results from September 15–16, 2026, not independent acceptance scores. Longer-horizon likelihood comparisons remain unresolved. Probabilities are not established as calibrated, and independent implementation transfer remains open.
+
+The packaged example checks installation and replay. It must not be presented as a fresh benchmark. See [verification.json](verification.json) for the exact smoke scope and [BENCHMARK.md](BENCHMARK.md) for the next comparison design.
+
+## Download and run
+
+**Verified platform: Apple Silicon macOS, Python 3.12.11.** The wheel is macOS ARM64 tagged. Other Python 3.12 patch builds and platforms have not been verified. The source archive is provided for inspection and experimentation; it does not establish Linux support. No GPU is required for the example.
+
+With the Hugging Face `hf` CLI installed:
+
+```bash
+hf download Executespec/laxmi-controller-experimental --revision v0.1.0 --local-dir laxmi-release
+cd laxmi-release
+shasum -a 256 -c SHA256SUMS
+python3.12 -m venv .venv
+.venv/bin/python -m pip install runtime/laxmi_readout_runtime_capsule-0.1.0-py3-none-macosx_11_0_arm64.whl
+.venv/bin/python -I -B verify_release.py --release . --python "$PWD/.venv/bin/python" --output smoke-17-original --candidate 17-original
+```
+
+Expected completion: `17-original: all six operations passed`. The output directory must be new. The check observes the example, predicts, compares, restores, saves and scores an outcome. Omit `--candidate` to check all four candidates. The verifier makes candidate/example files private (`0600`) as required by the runtime. It writes local snapshots and logs; it does not run the controlled native software or train a model.
+
+The wheel declares exact dependency versions, also recorded in `requirements-macos-arm64-py312.txt`. Dependencies are installed separately; they are not bundled. Version pins are not a dependency artifact hash lock. The source package includes the runtime source and license files. No optimizer checkpoint is shipped.
+
+## Package layout
+
+| Path | Contents |
+|---|---|
+| `candidates/17-original/`, `17-normalized/`, `43-original/`, `43-normalized/` | Original retained safetensors weights, configuration, manifests and matching consumer metadata |
+| `runtime/` | Reviewed runtime wheel and source ZIP |
+| `examples/` | One retained synthetic observation, proposal comparison and outcome template |
+| `verify_release.py` | Installation/replay check using only this package |
+| `release.json`, `SHA256SUMS` | Candidate identity, runtime identity and file integrity |
+| `verification.json` | Sanitized installation/replay evidence |
+| `LICENSE`, `NOTICE`, `LICENSE_SCOPE.md`, `RELEASE_SCOPE.md` | Apache 2.0, attribution and exact distribution scope |
+| `CITATION.cff` | Suggested research citation |
+
+Weights, configuration, readout metadata and runtime must match. Original and normalized readouts share tensor layouts but differ in computation. Do not mix them. Existing candidate metadata intentionally retains `acceptance=false` and `release_qualified=false`; those fields record scientific qualification, not whether the experimental package has been uploaded.
+
+## Known boundaries
+
+- The input contract is bounded synthetic controller evidence. Do not relabel real executions to pass admission.
+- No arbitrary-PR understanding, patch generation, autonomous engineering or production suitability is established.
+- Missing observations remain unknown rather than fabricated zero counts.
+- Internal hashes verify consistency, not source ownership, event absence or wall-clock ordering.
+- Longer rollouts may contain unresolved/pruned probability mass; it is not silently renormalized away.
+- Supported consumer horizons are 1, 2, 4 and 8; this release's installation smoke covers H1 only.
+- The learned probabilities are not proven calibrated and do not authorize external actions.
+
+## Benchmark and iteration
+
+A future benchmark will compare identical pre-action evidence against native outcomes, with persistence/prior/source-rule baselines and Jev/Laya on shared questions. **No Jev/Laya comparison has been run.** Base versus fitted checkpoints will be separate arms. Public example replay is distinct from fresh prospective accuracy or independent transfer.
+
+Later versions should pin source, schema, runtime, weights, data exposure and results together. This version remains available as a baseline, including its negative result. See [CHANGELOG.md](CHANGELOG.md).
+
+## Author, attribution and license
+
+**Navneet Prabhakar** — creator, maintainer and sole contributor to Laxmi.
+
+[GitHub profile](https://github.com/navneetprabhakar)
+
+Original components explicitly listed in [RELEASE_SCOPE.md](RELEASE_SCOPE.md) are released under [Apache License 2.0](LICENSE). Preserve applicable attribution and license notices when redistributing; see [NOTICE](NOTICE). Third-party dependencies retain their own terms. This release does not distribute the full corpus or private operational archives.
+
+If you use Laxmi in research, please cite [CITATION.cff](CITATION.cff). Citation is requested, not an additional license condition.

RELEASE_SCOPE.md

@@ -0,0 +1,11 @@
+# Exact scope of this experimental distribution
+
+Copyright 2026 Navneet Prabhakar. Apache License 2.0 applies to the original Laxmi code, documentation, metadata, four original model candidates and authored synthetic examples explicitly included in this distribution and enumerated by SHA256SUMS.
+
+The included example is retained synthetic development evidence from the owned controller experiment. It includes representations of original controller methods, not downloaded corpus code. Model weights are the original experiment's inference-only exports. No third-party base-model weights, tokenizers, training corpora, optimizer state, private tests, credentials, native stores or operational archives are included.
+
+The runtime closure consists of original Laxmi source files extracted byte-for-byte from the retained runtime wheel; source-lock fingerprints are preserved. The installed entry wrapper changes only Python compatibility checking from an exact compiler/build string to the Python 3.12 major/minor pair. The new package manifest identifies this wrapper and the selected source closure. Its numerical outputs must match retained reference forecasts in the declared installation check.
+
+Dependencies such as PyTorch, NumPy, safetensors and jsonschema are referenced as installation requirements and are not bundled. Their licenses and copyright holders remain applicable; Laxmi does not claim their authorship. The source ZIP contains the package manifest, Python sources, required original configuration/schema, build files and Laxmi license/notice files.
+
+Repository-wide datasets and other artifacts outside this distribution receive no new redistribution grant from this release. Scientific model qualification remains open despite publication. License selection and distribution permission do not establish model accuracy or independence of evaluation.

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candidates/17-original/inference/manifest.json

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candidates/43-normalized/consumer.json

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candidates/43-normalized/inference/config.json

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candidates/43-normalized/inference/manifest.json

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candidates/43-original/inference/config.json

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candidates/43-original/inference/manifest.json

@@ -0,0 +1 @@
+{"count_semantics":{"fields":["executor_starts_delta","settlement_calls_delta"],"parameterization":"log_rate","schema_version":"laxmi.controller-count-head.v4","semantics":"endpoint_step_sample_bracket_deltas_not_cumulative_through_horizon","state_dim":32},"files":{"config.json":"3852d096e8d9f07d37ee950c24be35b4805fdca47a21b0b1f2ae105f227f2c1c","model.safetensors":"604d14c55256a7dd3c8f56072eda97fd7517912bd4d965d416d76040428261cb"},"manifest_sha256":"f722d99a0fc7eabe96171fdb05228a781ef30d955b32d554be62949c3e1eb3ec","optimizer_state_included":false,"schema_version":"laxmi.controller-local-source-inference-checkpoint.v3","support_sha256":"a1936a04a13ce10925e569a6286c3378a58d7a7e47dcb1f1517bfcc3c45d7e1f","training_qualification_verified":false}

candidates/43-original/inference/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:604d14c55256a7dd3c8f56072eda97fd7517912bd4d965d416d76040428261cb
+size 304404

examples/outcome-template.json

@@ -0,0 +1,48 @@
+{
+  "horizon": 1,
+  "input_file_sha256": "2032524a11a31ff92634369060ff5a41adc6bc993161d6eb47a25a4839756ad1",
+  "prediction_file_sha256": "12c51a48828b37f225eecb19714be8fbd521cbec83b052eb45d065253cd8f723",
+  "schema_version": "laxmi.experimental-observed-endpoint.v1",
+  "targets": {
+    "checkpoint_reference_present": {
+      "status": "observed",
+      "value": true
+    },
+    "checkpoint_verified": {
+      "status": "observed",
+      "value": true
+    },
+    "executor_starts_delta": {
+      "status": "observed",
+      "value": 0
+    },
+    "next_completed_present": {
+      "status": "observed",
+      "value": true
+    },
+    "next_preparation_present": {
+      "status": "observed",
+      "value": true
+    },
+    "next_private_execution_complete_present": {
+      "status": "observed",
+      "value": false
+    },
+    "phase": {
+      "status": "observed",
+      "value": 0
+    },
+    "settlement_calls_delta": {
+      "status": "observed",
+      "value": 0
+    },
+    "state": {
+      "status": "observed",
+      "value": "CUSTODY_INITIALIZED"
+    },
+    "terminal": {
+      "status": "observed",
+      "value": false
+    }
+  }
+}

release.json

@@ -0,0 +1,33 @@
+{
+  "author": "Navneet Prabhakar",
+  "candidates": {
+    "17-normalized": {
+      "metadata_sha256": "225a71bb67d619e70f1aec0313d2c5791d0a94ac3942297ad12ba284269cddd8",
+      "example_forecast_sha256": "e4c9661abcc03fe8aee0ae5bf57e1b12f9e8a56becc67e41d26ea106b0ae5267"
+    },
+    "17-original": {
+      "metadata_sha256": "a958ceb64e802decf331d10e98ddfcdc042658834469a0cc4a131e44134de906",
+      "example_forecast_sha256": "5cca70b946ccaac1a453f899fc54e088452728a24ae9e6ba7f5f2e8ee5b6e6e6"
+    },
+    "43-normalized": {
+      "metadata_sha256": "8eb622127bb5a9d739967855b8d7d8b7290395d7ae3a83315325b1a20869146f",
+      "example_forecast_sha256": "cbe28210ab8bce43ebe5223b38c62fdcd07cf796d26173651da03fd32c2135e8"
+    },
+    "43-original": {
+      "metadata_sha256": "4e02bd6724288e074fb30f04164a83559eff5e370e94bc473ace3ac22bc7a742",
+      "example_forecast_sha256": "c18c7cbb05b59973026e370a6ba8bfb2e35f1e9d772ff183a32dec94da6ccc93"
+    }
+  },
+  "clean_install_verified": true,
+  "dependency_hash_lock": false,
+  "license": "Apache-2.0",
+  "model_acceptance": false,
+  "original_wheel_sha256": "e1bafeb8d72e231a3029cf11ffabb8e54aa28e1b953040e81e77cf1fa3ac270d",
+  "package_sha256": "a74db8e3196267387e3a4cfb55b838d112a86c88adac031f6515b88230a29fcc",
+  "runtime_sources": 133,
+  "schema_version": "laxmi.experimental-public-package.v1",
+  "supported_platform": "macOS ARM64, Python 3.12; other platforms unverified",
+  "version": "0.1.0",
+  "original_experiment_source_commit": "4dae1a041be532d663f477effe9b654b428bc04e",
+  "distribution_scope": "RELEASE_SCOPE.md; exact files enumerated by SHA256SUMS"
+}

requirements-macos-arm64-py312.txt

@@ -0,0 +1,22 @@
+# Installed dependency closure snapshot, not a hash-locked release requirement.
+# Python 3.12.11 (main, Aug 18 2025, 19:02:39) [Clang 20.1.4 ]
+# Darwin arm64; markers evaluated for this platform with no optional extras.
+# Roots: torch, numpy, safetensors, jsonschema. Captured 2026-09-10.
+# Do not use for Linux/GPU provisioning or claim reproducible wheel provenance.
+attrs==26.1.0
+filelock==3.29.6
+fsspec==2026.6.0
+jinja2==3.1.6
+jsonschema==4.26.0
+jsonschema-specifications==2025.9.1
+markupsafe==3.0.3
+mpmath==1.3.0
+networkx==3.6.1
+numpy==2.5.1
+referencing==0.37.0
+rpds-py==2026.6.3
+safetensors==0.8.0
+setuptools==81.0.0
+sympy==1.14.0
+torch==2.12.1
+typing-extensions==4.16.0

runtime/laxmi-readout-runtime-capsule-0.1.0-source.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67517f7c692d81580248e994c61829ddfdf2f9ef29358dd71a6b17eb953ef99f
+size 339677

runtime/laxmi_readout_runtime_capsule-0.1.0-py3-none-macosx_11_0_arm64.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47d03da88fa9bd9c1b4573ac4a0d5cb36c2cd1d106d687b68aa2968b713ee9b3
+size 346135

verification.json

@@ -0,0 +1,33 @@
+{
+  "status": "passed",
+  "platform": "macOS ARM64, Python 3.12; inspect interpreter for exact patch version",
+  "scope": "one retained synthetic depth-1 H1 example per candidate; installation/replay only",
+  "records": [
+    {
+      "arm": "17-normalized",
+      "observe_predict_compare_restore_save_score": true,
+      "exact_retained_forecast": true,
+      "forecast_sha256": "e4c9661abcc03fe8aee0ae5bf57e1b12f9e8a56becc67e41d26ea106b0ae5267"
+    },
+    {
+      "arm": "17-original",
+      "observe_predict_compare_restore_save_score": true,
+      "exact_retained_forecast": true,
+      "forecast_sha256": "5cca70b946ccaac1a453f899fc54e088452728a24ae9e6ba7f5f2e8ee5b6e6e6"
+    },
+    {
+      "arm": "43-normalized",
+      "observe_predict_compare_restore_save_score": true,
+      "exact_retained_forecast": true,
+      "forecast_sha256": "cbe28210ab8bce43ebe5223b38c62fdcd07cf796d26173651da03fd32c2135e8"
+    },
+    {
+      "arm": "43-original",
+      "observe_predict_compare_restore_save_score": true,
+      "exact_retained_forecast": true,
+      "forecast_sha256": "c18c7cbb05b59973026e370a6ba8bfb2e35f1e9d772ff183a32dec94da6ccc93"
+    }
+  ],
+  "model_acceptance": false,
+  "dependency_hash_lock": false
+}

verify_release.py

@@ -0,0 +1,97 @@
+"""Check only staged public artifacts in an installed runtime, with no training."""
+
+import argparse
+import hashlib
+import json
+import os
+from pathlib import Path
+import subprocess
+
+
+def sha(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def verify(release, python, output, reference=None, candidate_name=None):
+    release = release.resolve()
+    output.mkdir(parents=True, exist_ok=False)
+    receipt = json.loads((release / "release.json").read_text())
+    env = dict(os.environ)
+    for key in ("PYTHONPATH", "PYTHONHOME", "VIRTUAL_ENV"):
+        env.pop(key, None)
+    env.update(PYTHONDONTWRITEBYTECODE="1", OMP_NUM_THREADS="1", MKL_NUM_THREADS="1")
+    results = []
+    for arm, metadata in receipt["candidates"].items():
+        if candidate_name is not None and arm != candidate_name:
+            continue
+        local = output / arm
+        local.mkdir()
+        candidate = release / "candidates" / arm
+        for p in candidate.rglob("*"):
+            if p.is_file():
+                p.chmod(0o600)
+        for p in (release / "examples").iterdir():
+            p.chmod(0o600)
+        assert sha(candidate / "consumer.json") == metadata["metadata_sha256"]
+        base = [str(python), "-I", "-B", "-m", "laxmi_readout_runtime_v1",
+                "--expected-package-sha256", receipt["package_sha256"],
+                "--checkpoint", str(candidate / "inference"), "--metadata", str(candidate / "consumer.json"),
+                "--metadata-sha256", metadata["metadata_sha256"]]
+
+        def run(name, args):
+            value = subprocess.run(base + args, cwd=output, env=env, capture_output=True, timeout=120)
+            (local / f"{name}.stderr").write_bytes(value.stderr)
+            log = local / f"{name}.json"
+            log.write_bytes(value.stdout)
+            log.chmod(0o600)
+            if value.returncode:
+                raise RuntimeError(f"{arm}/{name} failed: " + value.stderr.decode()[-1500:])
+            return json.loads(value.stdout)
+
+        state = local / "state.json"
+        run("observe", ["observe", "--input", str(release / "examples/input.json"), "--out", str(state)])
+        original = state.read_bytes()
+        pred = run("predict", ["predict", "--state", str(state), "--input", str(release / "examples/input.json")])
+        compared = run("compare", ["compare", "--state", str(state), "--input", str(release / "examples/compare.json")])
+        assert pred["forecast"] == compared["forecast"][0]
+        run("restore", ["restore-state", "--state", str(state)])
+        run("save", ["save-state", "--state", str(state), "--out", str(local / "saved.json")])
+        assert state.read_bytes() == original == (local / "saved.json").read_bytes()
+        outcome = json.loads((release / "examples/outcome-template.json").read_text())
+        outcome["prediction_file_sha256"] = sha(local / "predict.json")
+        outcome["input_file_sha256"] = sha(release / "examples/input.json")
+        target = local / "outcome.json"
+        target.write_text(json.dumps(outcome))
+        target.chmod(0o600)
+        run("score", ["score-outcome", "--state", str(state), "--prediction", str(local / "predict.json"),
+                      "--outcome", str(target), "--out", str(local / "comparison.json")])
+        exact = None
+        if reference:
+            retained = json.loads((reference / f"{arm}-predict.log").read_text())
+            assert pred["forecast"] == retained["forecast"]
+            exact = True
+        forecast_digest = hashlib.sha256(json.dumps(pred["forecast"], sort_keys=True).encode()).hexdigest()
+        if "example_forecast_sha256" in metadata:
+            assert forecast_digest == metadata["example_forecast_sha256"]
+        results.append(dict(arm=arm, observe_predict_compare_restore_save_score=True,
+                            exact_retained_forecast=exact,
+                            forecast_sha256=forecast_digest))
+        print(arm + ": all six operations passed", flush=True)
+    if not results:
+        raise ValueError("candidate not present in this release")
+    summary = dict(status="passed", platform="macOS ARM64, Python 3.12; inspect interpreter for exact patch version",
+                   scope="one retained synthetic depth-1 H1 example per candidate; installation/replay only",
+                   records=results, model_acceptance=False, dependency_hash_lock=False)
+    (output / "verification.json").write_text(json.dumps(summary, indent=2) + "\n")
+    return summary
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--release", type=Path, required=True)
+    parser.add_argument("--python", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--reference", type=Path)
+    parser.add_argument("--candidate", choices=("17-original", "17-normalized", "43-original", "43-normalized"))
+    args = parser.parse_args()
+    verify(args.release, args.python.absolute(), args.output.resolve(), args.reference, args.candidate)