Publish szl-lambda-gate v1 from szl-holdings/szl-lambda-gate@219ee89500f2d2a137f547c1c6ed8d97c6c66e9d

Source: https://github.com/szl-holdings/szl-lambda-gate/tree/219ee89500f2d2a137f547c1c6ed8d97c6c66e9d
Publisher: scripts/szl_kernel_publish.py (https://github.com/szl-holdings/szl-lambda-gate/actions/runs/38478441021)

Files changed (13) hide show

LICENSE +185 -2
README.md +105 -172
SZL_KERNEL_SOURCE.json +49 -0
build/torch-cpu/__init__.py +0 -2
build/torch-cpu/metadata.json +26 -1
build/torch-cpu/szl_lambda_gate/__init__.py +1 -1
build/torch-cpu/szl_lambda_gate/governed_norm/_receipt.py +101 -17
build/torch-universal/__init__.py +0 -2
build/torch-universal/metadata.json +26 -1
build/torch-universal/szl_lambda_gate/__init__.py +1 -1
build/torch-universal/szl_lambda_gate/governed_norm/_receipt.py +101 -17
distribution.json +0 -1
source/LICENSE +0 -202

LICENSE CHANGED Viewed

@@ -1,7 +1,192 @@
                                  Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
    Copyright 2026 SZL Holdings
    Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,5 +200,3 @@
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-   The full Apache License 2.0 text is available at the URL above.

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README.md CHANGED Viewed

@@ -1,172 +1,105 @@
----
-tags:
-- kernel
-- governance
-- lambda
-- gate
-- provenance
-- doi:10.5281/zenodo.19944926
-library_name: kernels
-license: apache-2.0
----
-<p align="center">
-  <a href="https://huggingface.co/SZLHOLDINGS/szl-lambda-gate/tree/main/build/torch-universal/szl_lambda_gate"><img src="https://img.shields.io/badge/kernel%20hub-torch--universal-5b8dee?style=flat-square" alt="kernel hub"></a>
-  <a href="https://huggingface.co/SZLHOLDINGS/szl-lambda-gate/blob/main/MODEL_PROVENANCE.json"><img src="https://img.shields.io/badge/provenance-MODEL_PROVENANCE.json-3af4c8?style=flat-square" alt="provenance"></a>
-  <a href="https://huggingface.co/SZLHOLDINGS/szl-lambda-gate/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-7e8aa3?style=flat-square" alt="license"></a>
-</p>
-# szl-lambda-gate
-**Λ — a governance aggregator as a Hugging Face kernel.** A differentiable, torch.compile-friendly weighted-geometric-mean aggregator with an ADVISORY non-compensatory gate and runtime axiom self-checks, from [SZL Holdings](https://huggingface.co/SZLHOLDINGS).
-> Companion to [`szl-governed-norm`](https://huggingface.co/SZLHOLDINGS/szl-governed-norm). Where that kernel makes a normalization *auditable*, this one makes a *governance decision* computable and checkable at the tensor layer.
-## Interactive demo
-> **Live demos (in-browser, nothing to install)** — [`lambda-gate-holo`](https://szlholdings-lambda-gate-holo.static.hf.space) (this kernel's holographic gate demo) · [`szl-kernels-live`](https://szlholdings-szl-kernels-live.static.hf.space) (unified suite demo).
->
-> The quickstart above runs fully locally. For a full governed-kernel suite demo, see [szl-kernels](https://huggingface.co/SZLHOLDINGS/szl-kernels). For the live a11oy substrate, see [a11oy Space](https://huggingface.co/spaces/SZLHOLDINGS/a11oy).
-## What Λ is — and is NOT (read this first)
-Λ is the **weighted geometric mean** over axis scores in [0,1]:
-\[ \Lambda(x) = \prod_i x_i^{w_i}, \quad \sum_i w_i = 1, \; w_i > 0, \; x_i \in [0,1] \]
-It is a **non-compensatory, ADVISORY** roll-up: any single zeroed (or non-finite) axis drives the whole aggregate to 0 — a conservative "one bad axis fails the gate" signal. **Λ is NOT "proven trust" and NOT a closed theorem.** Its *uniqueness* (that the weighted geometric mean is the only aggregator satisfying the carried axioms) remains **Conjecture 1 — OPEN**. A gate "pass" is an advisory signal, never a guarantee. We label this honestly everywhere.
-## Quickstart
-```bash
-pip install kernels torch
-```
-```python
-import torch
-from kernels import get_kernel
-# Current `kernels` (>=0.15) requires an explicit revision/version + trust flag for org kernels:
-lg = get_kernel("SZLHOLDINGS/szl-lambda-gate", revision="main", trust_remote_code=True)
-# (once a tag is published you can pin it, e.g. revision="v0.2.0")
-axes = torch.tensor([0.9, 0.8, 0.95])      # axis scores in [0,1]
-score = lg.lambda_aggregate(axes)          # Λ(x) ∈ [0,1]
-res   = lg.lambda_gate(axes, threshold=0.5)
-print(res.score, res.passed, res.advisory) # advisory is always True
-print(lg.selfcheck())                      # empirical A1–A4 checks + version
-```
-## API
-| Function | Notes |
-|---|---|
-| `lambda_aggregate(axes, weights=None)` | Λ over the last dim. Differentiable, batched, torch.compile-friendly. |
-| `lambda_gate(axes, weights=None, threshold=0.5)` | Advisory gate → `LambdaGateResult(score, passed, threshold, advisory)`. |
-| `lambda_gate_batch(candidates, weights=None, threshold=0.5)` | Score many candidate vectors `(..., N, k)` in one call; returns the advisory pass mask. |
-| `selfcheck()` | Empirical A1–A4 axiom checks + adversarial falsification search + version. NOT a uniqueness proof. |
-| `is_monotone / is_homogeneous / is_egyptian_exact / is_bounded_by_max` | The four carried axioms as real runtime checks. |
-| `yuyay_weights()`, `YUYAY_AXES`, `YUYAY_FLOORS` | Canonical 13-axis Yuyay preset (advisory). |
-| layers: `LambdaGate`, `LambdaAggregate` | Pure `nn.Module` for the Kernel Hub layer-mapping mechanism. |
-## Carried axioms (verifiable, not a proof)
-- **A1 IsMonotone** — Λ is non-decreasing in each axis.
-- **A2 IsHomogeneous (deg 1)** — Λ(t·x) = t·Λ(x).
-- **A3 IsEgyptianExact** — Λ(c,…,c) = c.
-- **A4 IsBounded** — Λ(x) ≤ maxᵢ xᵢ.
-`selfcheck()` verifies these empirically on sampled inputs and runs a random falsification search. A clean run is **evidence, not proof** — Λ-uniqueness is Conjecture 1 (open).
-## Provenance
-Backed by the Lean 4 formalization [szl-holdings/lutar-lean](https://github.com/szl-holdings/lutar-lean) (749 declarations / 14 axioms / 163 tracked sorries), DOI [10.5281/zenodo.20434308](https://doi.org/10.5281/zenodo.20434308). Λ uniqueness = Conjecture 1 (open).
-## Honesty
-- Pure-Python universal kernel — a correctness reference, not a CUDA speed record. No fabricated benchmarks (50 passing tests).
-- Λ is advisory; never "proven trust."
-- Prior art honestly attributed: the weighted geometric mean as a less-compensatory composite indicator is established practice (UN HDI 2010, OECD Composite Indicators Handbook 2008); the veto/cut-off idea is ELECTRE. The 13-axis conjunctive form is SZL's own yuyay_v3 gate.
-## Compatibility
-Python 3.9+, `torch>=2.5`, standard library + torch only.
-## License
-Apache-2.0. Copyright 2026 SZL Holdings.
----
-## SZL Kernels Suite
-Part of the [`szl-kernels`](https://huggingface.co/SZLHOLDINGS/szl-kernels) governed-kernel suite — the hub links every member, and each member links back to the hub so no leaf is orphaned:
-| Kernel | Lane |
-|---|---|
-| [`szl-kernels`](https://huggingface.co/SZLHOLDINGS/szl-kernels) | **hub** — unified suite, cross-kernel `UnifiedReceiptChain` |
-| [`szl-governed-norm`](https://huggingface.co/SZLHOLDINGS/szl-governed-norm) | RMSNorm/LayerNorm + SHA3-256 receipts |
-| **`szl-lambda-gate`** (this repo) | **advisory Λ gate (Conjecture 1, OPEN)** |
-| [`governed-inference-meter`](https://huggingface.co/SZLHOLDINGS/governed-inference-meter) | MEASURED-joule energy accounting (NVML) |
-| [`szl-govsign`](https://huggingface.co/SZLHOLDINGS/szl-govsign) | signed governance attestation (DSSE / in-toto) |
-| [`szl-blocked`](https://huggingface.co/SZLHOLDINGS/szl-blocked) | honest-BLOCKED state + EU AI Act Annex IV DRAFT |
-| [`szl-provctl`](https://huggingface.co/SZLHOLDINGS/szl-provctl) | provenance-DAG verify + in-toto/SLSA interop |
-**Live Spaces:** [a11oy](https://huggingface.co/spaces/SZLHOLDINGS/a11oy) · [hatun-mcp](https://huggingface.co/spaces/SZLHOLDINGS/hatun-mcp).
-**Related — Governed Kernels collection:** [Governed Kernels — verifiable AI building blocks](https://huggingface.co/collections/SZLHOLDINGS/governed-kernels-verifiable-ai-building-blocks-6a41d3936cfce4fba83ce378) groups the whole family in one page. **Live console:** [a11oy](https://szlholdings-a11oy.hf.space) · [a-11-oy.com](https://a-11-oy.com) · [llm-router](https://szlholdings-llm-router-live.hf.space) · [receipt verifier](https://szlholdings-governed-receipt-verifier.static.hf.space) · [receipt spec (hub)](https://github.com/szl-holdings/governed-receipt-spec).
----
-<sub><b>SZL Holdings</b> · Λ governance aggregator · advisory, not proven trust · <a href="https://a-11-oy.com">a-11-oy.com</a> · <a href="https://github.com/szl-holdings">github.com/szl-holdings</a> · <a href="https://huggingface.co/SZLHOLDINGS">huggingface.co/SZLHOLDINGS</a></sub>
----
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.19944926.svg)](https://doi.org/10.5281/zenodo.19944926)
-## Citation
-**Cite this.** Part of the SZL Holdings *Ouroboros Thesis* (Governed Post-Determinism).
-Concept DOI (always-latest): [10.5281/zenodo.19944926](https://doi.org/10.5281/zenodo.19944926).
-Author: Stephen P. Lutar Jr. · [ORCID 0009-0001-0110-4173](https://orcid.org/0009-0001-0110-4173) · License CC-BY-4.0.
-Full DOI-pinned lineage (v1→v26) + the 8 papers: [szl-papers PAPERS_INDEX](https://github.com/szl-holdings/szl-papers/blob/main/PAPERS_INDEX.md).
-No artifact-specific DOI is minted for this model; the concept DOI above covers the program.
-Honesty (Doctrine v11): Λ unconditional uniqueness is **Conjecture 1** (machine-checked FALSE as stated) — never a theorem; conditional uniqueness is **Theorem U** (axiom-free). Locked-proven formulas = **exactly 8** {F1,F4,F7,F11,F12,F18,F19,F22}; ~185 experimental theorems are a separate CI-green tier; Khipu BFT safety = Conjecture 2. Trust never 100%.
-```bibtex
-@misc{lutar_szl_ouroboros,
-  author    = {Lutar, Stephen P., Jr.},
-  title     = {SZL Holdings --- The Ouroboros Thesis (Governed Post-Determinism)},
-  year      = {2026},
-  publisher = {Zenodo},
-  doi       = {10.5281/zenodo.19944926},
-  url       = {https://doi.org/10.5281/zenodo.19944926},
-  note      = {Concept DOI --- always resolves to the latest version. ORCID 0009-0001-0110-4173. CC-BY-4.0.}
-}
-```
-*Signed-off-by: Stephen Lutar <stephenlutar2@gmail.com>*
-## Files in this repo
-| Path | What it is |
-|---|---|
-| `build/torch-universal/szl_lambda_gate/__init__.py` | public API — `lambda_aggregate`, `lambda_gate`, `selfcheck()` |
-| `build/torch-universal/szl_lambda_gate/_lambda.py` | the weighted-geometric-mean aggregator + A1–A4 empirical checks |
-| `build/torch-universal/szl_lambda_gate/layers.py` | `nn.Module` wrapper |
-| `build.toml` · `metadata.json` | Kernel Hub build/metadata manifests |
-| `LICENSE` · `SECURITY.md` | Apache-2.0 · security policy |
----
-<p align="center">
-  <a href="https://huggingface.co/SZLHOLDINGS">SZL Holdings</a> ·
-  <a href="https://a-11-oy.com">a-11-oy.com</a> ·
-  <a href="https://huggingface.co/SZLHOLDINGS/szl-kernels">szl-kernels</a>
-</p>
-<p align="center"><sub>SLSA: L1 honest · L2 attested · L3 roadmap. Λ = Conjecture 1. Trust ceiling 0.97.</sub></p>

+---
+license: apache-2.0
+library_name: kernels
+tags:
+  - kernel
+  - governance
+  - lambda
+  - gate
+  - provenance
+  - szl-holdings
+  - doi:10.5281/zenodo.19944926
+---
+# szl-lambda-gate
+**Λ, a governance aggregator, as a pure-PyTorch (noarch) kernel.** A differentiable,
+`torch.compile`-friendly weighted-geometric-mean aggregator with an ADVISORY
+non-compensatory gate, the strict `szl.lambda/v1` entry point and runtime axiom
+self-checks, from [SZL Holdings](https://huggingface.co/SZLHOLDINGS).
+This repository is published from GitHub
+[szl-holdings/szl-lambda-gate](https://github.com/szl-holdings/szl-lambda-gate) by its
+`hf-kernel-publish` workflow, which is its only writer. GitHub is the source of truth:
+every file under `build/` is a byte copy of `torch-ext/szl_lambda_gate` at the commit
+named in `SZL_KERNEL_SOURCE.json`.
+## Load it
+```python
+# pip install "kernels>=0.17.2" torch
+import torch
+from kernels import get_kernel
+lg = get_kernel("SZLHOLDINGS/szl-lambda-gate", version=1, trust_remote_code=True)
+axes = torch.tensor([0.9, 0.8, 0.95], dtype=torch.float64)   # axis scores in [0,1]
+weights = torch.tensor([0.5, 0.25, 0.25], dtype=torch.float64)
+gate = lg.lambda_v1_gate(axes, weights, tau=0.8)              # GO / NO_GO / ABSTAIN / BLOCK
+print(gate.verdict, gate.code)
+print(lg.lambda_aggregate(axes))   # legacy Λ(x) in [0,1]
+print(lg.selfcheck())              # empirical A1-A4 checks, not a proof
+```
+- `version=1` resolves the `v1` branch, the stable API channel. It
+  is republished on every release. The `kernels` library refuses a call that names neither
+  a version nor a revision.
+- `trust_remote_code` is required: SZLHOLDINGS is not a trusted kernel publisher on the Hub.
+  The narrowest form is `trust_remote_code=["SZLHOLDINGS/szl-lambda-gate"]`.
+- CPU hosts load `build/torch-cpu`; CUDA and other hosts load `build/torch-universal`.
+  Both variants carry identical code, built from the same GitHub commit.
+### Pin an exact build (production)
+```python
+from huggingface_hub import HfApi
+from kernels import get_kernel
+refs = HfApi().list_repo_refs("SZLHOLDINGS/szl-lambda-gate", repo_type="kernel")
+pin = next(b.target_commit for b in refs.branches if b.name == "v1")
+lg = get_kernel("SZLHOLDINGS/szl-lambda-gate", revision=pin, trust_remote_code=["SZLHOLDINGS/szl-lambda-gate"])
+```
+Record `pin` (a commit of this kernel repository) in your lockfile. Commits of the
+model repository [https://huggingface.co/SZLHOLDINGS/szl-lambda-gate](https://huggingface.co/SZLHOLDINGS/szl-lambda-gate) are not kernel revisions and
+do not resolve here.
+## What Λ is, and is not
+Λ is the weighted geometric mean over axis scores in [0,1]:
+Λ(x) = ∏ xᵢ^wᵢ with Σ wᵢ = 1, wᵢ > 0. It is a non-compensatory, ADVISORY roll-up: any
+zero (or, in the legacy entry, non-finite) axis drives the aggregate to 0. A gate pass is
+an advisory signal that the host must enforce; it is not proven trust. Λ uniqueness
+remains **Conjecture 1 (open)**. The A1-A4 self-checks are empirical evidence on sampled
+inputs, not a proof.
+## API
+| Entry | Notes |
+|---|---|
+| `lambda_v1(axes, weights)` / `lambda_v1_gate(axes, weights, tau)` | Strict `szl.lambda/v1` contract (`spec/szl.lambda.v1.json`): validated, never repaired; bad input returns `BLOCK` with a `LAMBDA_*` code; τ is required. |
+| `lambda_aggregate(axes, weights=None)` | Legacy Λ over the last dim. Differentiable, batched. Clamps and renormalises (see the spec for the differences). |
+| `lambda_gate(axes, weights=None, threshold=...)` / `lambda_gate_batch(...)` | Legacy advisory gate → `LambdaGateResult(score, passed, threshold, advisory)`. |
+| `selfcheck()`, `is_monotone`, `is_homogeneous`, `is_egyptian_exact`, `is_bounded_by_max`, `find_axiom_violation` | Empirical A1-A4 checks and a falsification search. |
+| `governed_norm` (`rms_norm`, `layer_norm`, `fused_add_rms_norm`) | Governed normalization with SHA3-256 hash-chained receipts. Also published standalone as [SZLHOLDINGS/szl-governed-norm](https://huggingface.co/kernels/SZLHOLDINGS/szl-governed-norm). |
+| `layers.LambdaGate`, `layers.LambdaAggregate` | Pure `nn.Module` layers for kernel layer mapping. |
+## Files
+| Path | What it is |
+|---|---|
+| `build/torch-cpu/`, `build/torch-universal/` | The kernel package and its generated `metadata.json` (with a sha256 digest of every file). |
+| `spec/szl.lambda.v1.json`, `spec/lambda_v1_vectors.json` | The `szl.lambda/v1` contract and its golden vectors. |
+| `reference/szl_lambda_v1.py` | Stdlib-only reference implementation of the contract. |
+| `SZL_KERNEL_SOURCE.json` | The GitHub commit and per-file sha256 this tree was built from. |
+| `build.toml`, `LICENSE` | Kernel build manifest and Apache-2.0 license, copied from GitHub. |
+No speedup, throughput or energy figure is claimed for this kernel. Test results live
+in GitHub Actions for the source commit.
+## License and citation
+Apache-2.0. Copyright 2026 SZL Holdings. Concept DOI
+[10.5281/zenodo.19944926](https://doi.org/10.5281/zenodo.19944926); see `CITATION.cff` in
+the GitHub repository.

SZL_KERNEL_SOURCE.json ADDED Viewed

	@@ -0,0 +1,49 @@

+{
+  "content_sha256": "7bb76d1613da5e0487cc182afb64115e5b9998e06b57027694e9bb381ba94435",
+  "files": {
+    "LICENSE": "bc8cef3450d423b046a3ba2b1a03089c8c795302f502cabbdbef055a68f0b1e7",
+    "README.md": "e5141ba71f4ea828c30537ad9c4e88760f1712d1bf1a90cc5939ee902c9620c3",
+    "build.toml": "5c5ebbfe83770a898b8b8893e342c2c5547c65cd2b5a96146b14e3abb9411f3b",
+    "build/torch-cpu/metadata.json": "94fd9128b27adcb36237f2ee9618b09f7a61e1f88ba520623dda3b504154ac10",
+    "build/torch-cpu/szl_lambda_gate/__init__.py": "2d95cb2a572a9ee8f00678e1a8aff347a12ccb0d529d2945a6a3bd67fa565854",
+    "build/torch-cpu/szl_lambda_gate/_lambda.py": "f953a38019c91a6fa8a9c411f55baa4f5f2b9e76d7153bba54bb8a5d9469af38",
+    "build/torch-cpu/szl_lambda_gate/_ops.py": "6adf1dc42e7a9e6da914e304da9720438e59c234104e11f3e5ccaa45ecee23b6",
+    "build/torch-cpu/szl_lambda_gate/_v1.py": "9ac2d8fe0a47ff191eba76e62b1e9dd9b2b54f64ba0c3d86306e0cdd58bc468c",
+    "build/torch-cpu/szl_lambda_gate/governed_norm/__init__.py": "50fe4fb09a165d0ae9a781f0ef3515388c752a14cde95f73687854f59a7dfadc",
+    "build/torch-cpu/szl_lambda_gate/governed_norm/_norm.py": "cf72ace281ec86c504426f75e23eebdb661a1e3a12e4dfcc6c8b9fa634b03942",
+    "build/torch-cpu/szl_lambda_gate/governed_norm/_receipt.py": "0fa2cf7e8c5f4f9b5908868ba6d6ec05e9dfbec26a7656b89ab2bfbdf3d6d870",
+    "build/torch-cpu/szl_lambda_gate/governed_norm/layers.py": "d79c83355cb2332b9d0e9d98584b86434e486532e27d30652df10158b30fc37b",
+    "build/torch-cpu/szl_lambda_gate/layers.py": "51da478e185ee4d16daa9971bdcbdcfcbb0c0c5b7837a002b97cd05cb968ea75",
+    "build/torch-universal/metadata.json": "a69500fb8634cb39482b1c73efba0b9feda6cfacc0be3cb073d505e25aa5a6f4",
+    "build/torch-universal/szl_lambda_gate/__init__.py": "2d95cb2a572a9ee8f00678e1a8aff347a12ccb0d529d2945a6a3bd67fa565854",
+    "build/torch-universal/szl_lambda_gate/_lambda.py": "f953a38019c91a6fa8a9c411f55baa4f5f2b9e76d7153bba54bb8a5d9469af38",
+    "build/torch-universal/szl_lambda_gate/_ops.py": "6adf1dc42e7a9e6da914e304da9720438e59c234104e11f3e5ccaa45ecee23b6",
+    "build/torch-universal/szl_lambda_gate/_v1.py": "9ac2d8fe0a47ff191eba76e62b1e9dd9b2b54f64ba0c3d86306e0cdd58bc468c",
+    "build/torch-universal/szl_lambda_gate/governed_norm/__init__.py": "50fe4fb09a165d0ae9a781f0ef3515388c752a14cde95f73687854f59a7dfadc",
+    "build/torch-universal/szl_lambda_gate/governed_norm/_norm.py": "cf72ace281ec86c504426f75e23eebdb661a1e3a12e4dfcc6c8b9fa634b03942",
+    "build/torch-universal/szl_lambda_gate/governed_norm/_receipt.py": "0fa2cf7e8c5f4f9b5908868ba6d6ec05e9dfbec26a7656b89ab2bfbdf3d6d870",
+    "build/torch-universal/szl_lambda_gate/governed_norm/layers.py": "d79c83355cb2332b9d0e9d98584b86434e486532e27d30652df10158b30fc37b",
+    "build/torch-universal/szl_lambda_gate/layers.py": "51da478e185ee4d16daa9971bdcbdcfcbb0c0c5b7837a002b97cd05cb968ea75",
+    "reference/szl_lambda_v1.py": "57b264fa96056d1f2d77b797ba80eab087a729283f16416196fdd1423c9c532f",
+    "spec/lambda_v1_vectors.json": "460bb966ff2bec4fd99e03d267b721f22c5fad731e0667723407a6909bd4dcec",
+    "spec/szl.lambda.v1.json": "04a8368568d59509c8603cdc45cf80f6b1395e751c77eabb1957bbe12906f2d9"
+  },
+  "kernel_name": "szl-lambda-gate",
+  "kernel_version": 1,
+  "load": {
+    "call": "get_kernel(\"SZLHOLDINGS/szl-lambda-gate\", version=1, trust_remote_code=True)",
+    "requires": "kernels>=0.17.2"
+  },
+  "packages": [
+    {
+      "name": "szl_lambda_gate",
+      "src": "torch-ext/szl_lambda_gate"
+    }
+  ],
+  "publisher": "scripts/szl_kernel_publish.py",
+  "repo_id": "SZLHOLDINGS/szl-lambda-gate",
+  "repo_type": "kernel",
+  "schema": "szl.kernel-source-binding/v1",
+  "source_commit": "219ee89500f2d2a137f547c1c6ed8d97c6c66e9d",
+  "source_repository": "szl-holdings/szl-lambda-gate"
+}

build/torch-cpu/__init__.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- from .szl_lambda_gate import * # noqa: F401,F403
2	- from .szl_lambda_gate import __all__

build/torch-cpu/metadata.json CHANGED Viewed

	@@ -1 +1,26 @@
1	- {"backend":{"type":"cpu"},"digest":{"algorithm":"sha256","files":{"__init__.py":"NuinjlWRNlWgg0y2D61HEaKtW/VOktRHP8w2Nj3opjQ=","szl_lambda_gate/__init__.py":"QpfkkacuKUGxXitqBYLq0nM4NnCsX7uNFOLHbeye0V0=","szl_lambda_gate/_lambda.py":"+VOjgBnJGm+oqcQR9VuqT18rnnbXFTu6VLuKXZRprzg=","szl_lambda_gate/_ops.py":"at8dxC56nm2pFOME2pcgQ45ZwjQQThHz5cyqRezuI7Y=","szl_lambda_gate/_v1.py":"msLY/gpH/xkeunbmKx6d2bK1T2S6DD2GMG4M3Vi8Row=","szl_lambda_gate/governed_norm/__init__.py":"UP5PsJoWXQrpp4Hw7zUVOIx1KhTN6V9zaHhU9Zp9+tw=","szl_lambda_gate/governed_norm/_norm.py":"z3Ks4oHshsUEQm914j7r22YaHjoS5N/MbIufpjSwOUI=","szl_lambda_gate/governed_norm/_receipt.py":"2xJEvRhK/9ypKb04rHKLJpb4KtVlqvgtFMo4RdTXSKk=","szl_lambda_gate/governed_norm/layers.py":"15yDNVyyMyudDp2YWEuGQ05IZTLifTBlLfEBWLMPw3s=","szl_lambda_gate/layers.py":"UdpHjhhe5NFtqplxvcvc/LsMDFt4N6ACuXzQXLlo6nU="}},"id":"_szl_lambda_gate_cpu_7cb79cba7ecb5dbb9da59b2d6b516a98d2a114d6","license":"Apache-2.0","name":"szl-lambda-gate","python-depends":[],"source":"https://github.com/szl-holdings/szl-lambda-gate","universal":false,"version":1}

+{
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "szl_lambda_gate/__init__.py": "LZXLKlcqnujwBnjhqK/zR6Esyw1SnSlFpqO9Z/pWWFQ=",
+      "szl_lambda_gate/_lambda.py": "+VOjgBnJGm+oqcQR9VuqT18rnnbXFTu6VLuKXZRprzg=",
+      "szl_lambda_gate/_ops.py": "at8dxC56nm2pFOME2pcgQ45ZwjQQThHz5cyqRezuI7Y=",
+      "szl_lambda_gate/_v1.py": "msLY/gpH/xkeunbmKx6d2bK1T2S6DD2GMG4M3Vi8Row=",
+      "szl_lambda_gate/governed_norm/__init__.py": "UP5PsJoWXQrpp4Hw7zUVOIx1KhTN6V9zaHhU9Zp9+tw=",
+      "szl_lambda_gate/governed_norm/_norm.py": "z3Ks4oHshsUEQm914j7r22YaHjoS5N/MbIufpjSwOUI=",
+      "szl_lambda_gate/governed_norm/_receipt.py": "D6LPfoxfT5tZCIaLptbsBenfvsJqdla4mrK/vfPW2HA=",
+      "szl_lambda_gate/governed_norm/layers.py": "15yDNVyyMyudDp2YWEuGQ05IZTLifTBlLfEBWLMPw3s=",
+      "szl_lambda_gate/layers.py": "UdpHjhhe5NFtqplxvcvc/LsMDFt4N6ACuXzQXLlo6nU="
+    }
+  },
+  "id": "_szl_lambda_gate_cpu_219ee89500f2",
+  "kernel-depends": [],
+  "license": "Apache-2.0",
+  "name": "szl-lambda-gate",
+  "python-depends": [],
+  "source": "https://github.com/szl-holdings/szl-lambda-gate",
+  "version": 1
+}

build/torch-cpu/szl_lambda_gate/__init__.py CHANGED Viewed

@@ -16,7 +16,7 @@ Load from the Hub:
     import torch
     from kernels import get_kernel
-    lg = get_kernel("SZLHOLDINGS/szl-lambda-gate")
     axes = torch.tensor([0.9, 0.8, 0.95])        # axis scores in [0,1]
     score = lg.lambda_aggregate(axes)            # Λ(x) ∈ [0,1]
     res = lg.lambda_gate(axes, threshold=0.5)    # ADVISORY pass/fail

     import torch
     from kernels import get_kernel
+    lg = get_kernel("SZLHOLDINGS/szl-lambda-gate", version=1, trust_remote_code=True)
     axes = torch.tensor([0.9, 0.8, 0.95])        # axis scores in [0,1]
     score = lg.lambda_aggregate(axes)            # Λ(x) ∈ [0,1]
     res = lg.lambda_gate(axes, threshold=0.5)    # ADVISORY pass/fail

build/torch-cpu/szl_lambda_gate/governed_norm/_receipt.py CHANGED Viewed

@@ -16,8 +16,7 @@ HONESTY:
   it proves authorship. DSSE signing is a separate, out-of-band concern.
 - Receipts are kept in an in-process, append-only chain. Nothing is written
   to disk or the network from inside the kernel.
-- Stdlib + torch (+ numpy for the output digest) only — Kernel Hub
-  universal-kernel requirement.
 - The canonical szl-receipt v0.2.0 evidence binding (``emit_receipt``) is
   ADDITIVE and IMPORT-GUARDED: with szl-receipt absent it returns ``None`` and
   the kernel runs unchanged. It binds subject / input-digest / output-digest /
@@ -27,6 +26,7 @@ HONESTY:
 """
 import hashlib
 import json
 import threading
 import time
 from typing import Any, Dict, List, Optional, Union
@@ -71,19 +71,90 @@ def _maybe_sign(
     return env
-def _tensor_digest(t: torch.Tensor, decimals: int = 6) -> str:
-    """Deterministic SHA3-256 over a tensor's rounded float32 contents.
-    Rounding to a fixed number of decimals makes the digest stable across
-    devices/dtypes for the same logical values (tiny FP noise won't change
-    it). This is an integrity fingerprint, not a signature.
     """
     flat = t.detach().to(torch.float32).reshape(-1)
-    # Round to `decimals` places, integerize, hash the raw bytes. CPU move is
-    # required to read bytes; kept O(n) and allocation-light.
-    scaled = torch.round(flat * (10 ** decimals)).to(torch.int64).cpu().numpy().tobytes()
     h = hashlib.sha3_256()
-    h.update(scaled)
     return h.hexdigest()
@@ -112,17 +183,19 @@ def emit_receipt(
     policy_id: str = _POLICY_ID,
     sign_key: Optional[Union[str, bytes]] = None,
     organ: str = _ORGAN,
 ) -> Optional[Dict[str, Any]]:
     """Canonical szl-receipt v0.2.0 evidence binding for a governed-norm call.
     ADDITIVE and IMPORT-GUARDED: returns ``None`` when szl-receipt is not
     installed, so this universal Kernel-Hub kernel still imports and runs on
-    stdlib + torch + numpy alone. When szl-receipt is present it binds an
     EVIDENCE trail and wraps it in a DSSE envelope via ``sign_receipt``:
         subject        organ / norm-call id (who/what emitted this)
         input_digest   SHA3-256 over canonical {input shape, dtype, eps}
-        output_digest  the EXISTING SHA3-256 rounded-tensor digest of ``out``
         policy_id      the governing policy id
         energy         the literal string "UNAVAILABLE"
@@ -145,7 +218,7 @@ def emit_receipt(
     body = {
         "subject": subject if subject is not None else f"{organ}/{op}",
         "input_digest": _input_digest(x, eps),
-        "output_digest": _tensor_digest(out),
         "policy_id": policy_id,
         "energy": _ENERGY_UNAVAILABLE,
     }
@@ -179,27 +252,37 @@ class ReceiptChain:
         sign_key: Optional[Union[str, bytes]] = None,
         organ: str = _ORGAN,
         policy_id: str = _POLICY_ID,
     ) -> Dict[str, Any]:
         with self._lock:
             prev = self._records[-1]["digest"] if self._records else _GENESIS
             seq = len(self._records)
             body = {
                 "seq": seq,
                 "op": op,
                 "in_shape": list(x.shape),
                 "in_dtype": str(x.dtype).replace("torch.", ""),
                 "eps": float(eps),
-                "out_digest": _tensor_digest(out),
                 "prev": prev,
             }
             digest = self._digest_body(body)
-            rec = dict(body, digest=digest, ts=time.time())
             sig = _maybe_sign(body, sign_key, organ)
             if sig is not None:
                 rec["signature"] = sig
             # ADDITIVE canonical szl-receipt v0.2.0 evidence binding. Import-
             # guarded: None when szl-receipt is absent, so the universal kernel
-            # keeps working on stdlib + torch + numpy only. Binds subject /
             # input-digest / output-digest / policy-id / energy; energy is
             # "UNAVAILABLE" (no joules measured here). It does NOT enter the
             # SHA3-256 chain body, so verify() is unaffected.
@@ -209,6 +292,7 @@ class ReceiptChain:
                 policy_id=policy_id,
                 sign_key=sign_key,
                 organ=organ,
             )
             if binding is not None:
                 rec["receipt"] = binding

   it proves authorship. DSSE signing is a separate, out-of-band concern.
 - Receipts are kept in an in-process, append-only chain. Nothing is written
   to disk or the network from inside the kernel.
+- Stdlib + torch only — Kernel Hub universal-kernel requirement.
 - The canonical szl-receipt v0.2.0 evidence binding (``emit_receipt``) is
   ADDITIVE and IMPORT-GUARDED: with szl-receipt absent it returns ``None`` and
   the kernel runs unchanged. It binds subject / input-digest / output-digest /
 """
 import hashlib
 import json
+import struct
 import threading
 import time
 from typing import Any, Dict, List, Optional, Union
     return env
+# Historical receipts used the rounded-int64 encoding. The class-byte encoding
+# prefixes one class byte per element, so finite-value digests change too.
+# A stored hash is never rewritten from one encoding into the other.
+# Donor: szl-governed-norm src _receipt.py at
+# 3ef27eb7ebf491b0a6ce69be170ecef4c37885a2, file pin
+# c68d06d35058542ea77dc6bad4c8bde2361cc16a.
+TENSOR_DIGEST_LEGACY = "rounded-int64-v1"
+TENSOR_DIGEST_CLASS_BYTE = "class-byte-v1"
+TENSOR_DIGEST_DEFAULT = TENSOR_DIGEST_CLASS_BYTE
+def _update_tensor_bytes(h, tensor: torch.Tensor) -> None:
+    """Hash native-endian uint8 or int64 tensor bytes without NumPy.
+    Bounded chunks preserve the historical byte stream without building a
+    Python list for the entire output tensor.
+    """
+    flat = tensor.cpu().contiguous().reshape(-1)
+    for chunk in flat.split(4096):
+        values = chunk.tolist()
+        if flat.dtype == torch.uint8:
+            h.update(bytes(values))
+        elif flat.dtype == torch.int64:
+            h.update(struct.pack(f"={len(values)}q", *values))
+        else:
+            raise TypeError(f"unsupported tensor digest dtype: {flat.dtype}")
+def _tensor_digest(
+    t: torch.Tensor,
+    decimals: int = 6,
+    *,
+    encoding: str = TENSOR_DIGEST_DEFAULT,
+) -> str:
+    """SHA3-256 of a tensor under an explicit encoding.
+    ``rounded-int64-v1`` is the historical interpretation. ``class-byte-v1``
+    is the current default. Passing the other encoding interprets the same
+    tensor; it does not replace a hash that was already stored.
+    """
+    if encoding == TENSOR_DIGEST_LEGACY:
+        return _tensor_digest_legacy(t, decimals)
+    if encoding == TENSOR_DIGEST_CLASS_BYTE:
+        return _tensor_digest_class_byte(t, decimals)
+    raise ValueError(
+        f"unknown tensor digest encoding {encoding!r}; "
+        f"expected {TENSOR_DIGEST_LEGACY!r} or {TENSOR_DIGEST_CLASS_BYTE!r}"
+    )
+def _tensor_digest_legacy(t: torch.Tensor, decimals: int = 6) -> str:
+    """Historical digest: rounded int64 payload only.
+    Non-finite values can collide under int64 saturation. This function
+    remains so those historical hashes can still be reproduced.
     """
     flat = t.detach().to(torch.float32).reshape(-1)
+    scaled = torch.round(flat * (10 ** decimals)).to(torch.int64)
     h = hashlib.sha3_256()
+    _update_tensor_bytes(h, scaled)
+    return h.hexdigest()
+def _tensor_digest_class_byte(t: torch.Tensor, decimals: int = 6) -> str:
+    """Class-byte digest. Finite digests change because the class bytes are hashed first.
+    Each element contributes a class byte (0 = finite, 1 = +Inf, 2 = -Inf,
+    3 = NaN) and then its rounded int64 payload. Non-finite payloads are 0,
+    so the class byte carries their identity. Finite payloads stay the
+    rounded int64 values, but the prefix means the SHA3-256 digest is not
+    the legacy digest. -0.0 and +0.0 still share a digest.
+    """
+    flat = t.detach().to(torch.float32).reshape(-1)
+    cls = torch.zeros_like(flat, dtype=torch.uint8)
+    cls[flat == float("inf")] = 1
+    cls[flat == float("-inf")] = 2
+    cls[torch.isnan(flat)] = 3
+    finite = cls == 0
+    scaled = torch.where(
+        finite, torch.round(flat * (10 ** decimals)), torch.zeros_like(flat)
+    ).to(torch.int64)
+    h = hashlib.sha3_256()
+    _update_tensor_bytes(h, cls)
+    _update_tensor_bytes(h, scaled)
     return h.hexdigest()
     policy_id: str = _POLICY_ID,
     sign_key: Optional[Union[str, bytes]] = None,
     organ: str = _ORGAN,
+    digest_encoding: str = TENSOR_DIGEST_DEFAULT,
 ) -> Optional[Dict[str, Any]]:
     """Canonical szl-receipt v0.2.0 evidence binding for a governed-norm call.
     ADDITIVE and IMPORT-GUARDED: returns ``None`` when szl-receipt is not
     installed, so this universal Kernel-Hub kernel still imports and runs on
+    stdlib + torch alone. When szl-receipt is present it binds an
     EVIDENCE trail and wraps it in a DSSE envelope via ``sign_receipt``:
         subject        organ / norm-call id (who/what emitted this)
         input_digest   SHA3-256 over canonical {input shape, dtype, eps}
+        output_digest  SHA3-256 of ``out`` under ``digest_encoding``
+                       (default class-byte-v1; legacy remains callable)
         policy_id      the governing policy id
         energy         the literal string "UNAVAILABLE"
     body = {
         "subject": subject if subject is not None else f"{organ}/{op}",
         "input_digest": _input_digest(x, eps),
+        "output_digest": _tensor_digest(out, encoding=digest_encoding),
         "policy_id": policy_id,
         "energy": _ENERGY_UNAVAILABLE,
     }
         sign_key: Optional[Union[str, bytes]] = None,
         organ: str = _ORGAN,
         policy_id: str = _POLICY_ID,
+        digest_encoding: str = TENSOR_DIGEST_DEFAULT,
     ) -> Dict[str, Any]:
         with self._lock:
             prev = self._records[-1]["digest"] if self._records else _GENESIS
             seq = len(self._records)
+            # The encoding label stays outside the hashed body. verify()
+            # still rebuilds that body from the original keys, so a legacy
+            # receipt is not rewritten when this field is absent or present.
+            out_digest = _tensor_digest(out, encoding=digest_encoding)
             body = {
                 "seq": seq,
                 "op": op,
                 "in_shape": list(x.shape),
                 "in_dtype": str(x.dtype).replace("torch.", ""),
                 "eps": float(eps),
+                "out_digest": out_digest,
                 "prev": prev,
             }
             digest = self._digest_body(body)
+            rec = dict(
+                body,
+                digest=digest,
+                ts=time.time(),
+                tensor_digest_encoding=digest_encoding,
+            )
             sig = _maybe_sign(body, sign_key, organ)
             if sig is not None:
                 rec["signature"] = sig
             # ADDITIVE canonical szl-receipt v0.2.0 evidence binding. Import-
             # guarded: None when szl-receipt is absent, so the universal kernel
+            # keeps working on stdlib + torch only. Binds subject /
             # input-digest / output-digest / policy-id / energy; energy is
             # "UNAVAILABLE" (no joules measured here). It does NOT enter the
             # SHA3-256 chain body, so verify() is unaffected.
                 policy_id=policy_id,
                 sign_key=sign_key,
                 organ=organ,
+                digest_encoding=digest_encoding,
             )
             if binding is not None:
                 rec["receipt"] = binding

build/torch-universal/__init__.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- from .szl_lambda_gate import * # noqa: F401,F403
2	- from .szl_lambda_gate import __all__

build/torch-universal/metadata.json CHANGED Viewed

	@@ -1 +1,26 @@
1	- {"backend":{"type":"cpu"},"digest":{"algorithm":"sha256","files":{"__init__.py":"NuinjlWRNlWgg0y2D61HEaKtW/VOktRHP8w2Nj3opjQ=","szl_lambda_gate/__init__.py":"QpfkkacuKUGxXitqBYLq0nM4NnCsX7uNFOLHbeye0V0=","szl_lambda_gate/_lambda.py":"+VOjgBnJGm+oqcQR9VuqT18rnnbXFTu6VLuKXZRprzg=","szl_lambda_gate/_ops.py":"at8dxC56nm2pFOME2pcgQ45ZwjQQThHz5cyqRezuI7Y=","szl_lambda_gate/_v1.py":"msLY/gpH/xkeunbmKx6d2bK1T2S6DD2GMG4M3Vi8Row=","szl_lambda_gate/governed_norm/__init__.py":"UP5PsJoWXQrpp4Hw7zUVOIx1KhTN6V9zaHhU9Zp9+tw=","szl_lambda_gate/governed_norm/_norm.py":"z3Ks4oHshsUEQm914j7r22YaHjoS5N/MbIufpjSwOUI=","szl_lambda_gate/governed_norm/_receipt.py":"2xJEvRhK/9ypKb04rHKLJpb4KtVlqvgtFMo4RdTXSKk=","szl_lambda_gate/governed_norm/layers.py":"15yDNVyyMyudDp2YWEuGQ05IZTLifTBlLfEBWLMPw3s=","szl_lambda_gate/layers.py":"UdpHjhhe5NFtqplxvcvc/LsMDFt4N6ACuXzQXLlo6nU="}},"id":"_szl_lambda_gate_universal_7cb79cba7ecb5dbb9da59b2d6b516a98d2a114d6","license":"Apache-2.0","name":"szl-lambda-gate","python-depends":[],"source":"https://github.com/szl-holdings/szl-lambda-gate","universal":true,"version":1}

+{
+  "backend": {
+    "type": "cpu"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "szl_lambda_gate/__init__.py": "LZXLKlcqnujwBnjhqK/zR6Esyw1SnSlFpqO9Z/pWWFQ=",
+      "szl_lambda_gate/_lambda.py": "+VOjgBnJGm+oqcQR9VuqT18rnnbXFTu6VLuKXZRprzg=",
+      "szl_lambda_gate/_ops.py": "at8dxC56nm2pFOME2pcgQ45ZwjQQThHz5cyqRezuI7Y=",
+      "szl_lambda_gate/_v1.py": "msLY/gpH/xkeunbmKx6d2bK1T2S6DD2GMG4M3Vi8Row=",
+      "szl_lambda_gate/governed_norm/__init__.py": "UP5PsJoWXQrpp4Hw7zUVOIx1KhTN6V9zaHhU9Zp9+tw=",
+      "szl_lambda_gate/governed_norm/_norm.py": "z3Ks4oHshsUEQm914j7r22YaHjoS5N/MbIufpjSwOUI=",
+      "szl_lambda_gate/governed_norm/_receipt.py": "D6LPfoxfT5tZCIaLptbsBenfvsJqdla4mrK/vfPW2HA=",
+      "szl_lambda_gate/governed_norm/layers.py": "15yDNVyyMyudDp2YWEuGQ05IZTLifTBlLfEBWLMPw3s=",
+      "szl_lambda_gate/layers.py": "UdpHjhhe5NFtqplxvcvc/LsMDFt4N6ACuXzQXLlo6nU="
+    }
+  },
+  "id": "_szl_lambda_gate_universal_219ee89500f2",
+  "kernel-depends": [],
+  "license": "Apache-2.0",
+  "name": "szl-lambda-gate",
+  "python-depends": [],
+  "source": "https://github.com/szl-holdings/szl-lambda-gate",
+  "version": 1
+}

build/torch-universal/szl_lambda_gate/__init__.py CHANGED Viewed

@@ -16,7 +16,7 @@ Load from the Hub:
     import torch
     from kernels import get_kernel
-    lg = get_kernel("SZLHOLDINGS/szl-lambda-gate")
     axes = torch.tensor([0.9, 0.8, 0.95])        # axis scores in [0,1]
     score = lg.lambda_aggregate(axes)            # Λ(x) ∈ [0,1]
     res = lg.lambda_gate(axes, threshold=0.5)    # ADVISORY pass/fail

     import torch
     from kernels import get_kernel
+    lg = get_kernel("SZLHOLDINGS/szl-lambda-gate", version=1, trust_remote_code=True)
     axes = torch.tensor([0.9, 0.8, 0.95])        # axis scores in [0,1]
     score = lg.lambda_aggregate(axes)            # Λ(x) ∈ [0,1]
     res = lg.lambda_gate(axes, threshold=0.5)    # ADVISORY pass/fail

build/torch-universal/szl_lambda_gate/governed_norm/_receipt.py CHANGED Viewed

@@ -16,8 +16,7 @@ HONESTY:
   it proves authorship. DSSE signing is a separate, out-of-band concern.
 - Receipts are kept in an in-process, append-only chain. Nothing is written
   to disk or the network from inside the kernel.
-- Stdlib + torch (+ numpy for the output digest) only — Kernel Hub
-  universal-kernel requirement.
 - The canonical szl-receipt v0.2.0 evidence binding (``emit_receipt``) is
   ADDITIVE and IMPORT-GUARDED: with szl-receipt absent it returns ``None`` and
   the kernel runs unchanged. It binds subject / input-digest / output-digest /
@@ -27,6 +26,7 @@ HONESTY:
 """
 import hashlib
 import json
 import threading
 import time
 from typing import Any, Dict, List, Optional, Union
@@ -71,19 +71,90 @@ def _maybe_sign(
     return env
-def _tensor_digest(t: torch.Tensor, decimals: int = 6) -> str:
-    """Deterministic SHA3-256 over a tensor's rounded float32 contents.
-    Rounding to a fixed number of decimals makes the digest stable across
-    devices/dtypes for the same logical values (tiny FP noise won't change
-    it). This is an integrity fingerprint, not a signature.
     """
     flat = t.detach().to(torch.float32).reshape(-1)
-    # Round to `decimals` places, integerize, hash the raw bytes. CPU move is
-    # required to read bytes; kept O(n) and allocation-light.
-    scaled = torch.round(flat * (10 ** decimals)).to(torch.int64).cpu().numpy().tobytes()
     h = hashlib.sha3_256()
-    h.update(scaled)
     return h.hexdigest()
@@ -112,17 +183,19 @@ def emit_receipt(
     policy_id: str = _POLICY_ID,
     sign_key: Optional[Union[str, bytes]] = None,
     organ: str = _ORGAN,
 ) -> Optional[Dict[str, Any]]:
     """Canonical szl-receipt v0.2.0 evidence binding for a governed-norm call.
     ADDITIVE and IMPORT-GUARDED: returns ``None`` when szl-receipt is not
     installed, so this universal Kernel-Hub kernel still imports and runs on
-    stdlib + torch + numpy alone. When szl-receipt is present it binds an
     EVIDENCE trail and wraps it in a DSSE envelope via ``sign_receipt``:
         subject        organ / norm-call id (who/what emitted this)
         input_digest   SHA3-256 over canonical {input shape, dtype, eps}
-        output_digest  the EXISTING SHA3-256 rounded-tensor digest of ``out``
         policy_id      the governing policy id
         energy         the literal string "UNAVAILABLE"
@@ -145,7 +218,7 @@ def emit_receipt(
     body = {
         "subject": subject if subject is not None else f"{organ}/{op}",
         "input_digest": _input_digest(x, eps),
-        "output_digest": _tensor_digest(out),
         "policy_id": policy_id,
         "energy": _ENERGY_UNAVAILABLE,
     }
@@ -179,27 +252,37 @@ class ReceiptChain:
         sign_key: Optional[Union[str, bytes]] = None,
         organ: str = _ORGAN,
         policy_id: str = _POLICY_ID,
     ) -> Dict[str, Any]:
         with self._lock:
             prev = self._records[-1]["digest"] if self._records else _GENESIS
             seq = len(self._records)
             body = {
                 "seq": seq,
                 "op": op,
                 "in_shape": list(x.shape),
                 "in_dtype": str(x.dtype).replace("torch.", ""),
                 "eps": float(eps),
-                "out_digest": _tensor_digest(out),
                 "prev": prev,
             }
             digest = self._digest_body(body)
-            rec = dict(body, digest=digest, ts=time.time())
             sig = _maybe_sign(body, sign_key, organ)
             if sig is not None:
                 rec["signature"] = sig
             # ADDITIVE canonical szl-receipt v0.2.0 evidence binding. Import-
             # guarded: None when szl-receipt is absent, so the universal kernel
-            # keeps working on stdlib + torch + numpy only. Binds subject /
             # input-digest / output-digest / policy-id / energy; energy is
             # "UNAVAILABLE" (no joules measured here). It does NOT enter the
             # SHA3-256 chain body, so verify() is unaffected.
@@ -209,6 +292,7 @@ class ReceiptChain:
                 policy_id=policy_id,
                 sign_key=sign_key,
                 organ=organ,
             )
             if binding is not None:
                 rec["receipt"] = binding

   it proves authorship. DSSE signing is a separate, out-of-band concern.
 - Receipts are kept in an in-process, append-only chain. Nothing is written
   to disk or the network from inside the kernel.
+- Stdlib + torch only — Kernel Hub universal-kernel requirement.
 - The canonical szl-receipt v0.2.0 evidence binding (``emit_receipt``) is
   ADDITIVE and IMPORT-GUARDED: with szl-receipt absent it returns ``None`` and
   the kernel runs unchanged. It binds subject / input-digest / output-digest /
 """
 import hashlib
 import json
+import struct
 import threading
 import time
 from typing import Any, Dict, List, Optional, Union
     return env
+# Historical receipts used the rounded-int64 encoding. The class-byte encoding
+# prefixes one class byte per element, so finite-value digests change too.
+# A stored hash is never rewritten from one encoding into the other.
+# Donor: szl-governed-norm src _receipt.py at
+# 3ef27eb7ebf491b0a6ce69be170ecef4c37885a2, file pin
+# c68d06d35058542ea77dc6bad4c8bde2361cc16a.
+TENSOR_DIGEST_LEGACY = "rounded-int64-v1"
+TENSOR_DIGEST_CLASS_BYTE = "class-byte-v1"
+TENSOR_DIGEST_DEFAULT = TENSOR_DIGEST_CLASS_BYTE
+def _update_tensor_bytes(h, tensor: torch.Tensor) -> None:
+    """Hash native-endian uint8 or int64 tensor bytes without NumPy.
+    Bounded chunks preserve the historical byte stream without building a
+    Python list for the entire output tensor.
+    """
+    flat = tensor.cpu().contiguous().reshape(-1)
+    for chunk in flat.split(4096):
+        values = chunk.tolist()
+        if flat.dtype == torch.uint8:
+            h.update(bytes(values))
+        elif flat.dtype == torch.int64:
+            h.update(struct.pack(f"={len(values)}q", *values))
+        else:
+            raise TypeError(f"unsupported tensor digest dtype: {flat.dtype}")
+def _tensor_digest(
+    t: torch.Tensor,
+    decimals: int = 6,
+    *,
+    encoding: str = TENSOR_DIGEST_DEFAULT,
+) -> str:
+    """SHA3-256 of a tensor under an explicit encoding.
+    ``rounded-int64-v1`` is the historical interpretation. ``class-byte-v1``
+    is the current default. Passing the other encoding interprets the same
+    tensor; it does not replace a hash that was already stored.
+    """
+    if encoding == TENSOR_DIGEST_LEGACY:
+        return _tensor_digest_legacy(t, decimals)
+    if encoding == TENSOR_DIGEST_CLASS_BYTE:
+        return _tensor_digest_class_byte(t, decimals)
+    raise ValueError(
+        f"unknown tensor digest encoding {encoding!r}; "
+        f"expected {TENSOR_DIGEST_LEGACY!r} or {TENSOR_DIGEST_CLASS_BYTE!r}"
+    )
+def _tensor_digest_legacy(t: torch.Tensor, decimals: int = 6) -> str:
+    """Historical digest: rounded int64 payload only.
+    Non-finite values can collide under int64 saturation. This function
+    remains so those historical hashes can still be reproduced.
     """
     flat = t.detach().to(torch.float32).reshape(-1)
+    scaled = torch.round(flat * (10 ** decimals)).to(torch.int64)
     h = hashlib.sha3_256()
+    _update_tensor_bytes(h, scaled)
+    return h.hexdigest()
+def _tensor_digest_class_byte(t: torch.Tensor, decimals: int = 6) -> str:
+    """Class-byte digest. Finite digests change because the class bytes are hashed first.
+    Each element contributes a class byte (0 = finite, 1 = +Inf, 2 = -Inf,
+    3 = NaN) and then its rounded int64 payload. Non-finite payloads are 0,
+    so the class byte carries their identity. Finite payloads stay the
+    rounded int64 values, but the prefix means the SHA3-256 digest is not
+    the legacy digest. -0.0 and +0.0 still share a digest.
+    """
+    flat = t.detach().to(torch.float32).reshape(-1)
+    cls = torch.zeros_like(flat, dtype=torch.uint8)
+    cls[flat == float("inf")] = 1
+    cls[flat == float("-inf")] = 2
+    cls[torch.isnan(flat)] = 3
+    finite = cls == 0
+    scaled = torch.where(
+        finite, torch.round(flat * (10 ** decimals)), torch.zeros_like(flat)
+    ).to(torch.int64)
+    h = hashlib.sha3_256()
+    _update_tensor_bytes(h, cls)
+    _update_tensor_bytes(h, scaled)
     return h.hexdigest()
     policy_id: str = _POLICY_ID,
     sign_key: Optional[Union[str, bytes]] = None,
     organ: str = _ORGAN,
+    digest_encoding: str = TENSOR_DIGEST_DEFAULT,
 ) -> Optional[Dict[str, Any]]:
     """Canonical szl-receipt v0.2.0 evidence binding for a governed-norm call.
     ADDITIVE and IMPORT-GUARDED: returns ``None`` when szl-receipt is not
     installed, so this universal Kernel-Hub kernel still imports and runs on
+    stdlib + torch alone. When szl-receipt is present it binds an
     EVIDENCE trail and wraps it in a DSSE envelope via ``sign_receipt``:
         subject        organ / norm-call id (who/what emitted this)
         input_digest   SHA3-256 over canonical {input shape, dtype, eps}
+        output_digest  SHA3-256 of ``out`` under ``digest_encoding``
+                       (default class-byte-v1; legacy remains callable)
         policy_id      the governing policy id
         energy         the literal string "UNAVAILABLE"
     body = {
         "subject": subject if subject is not None else f"{organ}/{op}",
         "input_digest": _input_digest(x, eps),
+        "output_digest": _tensor_digest(out, encoding=digest_encoding),
         "policy_id": policy_id,
         "energy": _ENERGY_UNAVAILABLE,
     }
         sign_key: Optional[Union[str, bytes]] = None,
         organ: str = _ORGAN,
         policy_id: str = _POLICY_ID,
+        digest_encoding: str = TENSOR_DIGEST_DEFAULT,
     ) -> Dict[str, Any]:
         with self._lock:
             prev = self._records[-1]["digest"] if self._records else _GENESIS
             seq = len(self._records)
+            # The encoding label stays outside the hashed body. verify()
+            # still rebuilds that body from the original keys, so a legacy
+            # receipt is not rewritten when this field is absent or present.
+            out_digest = _tensor_digest(out, encoding=digest_encoding)
             body = {
                 "seq": seq,
                 "op": op,
                 "in_shape": list(x.shape),
                 "in_dtype": str(x.dtype).replace("torch.", ""),
                 "eps": float(eps),
+                "out_digest": out_digest,
                 "prev": prev,
             }
             digest = self._digest_body(body)
+            rec = dict(
+                body,
+                digest=digest,
+                ts=time.time(),
+                tensor_digest_encoding=digest_encoding,
+            )
             sig = _maybe_sign(body, sign_key, organ)
             if sig is not None:
                 rec["signature"] = sig
             # ADDITIVE canonical szl-receipt v0.2.0 evidence binding. Import-
             # guarded: None when szl-receipt is absent, so the universal kernel
+            # keeps working on stdlib + torch only. Binds subject /
             # input-digest / output-digest / policy-id / energy; energy is
             # "UNAVAILABLE" (no joules measured here). It does NOT enter the
             # SHA3-256 chain body, so verify() is unaffected.
                 policy_id=policy_id,
                 sign_key=sign_key,
                 organ=organ,
+                digest_encoding=digest_encoding,
             )
             if binding is not None:
                 rec["receipt"] = binding

distribution.json DELETED Viewed

@@ -1 +0,0 @@

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source/LICENSE DELETED Viewed

@@ -1,202 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-   1. Definitions.
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
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