Kernels
Safetensors
PyTorch
kernel
governance
lambda
gate
provenance
torch
surrogate
doi:10.5281/zenodo.19944926
Instructions to use SZLHOLDINGS/szl-lambda-gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use SZLHOLDINGS/szl-lambda-gate with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("SZLHOLDINGS/szl-lambda-gate") - Notebooks
- Google Colab
- Kaggle
File size: 2,572 Bytes
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"artifact": "SZLHOLDINGS/szl-lambda-gate surrogate v1",
"role": "advisory \u039b gate-decision surrogate (torch MLP) \u2014 kernel remains ground truth",
"generator": {
"script": "scripts/forge.py",
"seed": 20260721,
"kernel_version": "0.2.0",
"kernel_labelled": true,
"kernel_audited_samples": 800,
"labeler": "lambda_gate(axes, weights=yuyay_uniform_1/13, threshold=0.5).passed",
"axes": [
"moralGrounding",
"measurabilityHonesty",
"empiricalGrounding",
"logicalConsistency",
"sourceTransparency",
"reproducibility",
"licenseHygiene",
"scopeDiscipline",
"claimCalibration",
"evalAwareness",
"deceptionKeywords",
"conflictingDirectives",
"reversalDirective"
],
"threshold": 0.5
},
"data": {
"rows": 40000,
"classes": [
"GATE_FAIL",
"GATE_PASS"
],
"class_counts": {
"GATE_FAIL": 21828,
"GATE_PASS": 18172
},
"split": "80/20 permutation",
"features": [
"moralGrounding",
"measurabilityHonesty",
"empiricalGrounding",
"logicalConsistency",
"sourceTransparency",
"reproducibility",
"licenseHygiene",
"scopeDiscipline",
"claimCalibration",
"evalAwareness",
"deceptionKeywords",
"conflictingDirectives",
"reversalDirective"
],
"feature_policy": "13 Yuyay axis scores in [0,1]; includes non-compensatory zero-route rows"
},
"model": {
"type": "pytorch GateMLP (3 hidden ReLU layers, 64 units)",
"params": {
"input_dim": 13,
"hidden": 64,
"epochs": 150,
"batch": 512,
"lr": 0.002,
"optimizer": "Adam",
"loss": "BCEWithLogits",
"seed": 20260721
},
"file": "model.safetensors",
"sha256": "79987d9dd53f6c5496569c588ac5203b8d4debda2d8ef9ee7147c43d4740358d",
"config": "config.json"
},
"metrics_MEASURED": {
"fidelity_vs_kernel_heldout": 0.967,
"test_accuracy": 0.967,
"recall_GATE_PASS": 0.9912,
"recall_GATE_FAIL": 0.9469
},
"environment": {
"python": "3.12.12",
"torch": "2.13.0+cpu",
"numpy": "2.5.1",
"host": "replit 2-vCPU container",
"wall_seconds": 12.0
},
"honesty": "Every number above is MEASURED by this run. Fidelity = agreement%% with the kernel's ADVISORY lambda_gate decision on a held-out split. \u039b is the weighted geometric mean, NOT proven trust; uniqueness = Conjecture 1 (open). The surrogate never replaces the kernel gate.",
"trained_at_utc": "2026-07-21T02:53:27Z"
} |