qig-bench 0.1.7

Validation harness for QIG compute backends — benchmark against frozen physics results

qig-bench

Validation harness for QIG compute backends — benchmark against frozen physics results.

Install

pip install qig-bench

Usage

from qig_bench import run_suite
from qig_bench.compare import compare

results = run_suite(backend="my-backend", verification_root="path/to/qig-verification")
table = compare({"my-backend": results})
print(table)

Core Benchmarks

Five certified value-source benchmarks plus one Class-B reproduction-control.

#	Benchmark	id	Frozen Value	Tolerance	Class
1	Certified JT pillar κ_JT^cert	kappa_JT_cert	+0.02810	±5%	certified
2	Constitutive slope κ_h	kappa_h	−0.00475	±5%	certified
3	Screening ξ_G at L=5	xi_L5	0.6182	±2%	certified
4	Anderson α	anderson_alpha	0.089356/site	±5%	certified
5	Bridge exponent	bridge_exponent	0.86	±3%	certified
—	Class-B matrix-trace κ at L=4	kappa_L4	63.32	±5%	reproduction-control

κ supersession (2026-06-13, EXP-107 / frozen-facts-1.02F). The legacy ~63/64 matrix-trace kappa_L4 is a Class-B (FAIL-013) camera self-portrait, retired as a universal constant and kept here only as a labelled reproduction-control — never a value source. The certified κ slopes are small and signed: kappa_JT_cert = +0.02810 (row 9) and kappa_h = −0.00475 (row 2). The retired grid-interpolation benchmark regime_h_t (row 4) has been removed. Do not treat 63.32 / 63.79 / 64 as a physical constant.