b652a627ba
The Super-Blueprint §3.3 "grade-5 pseudoscalar alignment anchor" is mathematically vacuous in odd-dimensional Cl(4,1) and cannot be built as specified (3-agent R&D convergence — Opus/Grok/Terra — ratified): - I₅ is central in odd dim → V·I₅·Ṽ = ±I₅ for every versor; orientation is invariant by construction (nothing to block). - Field-state versors are even → F[31] ≡ 0; the gated ⟨F·F̃⟩₅ = 0 for the source and target of every transition. The gate compares 0 == 0. - The "even-versor parity gate" repair is equally vacuous (even subalgebra is closed under the sandwich; odd-grade mass ≡ 0 on the sanctioned path). Removes the namesake rather than leaving it masking an absence: - Delete _PSEUDOSCALAR_IDX + all F[31] reads (dead `ps` history element and CoherenceResidual.pseudoscalar — both structural zeros). - Rename telemetry channel pseudoscalar_floor → autonomy_floor (it was always self.floor, the earned-autonomy ceiling). - Bump telemetry schema goldtether_coherence_v1 → v2 (shape changed). The integrity-anchor role is subsumed by versor closure + GoldTether harmonized residual (#24) + biography/identity holonomy. Ledger §5 rewritten with the vacuity proof, subsumption, and the reusable "would this gate ever fire on the sanctioned construction path?" meta-criterion. No serving/runtime path touched (core.physics.* is off-serving). Tests: 33 pass / 1 xfail (Cartan #2) across goldtether + fidelity + miner + transfer suites; ruff clean.
122 lines
3.7 KiB
Python
122 lines
3.7 KiB
Python
"""ADR-0238 — GoldTether residual, floor, autonomy, may_relax_hitl, blend."""
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from __future__ import annotations
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import numpy as np
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from hypothesis import given, settings
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from hypothesis import strategies as st
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from algebra.rotor import make_rotor_from_angle
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from algebra.versor import versor_condition
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from core.physics.goldtether import (
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AutonomyBand,
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GoldTetherMonitor,
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OperatingMode,
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coherence_residual,
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)
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def _id() -> np.ndarray:
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v = np.zeros(32, dtype=np.float64)
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v[0] = 1.0
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return v
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def test_coherence_residual_nonnegative_and_zero_on_identity():
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assert coherence_residual(_id()) == 0.0
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r = coherence_residual(make_rotor_from_angle(0.5))
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assert r >= 0.0
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def test_residual_dual_corrected_and_replay():
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m = GoldTetherMonitor()
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F = make_rotor_from_angle(0.4)
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assert m.residual(F) == m.residual(F)
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assert m.residual(F) == coherence_residual(F)
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def test_fail_closed_on_drift():
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m = GoldTetherMonitor(epsilon_drift=1e-9)
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# Inject a non-closed multivector by raw coefficients
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dirty = np.zeros(32, dtype=np.float64)
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dirty[0] = 0.5
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dirty[1] = 0.5
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r, auto = m.update(dirty, epistemic_elevation=True)
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assert r > m.epsilon_drift
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assert auto == 0.0
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assert m.may_relax_hitl() is False
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def test_epistemic_elevation_raises_floor_and_autonomy():
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m = GoldTetherMonitor(epsilon_drift=1e-5, floor_step=0.1, autonomy_step=0.1)
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F = _id()
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for _ in range(10):
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m.update(F, epistemic_elevation=True)
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assert m.floor > 0.0
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assert m.autonomy > 0.0
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assert m.autonomy <= m.floor
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assert m.supervised_autonomy_level == m.autonomy
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def test_never_autonomy_above_floor():
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m = GoldTetherMonitor(floor_step=0.02, autonomy_step=0.5)
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for _ in range(20):
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m.update(_id(), epistemic_elevation=True)
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assert m.autonomy <= m.floor + 1e-12
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def test_may_relax_hitl_thresholds():
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m = GoldTetherMonitor(
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hitl_floor_threshold=0.3,
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hitl_autonomy_threshold=0.2,
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floor_step=0.1,
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autonomy_step=0.1,
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)
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assert m.may_relax_hitl() is False
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for _ in range(20):
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m.update(_id(), epistemic_elevation=True)
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assert m.may_relax_hitl() is True
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def test_force_reset():
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m = GoldTetherMonitor()
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m.update(_id(), epistemic_elevation=True)
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m.force_reset()
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assert m.floor == 0.0 and m.autonomy == 0.0 and m.history == []
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def test_serve_never_autonomous_band():
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m = GoldTetherMonitor(floor_step=0.2, autonomy_step=0.2, hitl_floor_threshold=0.1, hitl_autonomy_threshold=0.1)
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for _ in range(10):
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m.update(_id(), epistemic_elevation=True)
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d = m.decide(0.0, mode=OperatingMode.SERVE)
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assert d.band is not AutonomyBand.AUTONOMOUS
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def test_lifelong_curve_telemetry_replay():
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m1 = GoldTetherMonitor()
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m2 = GoldTetherMonitor()
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for _ in range(5):
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# identity always closed; deterministic elevation path
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m1.update(_id(), epistemic_elevation=True)
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m2.update(_id(), epistemic_elevation=True)
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assert m1.telemetry()["history_tail"] == m2.telemetry()["history_tail"]
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assert m1.telemetry()["schema_version"] == "goldtether_coherence_v2"
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def test_supervised_blend_closure_and_endpoints():
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m = GoldTetherMonitor()
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src = _id()
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tgt = make_rotor_from_angle(0.6)
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assert np.allclose(m.supervised_blend(src, tgt, 0.0), src)
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assert np.allclose(m.supervised_blend(src, tgt, 1.0), tgt, atol=1e-6)
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mid = m.supervised_blend(src, tgt, 0.5)
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assert versor_condition(mid) < 1e-6
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@given(st.floats(min_value=0.0, max_value=1.0, allow_nan=False, allow_infinity=False))
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@settings(max_examples=30)
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def test_blend_property_closed(alpha: float):
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m = GoldTetherMonitor()
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out = m.supervised_blend(_id(), make_rotor_from_angle(0.8), float(alpha))
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assert versor_condition(out) < 1e-6
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