Finding #20 (Super-Blueprint §3.2). surprise_residual was Euclidean Gram-Schmidt on flat 32-coefficient vectors — metric-blind (it ignored the (+,+,+,+,-) signature and the blade grade structure), so "inside the admissible span" was judged by the wrong geometry. Operator math (core/physics/surprise.py): - Exact metric-orthogonal projection: solve the normal equations G c = r (G_ij = cga_inner(b_i,b_j), r_i = cga_inner(b_i,x)) via lstsq, under cga_inner (32-vec) / eta (5-vec). - Fail-closed (typed SurpriseResidualError) on a metric-degenerate span, keyed on rank(G) < rank(B) — a null direction with no reciprocal (lone n_o). Refines the literal "rank(G) < k": mere linear dependence among non-null columns is admitted (lstsq projects onto the span), so a redundant live basis [1, source] and the non-degenerate pair {n_o, n_inf} are admitted; only a lone n_o is refused. The disclosure names the Gram null-space direction (not just zero-diagonal columns). - Reconciled productivity polarity: productive_transfer = low Procrustes AND low surprise (was `sur_norm >= 0.0`, always true). High surprise routes to discovery (split follow-up). Corrects the ledger's transfer/discovery conflation. Adversarial verification (3 independent lenses) found, and this fixes: - HIGH soundness hole: sur_norm was the reversion pseudo-norm, which VANISHES on a nonzero metric-null residual (the n_o/n_inf light cone) -> false-zero surprise -> an out-of-span light-cone probe was wrongly admitted as in-span. Now the DEFINITE (Euclidean) norm of the residual: the projection stays metric-exact, the magnitude is 0 iff nothing is unexplained. - HIGH regression: the analogical-transfer harness called surprise_residual OUTSIDE its try/except, so a degenerate source crashed the whole run. Now guarded: records a refused case and continues. - grade-support `allowed` -> exact-nonzero (removes a spurious-leak edge under coefficient amplification of sub-tolerance grade dust). DiscoveryCandidate wiring into the contemplation loop is split to its own follow-up (a distinct cross-cutting surface). Off-serving (nothing in serving/runtime imports core.physics.*). Tests: 15 behavioral tests (metric-vs-Euclidean divergence, null-cone regression, null refusal + combination-degenerate disclosure, 5-vector branch, polarity); 139-test physics sweep green; ruff clean.
188 lines
5.9 KiB
Python
188 lines
5.9 KiB
Python
"""Analogical transfer validation harness (ADR-0240)."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Sequence
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import numpy as np
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from algebra.cl41 import N_COMPONENTS
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from algebra.rotor import make_rotor_from_angle
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from algebra.versor import unitize_versor, versor_apply, versor_condition
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from core.physics.dynamic_manifold import conformal_procrustes, procrustes_residual
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from core.physics.goldtether import GoldTetherMonitor
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from core.physics.surprise import (
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SurpriseResidualError,
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dual_procrustes_surprise,
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surprise_residual,
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)
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@dataclass(frozen=True, slots=True)
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class TransferCase:
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case_id: str
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source_domain: str
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target_domain: str
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source: np.ndarray
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target: np.ndarray
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novel_query: np.ndarray
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expected_novel: np.ndarray
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@dataclass(frozen=True, slots=True)
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class TransferResult:
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case_id: str
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residual: float
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goldtether_before: float
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goldtether_after: float
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correct: bool
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refused: bool
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reason: str
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@dataclass(frozen=True, slots=True)
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class AnalogicalTransferReport:
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results: tuple[TransferResult, ...]
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counts: dict[str, int]
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max_residual: float
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wrong: int
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@property
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def all_correct_or_refused(self) -> bool:
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return self.wrong == 0
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def _identity() -> np.ndarray:
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v = np.zeros(N_COMPONENTS, dtype=np.float64)
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v[0] = 1.0
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return v
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def make_fixture_pair() -> TransferCase:
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src = _identity()
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R = make_rotor_from_angle(0.7, bivector_idx=6)
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tgt = versor_apply(R, src)
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novel_q = unitize_versor(make_rotor_from_angle(0.3, bivector_idx=7))
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expected = versor_apply(R, novel_q)
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return TransferCase(
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case_id="fixture-rotation-transfer-v1",
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source_domain="domain_a_geometry",
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target_domain="domain_b_geometry",
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source=src,
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target=tgt,
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novel_query=novel_q,
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expected_novel=expected,
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)
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def run_analogical_transfer(
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cases: Sequence[TransferCase],
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*,
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residual_threshold: float = 0.35,
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goldtether: GoldTetherMonitor | None = None,
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) -> AnalogicalTransferReport:
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"""Learn map source→target, apply to novel_query; gate with residual + GoldTether."""
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mon = goldtether or GoldTetherMonitor()
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results: list[TransferResult] = []
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counts = {"correct": 0, "wrong": 0, "refused": 0}
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for case in cases:
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gt_before = mon.residual(case.novel_query)
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try:
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V, proc_r = conformal_procrustes(case.source, case.target)
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mapped = versor_apply(V, case.novel_query)
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residual = float(np.linalg.norm(mapped - case.expected_novel))
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residual = min(residual, procrustes_residual(case.novel_query, case.expected_novel, V))
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closed = versor_condition(mapped) < 1e-6 and versor_condition(V) < 1e-6
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gt_after = mon.residual(mapped)
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except ValueError as exc:
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results.append(
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TransferResult(
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case_id=case.case_id,
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residual=float("inf"),
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goldtether_before=gt_before,
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goldtether_after=gt_before,
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correct=False,
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refused=True,
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reason=f"refused:{exc}",
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)
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)
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counts["refused"] += 1
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continue
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basis = np.column_stack([_identity(), case.source])
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try:
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_sur_v, sur_n = surprise_residual(case.novel_query, basis)
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except SurpriseResidualError as exc:
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results.append(
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TransferResult(
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case_id=case.case_id,
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residual=residual,
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goldtether_before=gt_before,
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goldtether_after=gt_after,
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correct=False,
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refused=True,
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reason=f"surprise_refused:{exc.reason}",
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)
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)
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counts["refused"] += 1
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continue
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dual = dual_procrustes_surprise(case.source, case.target, basis)
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if not closed:
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results.append(
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TransferResult(
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case_id=case.case_id,
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residual=residual,
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goldtether_before=gt_before,
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goldtether_after=gt_after,
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correct=False,
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refused=True,
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reason="closure_failed",
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)
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)
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counts["refused"] += 1
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continue
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# GoldTether residual must not increase (package acceptance criterion)
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gt_ok = gt_after <= gt_before + 1e-9
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if residual <= residual_threshold and gt_ok:
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mon.update(mapped, epistemic_elevation=True)
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results.append(
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TransferResult(
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case_id=case.case_id,
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residual=residual,
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goldtether_before=gt_before,
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goldtether_after=gt_after,
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correct=True,
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refused=False,
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reason="transfer_ok",
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)
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)
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counts["correct"] += 1
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else:
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results.append(
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TransferResult(
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case_id=case.case_id,
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residual=residual,
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goldtether_before=gt_before,
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goldtether_after=gt_after,
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correct=False,
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refused=False,
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reason=(
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"goldtether_increased"
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if not gt_ok
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else f"residual_above_threshold sur={sur_n:.3g} dual={dual['procrustes_residual']:.3g}"
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),
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)
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)
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counts["wrong"] += 1
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max_res = max((r.residual for r in results if np.isfinite(r.residual)), default=0.0)
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return AnalogicalTransferReport(
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results=tuple(results),
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counts=counts,
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max_residual=float(max_res),
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wrong=int(counts["wrong"]),
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)
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