44f7258b16
Stop wave/Third-Door physics from bypassing native dispatch: - Route geometric_product / versor_apply / versor_condition / cga_inner through algebra.backend in wave_manifold, goldtether, trajectory, dynamic_manifold, surprise, holographic_vault, atlas_packing, biography, self_authorship. - Backend: dtype-aware Rust use — f32 workloads use core_rs; f64 wave residual pins keep Python SOT until f64 GP parity exists. Coerce arrays for PyO3 bindings; fail soft to Python. - AST hygiene pin: tests/test_physics_backend_dispatch_hygiene.py - Docs: RUST.md, runtime_contracts, fidelity (ADR-0235 / UMA hygiene). Verified: wave + cohesion suites green default and CORE_BACKEND=rust (with core_rs built). MLX still exploratory off-serve.
561 lines
21 KiB
Python
561 lines
21 KiB
Python
"""
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core/physics/goldtether.py
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GoldTether — Coherence Residual Monitor + Dynamic Autonomy Floor
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ADR-0238
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Note (fidelity #19, RETIRED): an earlier draft borrowed grade-5 "pseudoscalar"
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vocabulary from Super-Blueprint §3.3 for the autonomy floor and read ``F[31]``
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into telemetry. That anchor is vacuous in odd-dim Cl(4,1) — field-state versors
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are even (``F[31] ≡ 0``) and ``I₅`` is central (``V·I₅·Ṽ = I₅`` for every
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versor), so no non-vacuous grade-5 transition invariant exists. The namesake is
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removed; the integrity-anchor role is carried by versor closure + the harmonized
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GoldTether residual + biography/identity holonomy. See
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``docs/research/third-door-blueprint-fidelity.md`` §5.
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Absolute mastery implementation on the live Cl(4,1) algebra kernel.
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All operators are pure where possible, dual-corrected, and enforce algebraic
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closure on versor-valued outputs.
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Distinct from Arena GoldTether (ADR-0199 / core.learning_arena.protocols).
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Any, Literal, Optional, Tuple
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import numpy as np
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from algebra.backend import geometric_product, versor_condition
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from algebra.cl41 import N_COMPONENTS, reverse
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from algebra.rotor import rotor_power, word_transition_rotor
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from algebra.versor import versor_unit_residual
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from core.physics.wave_manifold import WaveManifold
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_CLOSURE_TOL = 1e-6
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_NEAR_ZERO = 1e-12
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_TELEMETRY_SCHEMA = "goldtether_coherence_v2" # v2: dropped vacuous grade-5 channel (#19)
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_E4_IDX = 4
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_E5_IDX = 5
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PruneMode = Literal["fifo", "principal_axes"]
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@dataclass(frozen=True, slots=True)
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class GoldPromotionProof:
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"""Caller-supplied proof for replay-verified promotion into 𝓘_gold (ADR-0092).
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Physics never signs reviews. The review surface constructs this payload and
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passes ``authorized=True`` only after external verification. ``replay_hash``
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is opaque to GoldTether (determinism pin for the caller).
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"""
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residual: float
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replay_hash: str
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reviewer_id: str
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closed: bool
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def _primal_gold_invariants() -> list:
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"""R&D-Revised §5 bootstrapping seeds: the identity versor and the two
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conformal null directions ``n_o = 0.5(e5-e4)`` and ``n_inf = e4+e5``.
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Coordinate-free algebraic anchors so the geometric distance term never
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degenerates to drift-only at cold start.
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"""
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ident = np.zeros(N_COMPONENTS, dtype=np.float64)
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ident[0] = 1.0
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n_o = np.zeros(N_COMPONENTS, dtype=np.float64)
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n_o[_E5_IDX] = 0.5
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n_o[_E4_IDX] = -0.5
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n_inf = np.zeros(N_COMPONENTS, dtype=np.float64)
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n_inf[_E4_IDX] = 1.0
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n_inf[_E5_IDX] = 1.0
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return [ident, n_o, n_inf]
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class OperatingMode(str, Enum):
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PRACTICE = "practice"
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SERVE = "serve"
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class AutonomyBand(str, Enum):
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AUTONOMOUS = "autonomous"
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SUPERVISED_BLEND = "supervised_blend"
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FAIL_CLOSED = "fail_closed"
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@dataclass(frozen=True, slots=True)
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class CoherenceResidual:
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"""Structured residual view (extension of one-shot residual)."""
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primary: float
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dual: float
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combined: float
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kappa: float
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@dataclass(frozen=True, slots=True)
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class AutonomyDecision:
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band: AutonomyBand
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residual: float
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floor: float
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autonomy: float
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mode: OperatingMode
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reason: str
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def _as_mv(F: np.ndarray, name: str = "F") -> np.ndarray:
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arr = np.asarray(F, dtype=np.float64)
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if arr.shape != (N_COMPONENTS,):
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raise ValueError(f"{name} must have shape ({N_COMPONENTS},); got {arr.shape}")
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return arr
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def coherence_residual(F: np.ndarray) -> float:
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"""Public one-shot residual for tests and harnesses.
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R = || F · reverse(F) − 1 ||_F (dual-checked against reverse(F)).
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Canonical path (ADR-0241 Slice 2): :meth:`WaveManifold.measure_unitary_residual`
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— unitary wave amplitude drift, not a parallel residual implementation.
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"""
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return WaveManifold().measure_unitary_residual(_as_mv(F))
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@dataclass
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class GoldTetherMonitor:
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"""
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Continuous geometric monitor of the forever-lived trajectory.
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Primary residual:
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R(t) = || F(t) * reverse(F(t)) - 1 ||_F
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Dynamic autonomy floor rises only on proven epistemic elevation.
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supervised_autonomy_level ∈ [0, 1] is the single gate for HITL relaxation
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(exposed as ``autonomy``).
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"""
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epsilon_drift: float = 1e-6
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floor: float = 0.0
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autonomy: float = 0.0 # supervised_autonomy_level
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history: list = field(default_factory=list)
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max_history: int = 1024
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floor_step: float = 0.02
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floor_decay: float = 0.05
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autonomy_step: float = 0.01
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hitl_floor_threshold: float = 0.7
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hitl_autonomy_threshold: float = 0.5
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# Harmonized residual + alpha control law (ADR-0238 §2.3 / R&D-Revised §2.3).
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w_drift: float = 0.5
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r_floor: float = 0.1
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r_critical: float = 1.0
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gold_invariants: list = field(default_factory=_primal_gold_invariants, compare=False)
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@property
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def supervised_autonomy_level(self) -> float:
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return float(self.autonomy)
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def residual(self, F: np.ndarray) -> float:
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"""Compute the primary GoldTether residual. Always ≥ 0. Dual-corrected."""
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return coherence_residual(F)
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def update(
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self,
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F: np.ndarray,
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epistemic_elevation: bool = False,
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) -> Tuple[float, float]:
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"""
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Update monitor with new field state.
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Returns (residual, new_autonomy).
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Dual-correction: residual is checked both ways inside residual().
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"""
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r = self.residual(F)
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if r > self.epsilon_drift:
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# Fail-closed: force autonomy to zero
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self.autonomy = 0.0
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self.floor = max(0.0, self.floor - self.floor_decay)
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else:
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if epistemic_elevation:
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# Only proven elevation may raise the floor
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self.floor = min(1.0, self.floor + self.floor_step)
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# Autonomy may never exceed the floor
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self.autonomy = min(self.autonomy + self.autonomy_step, self.floor)
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self.history.append((float(r), float(self.floor), float(self.autonomy)))
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if len(self.history) > self.max_history:
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self.history.pop(0)
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return float(r), float(self.autonomy)
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def may_relax_hitl(self) -> bool:
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"""Hard gate: only true when residual is safe AND floor is high enough."""
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if not self.history:
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return False
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last_r, last_floor, last_auto = self.history[-1]
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return (
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last_r < self.epsilon_drift
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and last_floor >= self.hitl_floor_threshold
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and last_auto >= self.hitl_autonomy_threshold
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)
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def force_reset(self) -> None:
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"""Emergency fail-closed. Callable by HITL or safety pack only."""
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self.autonomy = 0.0
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self.floor = 0.0
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self.history.clear()
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# --- Harmonized residual + alpha control law (ADR-0238 §2.3) ---------------
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def goldtether_residual(self, F: np.ndarray) -> float:
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"""Scale-harmonized coherence residual (ADR-0238 §2.3):
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R = w·(drift / ε_drift) + (1−w)·(min_{I∈𝓘_gold} ‖F−I‖_F / ‖F‖_F)
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The algebraic drift term (normalized by the numerical floor ε_drift) and
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the geometric distance-to-gold term (normalized by ‖F‖) are each scaled to
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``[0, O(1)]`` so neither masks the other — the exact defect §2.3 exists to
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fix. This is the ALIGNMENT signal that drives the constraint weight α; the
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raw :func:`coherence_residual` stays the fail-closed *closure* gate.
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"""
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F_arr = _as_mv(F)
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# Unitary amplitude drift (wave substrate) + optional chiral readout.
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# Chiral charge is structurally ~0 on real even field-states (#19 family);
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# included as a non-negative integrity term so a future non-vacuous spinor
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# path can move the residual without a second API.
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wave = WaveManifold()
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drift = wave.measure_unitary_residual(F_arr)
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chiral = abs(float(wave.chiral_charge(F_arr)))
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drift_term = (
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(drift + chiral) / self.epsilon_drift
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if self.epsilon_drift > 0.0
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else (drift + chiral)
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)
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scale = float(np.linalg.norm(F_arr))
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if self.gold_invariants and scale > _NEAR_ZERO:
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min_dist = min(
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float(np.linalg.norm(F_arr - np.asarray(inv, dtype=np.float64)))
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for inv in self.gold_invariants
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)
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geo_term = min_dist / scale
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else:
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geo_term = 0.0
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w = float(self.w_drift)
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return float(w * drift_term + (1.0 - w) * geo_term)
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def alpha_constraint(
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self,
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F: np.ndarray,
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*,
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mode: OperatingMode | str = OperatingMode.PRACTICE,
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) -> float:
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"""Human-constraint weight ``α ∈ [0,1]`` for the supervised transition
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surface (R&D-Revised §2.3): ``α = Φ(R_gt; r_floor, r_critical)`` — a smooth
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step of the *instantaneous* harmonized residual — composed with the
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earned-autonomy ceiling and the serve-never-autonomous rule.
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``α = 0`` fully autonomous (trust self); ``α = 1`` full human override.
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Earned autonomy sets the FLOOR on α: the engine may never act more
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autonomously than it has earned over its trajectory, and SERVE is pinned
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to full override.
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"""
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op = OperatingMode(mode)
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if op is OperatingMode.SERVE:
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return 1.0
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r = self.goldtether_residual(F)
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lo, hi = float(self.r_floor), float(self.r_critical)
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if r <= lo:
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phi = 0.0
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elif r >= hi or hi <= lo:
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phi = 1.0
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else:
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phi = (r - lo) / (hi - lo)
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alpha_floor = 1.0 - float(self.autonomy)
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return float(min(1.0, max(phi, alpha_floor)))
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def supervised_transition(
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self,
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v_self: np.ndarray,
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v_constraint: np.ndarray,
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F: np.ndarray,
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*,
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mode: OperatingMode | str = OperatingMode.PRACTICE,
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) -> np.ndarray:
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"""Blend the engine's own transition ``v_self`` toward the human/gold
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``v_constraint`` by the residual-driven constraint weight α.
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``α=0 → v_self`` (autonomous), ``α=1 → v_constraint`` (override).
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Rides the exact geodesic (`supervised_blend`), so closure is preserved.
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"""
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alpha = self.alpha_constraint(F, mode=mode)
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return self.supervised_blend(v_self, v_constraint, alpha)
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def promotion_eligible(self, F: np.ndarray) -> bool:
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"""True iff F is closed and unit-residual (drift) is at/below ε_drift.
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Bootstrap gate (R&D §5): only coherent states are candidates for 𝓘_gold.
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Uses the dual-checked *closure* residual (not geo distance to gold), so
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novel closed states remain eligible for promotion. Does not authorize.
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"""
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F_arr = _as_mv(F)
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if float(versor_condition(F_arr)) >= _CLOSURE_TOL:
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return False
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return float(coherence_residual(F_arr)) <= float(self.epsilon_drift)
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def promote_gold_invariant(
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self,
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F: np.ndarray,
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*,
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authorized: bool = False,
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proof: Optional[GoldPromotionProof] = None,
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require_proof: bool = False,
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) -> None:
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"""Add a state versor to 𝓘_gold. CALLER-GATED (ADR-0092).
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- Without ``authorized=True``: refuse (proposal-only; proof alone is insufficient).
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- With authorize: refuse non-closed or high *drift* residual (live check —
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never trusts proof.closed / proof.residual as truth).
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- ``require_proof=True``: refuse if ``proof`` is missing.
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- Physics never self-signs reviews; ``proof`` is caller-supplied audit pin.
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"""
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if not authorized:
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raise ValueError(
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"promote_gold_invariant requires explicit authorization (ADR-0092 gate)"
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)
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if require_proof and proof is None:
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raise ValueError("promote_gold_invariant requires proof when require_proof=True")
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F_arr = _as_mv(F)
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cond = float(versor_condition(F_arr))
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# Closure residual only (geo distance to 𝓘_gold is expected for new axes).
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drift = float(coherence_residual(F_arr))
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if cond >= _CLOSURE_TOL or drift > float(self.epsilon_drift):
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raise ValueError(
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"promote_gold_invariant refused: not a closed versor "
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f"(versor_condition={cond:.3e}) or residual/drift {drift:.3e} "
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f"exceeds epsilon_drift={float(self.epsilon_drift)}"
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)
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self.gold_invariants.append(F_arr.copy())
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def prune_gold_invariants(
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self,
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max_size: int = 64,
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*,
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mode: PruneMode | str = "fifo",
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) -> None:
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"""Bound 𝓘_gold, always retaining the three primal seeds.
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Modes:
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* ``fifo`` — keep primals + most recent (#24).
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* ``principal_axes`` — keep primals + highest principal-energy non-primals
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(R&D §5 decay; coefficient PCA on the non-primal stack).
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``max_size < 3`` is clamped to 3 so primals are never stripped.
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"""
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mode_s = str(mode)
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if mode_s not in ("fifo", "principal_axes"):
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raise ValueError(f"prune_gold_invariants unknown mode: {mode_s!r}")
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max_size = max(3, int(max_size))
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if len(self.gold_invariants) <= max_size:
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return
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if mode_s == "fifo":
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primal = self.gold_invariants[:3]
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recent = self.gold_invariants[3:][-(max_size - 3) :]
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self.gold_invariants = primal + recent
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return
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self._prune_principal_axes(max_size)
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def _prune_principal_axes(self, max_size: int) -> None:
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"""R&D §5: retain primals + non-primals with highest principal-subspace energy.
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Stack non-primal 32-vectors as columns, take top eigen-directions of
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``XXᵀ/m``, score each member by squared projection onto that subspace,
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keep the top ``max_size - 3`` (stable by original index on ties).
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Differs from FIFO (last-N) whenever early high-energy axes outrank recent
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near-identity members.
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"""
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primal = list(self.gold_invariants[:3])
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rest = [
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np.asarray(v, dtype=np.float64).copy() for v in self.gold_invariants[3:]
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]
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n_keep = int(max_size) - 3
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if n_keep <= 0 or not rest:
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self.gold_invariants = primal
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return
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if len(rest) <= n_keep:
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self.gold_invariants = primal + rest
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return
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X = np.column_stack(rest) # (32, m)
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m = X.shape[1]
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# Gram on ambient 32-space (deterministic; no external deps).
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C = (X @ X.T) / float(max(m, 1))
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evals, evecs = np.linalg.eigh(C)
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# Leading subspace dimension: enough to distinguish members, ≤ n_keep.
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k = max(1, min(n_keep, m, N_COMPONENTS))
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order = np.argsort(evals)[::-1]
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basis = evecs[:, order[:k]] # (32, k)
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scored: list[tuple[float, int]] = []
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for i, v in enumerate(rest):
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coeff = basis.T @ v
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energy = float(coeff @ coeff)
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scored.append((energy, i))
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# Highest energy first; lower index wins ties (stable, anti-FIFO bias).
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scored.sort(key=lambda t: (-t[0], t[1]))
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keep_idx = sorted(i for _e, i in scored[:n_keep])
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kept = [rest[i] for i in keep_idx]
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# Non-primal retained members should stay closed when they entered as versors.
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for i, inv in enumerate(kept):
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cond = float(versor_condition(inv))
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if cond >= _CLOSURE_TOL:
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raise ValueError(
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f"prune principal_axes retained non-closed member[{i}]: "
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f"versor_condition={cond:.3e}"
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)
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self.gold_invariants = primal + kept
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def measure(self, F: np.ndarray, reference: Optional[np.ndarray] = None) -> CoherenceResidual:
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"""Structured residual (primary + optional geometric distance to reference)."""
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F_arr = _as_mv(F)
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primary = float(versor_unit_residual(F_arr))
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dual = float(versor_unit_residual(reverse(F_arr)))
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combined = max(primary, dual)
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if reference is not None:
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ref = _as_mv(reference, "reference")
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product = geometric_product(reverse(ref), F_arr).astype(np.float64)
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product[0] -= 1.0
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geo = float(np.linalg.norm(product))
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combined = max(combined, geo / (1.0 + geo))
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floor = max(self.floor, _NEAR_ZERO)
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kappa = float(1.0 / (1.0 + combined / floor)) if floor > 0 else 0.0
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return CoherenceResidual(
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primary=primary,
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dual=dual,
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combined=float(combined),
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kappa=kappa,
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)
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def decide(
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self,
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residual: float | CoherenceResidual,
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*,
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mode: OperatingMode | str = OperatingMode.PRACTICE,
|
||
) -> AutonomyDecision:
|
||
"""Map residual + mode to an autonomy band (HITL-safe defaults)."""
|
||
op = OperatingMode(mode)
|
||
r = float(residual.combined if isinstance(residual, CoherenceResidual) else residual)
|
||
if r > self.epsilon_drift or self.autonomy <= 0.0:
|
||
return AutonomyDecision(
|
||
band=AutonomyBand.FAIL_CLOSED,
|
||
residual=r,
|
||
floor=float(self.floor),
|
||
autonomy=float(self.autonomy),
|
||
mode=op,
|
||
reason="residual_or_autonomy_fail_closed",
|
||
)
|
||
if op is OperatingMode.SERVE:
|
||
# Serve never autonomous; HITL default.
|
||
return AutonomyDecision(
|
||
band=AutonomyBand.FAIL_CLOSED,
|
||
residual=r,
|
||
floor=float(self.floor),
|
||
autonomy=float(self.autonomy),
|
||
mode=op,
|
||
reason="serve_hitl_default",
|
||
)
|
||
if self.may_relax_hitl() and r < self.epsilon_drift:
|
||
return AutonomyDecision(
|
||
band=AutonomyBand.AUTONOMOUS,
|
||
residual=r,
|
||
floor=float(self.floor),
|
||
autonomy=float(self.autonomy),
|
||
mode=op,
|
||
reason="practice_may_relax_hitl",
|
||
)
|
||
return AutonomyDecision(
|
||
band=AutonomyBand.SUPERVISED_BLEND,
|
||
residual=r,
|
||
floor=float(self.floor),
|
||
autonomy=float(self.autonomy),
|
||
mode=op,
|
||
reason="practice_supervised",
|
||
)
|
||
|
||
def supervised_blend(
|
||
self,
|
||
source: np.ndarray,
|
||
target: np.ndarray,
|
||
alpha: float,
|
||
) -> np.ndarray:
|
||
"""Spin left-composition geodesic: out = rotor_power(R, α) * source."""
|
||
a = float(alpha)
|
||
if a < 0.0 or a > 1.0:
|
||
raise ValueError("alpha must be in [0, 1]")
|
||
src = _as_mv(source, "source")
|
||
tgt = _as_mv(target, "target")
|
||
if a <= _NEAR_ZERO:
|
||
out = src.copy()
|
||
elif a >= 1.0 - _NEAR_ZERO:
|
||
out = tgt.copy()
|
||
else:
|
||
R = word_transition_rotor(src, tgt)
|
||
R_a = rotor_power(R, a)
|
||
out = geometric_product(R_a, src).astype(np.float64)
|
||
cond = versor_condition(out)
|
||
if cond >= _CLOSURE_TOL:
|
||
raise ValueError(f"supervised_blend broke versor_condition: {cond:.3e}")
|
||
return out
|
||
|
||
def telemetry(self) -> dict[str, Any]:
|
||
"""Workbench-safe projection (autonomy floor channel)."""
|
||
last = self.history[-1] if self.history else (0.0, self.floor, self.autonomy)
|
||
return {
|
||
"schema_version": _TELEMETRY_SCHEMA,
|
||
"residual": float(last[0]),
|
||
"autonomy_floor": float(self.floor),
|
||
"supervised_autonomy_level": float(self.autonomy),
|
||
"may_relax_hitl": bool(self.may_relax_hitl()),
|
||
"epsilon_drift": float(self.epsilon_drift),
|
||
"n_history": len(self.history),
|
||
"history_tail": [
|
||
{"r": h[0], "floor": h[1], "autonomy": h[2]}
|
||
for h in self.history[-16:]
|
||
],
|
||
}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# ADR-0242 V1 — evidence-gated κ line search (optional, off-serve)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def propose_kappa_line_search(
|
||
residual_fn,
|
||
*,
|
||
lower: float = 0.1,
|
||
upper: float = 2.0,
|
||
evaluation_budget: int = 16,
|
||
objective_id: str = "goldtether_kappa",
|
||
objective_version: str = "v1",
|
||
) -> tuple[float, object]:
|
||
"""Optional κ search via Fibonacci section (ADR-0242 Phase 1 seam).
|
||
|
||
Returns ``(kappa, cert_or_failure)``. On failure, kappa is baseline 1.0.
|
||
Does **not** mutate GoldTetherMonitor state, COHERENT standing, or serve
|
||
autonomy — caller may record the result as telemetry only.
|
||
"""
|
||
from core.physics.fibonacci_search import (
|
||
BoundedUnimodalObjective,
|
||
fibonacci_section_search,
|
||
propose_kappa_from_search,
|
||
)
|
||
|
||
objective = BoundedUnimodalObjective(
|
||
lower=float(lower),
|
||
upper=float(upper),
|
||
evaluation_budget=int(evaluation_budget),
|
||
objective_id=str(objective_id),
|
||
objective_version=str(objective_version),
|
||
)
|
||
result = fibonacci_section_search(objective, residual_fn)
|
||
return propose_kappa_from_search(result)
|