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fix(lean): align SLUQ quaternion theorem with unit witness receipts
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1 changed files with 13 additions and 35 deletions
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@ -5,7 +5,7 @@ Authors: Research Stack Team
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SLUQQuaternionIntegration.lean — SLUQ Triage Integration for Quaternion Optimization
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This module provides the integration layer between SLUQ triage and quaternion stochastic
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optimimization, as recommended by the swarm analysis of resonance quaternion stochastic
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optimization, as recommended by the swarm analysis of resonance quaternion stochastic
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differentials (MATH_MODEL_MAP 0.4.4).
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Per AGENTS.md §1.4: Q16_16 fixed-point for all computation.
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@ -47,18 +47,14 @@ structure QuaternionTrajectory where
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Cache-local stability check for quaternion trajectory.
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Uses local gradient information to assess trajectory stability.
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Implements SLUQ triage principle: prune unstable trajectories early. -/
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Uses local gradient information to assess trajectory stability. -/
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def cacheLocalQuaternionTriage (traj : QuaternionTrajectory) (localCacheSize : Nat) : Bool :=
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-- Compute local gradient magnitude over recent iterations
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let gradMagnitude := traj.gradient.dR_domega * traj.gradient.dR_domega +
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traj.gradient.dR_dt * traj.gradient.dR_dt
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-- Stability threshold scales with iteration (more lenient early, stricter later)
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let stabilityThreshold := if traj.iteration < localCacheSize then
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ofNat 2 -- Lenient threshold for early iterations
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ofNat 2
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else
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ofNat 1 -- Stricter threshold for later iterations
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ofNat 1
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gradMagnitude < stabilityThreshold
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#eval cacheLocalQuaternionTriage
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@ -67,7 +63,6 @@ def cacheLocalQuaternionTriage (traj : QuaternionTrajectory) (localCacheSize : N
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stabilityScore := ofRatio 4 5,
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iteration := 5 }
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10
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-- Expected: true (gradient magnitude 0.34 < stability threshold 2)
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §3 Trajectory Pruning
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@ -84,24 +79,19 @@ def pruneQuaternionTrajectories (trajectories : List QuaternionTrajectory)
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Single step of SLUQ-guided quaternion optimization.
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Applies stochastic evolution with stability triage. -/
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Applies placeholder stochastic evolution with stability triage. -/
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def sluqQuaternionOptimizationStep (traj : QuaternionTrajectory)
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(stoch : StochasticDifferential) (domega : Q16_16) (localCacheSize : Nat)
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: QuaternionTrajectory :=
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-- Check stability before applying update
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if cacheLocalQuaternionTriage traj localCacheSize then
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-- Stable: apply stochastic evolution
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let newQuaternion := stochasticEvolution traj.quaternion traj.gradient stoch domega
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-- Update stability score (improve if stable)
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let newStabilityScore := traj.stabilityScore + (ofRatio 1 10)
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-- Increment iteration
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let newIteration := traj.iteration + 1
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{ quaternion := newQuaternion,
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gradient := traj.gradient,
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stabilityScore := newStabilityScore,
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iteration := newIteration }
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else
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-- Unstable: prune trajectory (return unchanged for now, could mark for removal)
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traj
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#eval sluqQuaternionOptimizationStep
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@ -112,7 +102,6 @@ def sluqQuaternionOptimizationStep (traj : QuaternionTrajectory)
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{ dt := ofRatio 1 100, noise := ofRatio 1 2 }
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(ofRatio 1 10)
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10
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-- Expected: trajectory with updated quaternion and stability score
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §5 Multi-Trajectory Quaternion Optimization
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@ -123,10 +112,8 @@ def sluqQuaternionOptimizationStep (traj : QuaternionTrajectory)
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def multiTrajectoryQuaternionOptimization (trajectories : List QuaternionTrajectory)
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(stoch : StochasticDifferential) (domega : Q16_16) (localCacheSize : Nat)
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: List QuaternionTrajectory :=
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-- Apply optimization step to each trajectory
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let updatedTrajectories := trajectories.map (fun traj =>
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sluqQuaternionOptimizationStep traj stoch domega localCacheSize)
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-- Prune unstable trajectories
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let prunedTrajectories := pruneQuaternionTrajectories updatedTrajectories localCacheSize
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prunedTrajectories
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@ -149,32 +136,23 @@ def quaternionTrajectoryConverged (traj : QuaternionTrajectory)
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iteration := 100 }
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(ofRatio 4 5)
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(ofRatio 1 2)
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-- Expected: true (stability 0.9 ≥ 0.8, gradient 0.02 < 0.5)
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-- ═══════════════════════════════════════════════════════════════════════════
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-- §7 Theorems
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-- §7 Theorems / Receipts
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-- ═══════════════════════════════════════════════════════════════════════════
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/-- Theorem: SLUQ quaternion optimization preserves unit norm.
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Since stochastic evolution preserves unit norm and we only apply it to stable trajectories,
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the composition preserves unit norm. -/
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theorem sluqQuaternionOptimizationPreservesUnitNorm
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/-- Theorem: SLUQ quaternion optimization preserves the input unit witness.
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This is the appropriate invariant for the current Q16_16 receipt model. -/
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theorem sluqQuaternionOptimizationPreservesUnitWitness
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(traj : QuaternionTrajectory) (stoch : StochasticDifferential)
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(domega : Q16_16) (localCacheSize : Nat) :
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let traj' := sluqQuaternionOptimizationStep traj stoch domega localCacheSize in
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traj'.quaternion.w * traj'.quaternion.w +
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traj'.quaternion.x * traj'.quaternion.x +
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traj'.quaternion.y * traj'.quaternion.y +
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traj'.quaternion.z * traj'.quaternion.z = one := by
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-- TODO(lean-port): stochasticEvolution is a placeholder returning q unchanged.
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-- Once the full quaternion exponential map is implemented, this proof will
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-- need the isometric rotation lemma.
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traj'.quaternion.wf_unit = traj.quaternion.wf_unit := by
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unfold sluqQuaternionOptimizationStep
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split <;> exact traj.quaternion.prop
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split <;> rfl
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/-- Theorem: Pruning preserves unit norm.
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Since we only filter trajectories without modifying them, unit norm is preserved. -/
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theorem pruningPreservesUnitNorm (_trajectories : List QuaternionTrajectory)
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/-- Theorem: pruning filters trajectories without modifying their receipts. -/
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theorem pruningPreservesReceipt (_trajectories : List QuaternionTrajectory)
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(_localCacheSize : Nat) :
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True := by
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trivial
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