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480 lines
18 KiB
Text
480 lines
18 KiB
Text
/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
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Released under Apache 2.0 license as described in the file LICENSE.
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Authors: Research Stack Team
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DomainKernel.lean — Generic Trajectory Kernel with Domain Adapters
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Architecture:
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1. Generic Kernel (domain-agnostic)
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- candidate generation
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- scoring via J(n)
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- stabilization
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- pruning (ACI-NMS)
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- propagation (butterfly gossip)
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- promotion
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2. Domain Adapter (domain-specific)
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- state encoding
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- local features
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- coupling features
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- admissibility constraints
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- visibility/importance signal
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Per user specification: One reusable machine, not one universal ontology.
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Axis 11 = the shared trajectory interface between domains.
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Domains:
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• Astrophysics: mass field, mirror asymmetry, neighborhood coupling
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• Neural: spike/state encoding, local activation, topological burst
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• Maritime: vessel state, tide/noise signal, path visibility
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-/
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import Semantics.SSMS_nD
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import Semantics.UniversalCoupling
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namespace Semantics.DomainKernel
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open Semantics.SSMS
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open Semantics.SSMS_nD
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open Semantics.UniversalCoupling
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-- ════════════════════════════════════════════════════════════
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-- §0 Cell and Patch Types
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-- ════════════════════════════════════════════════════════════
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/-- Grid cell for kernel routing. -/
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structure Cell where
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t : UInt8
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sigma : Bool
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h : Q1616
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s : Q1616
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p_next : UInt8
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deriving Repr, Inhabited
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/-- Patch applied to a cell. -/
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structure CellPatch where
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deltaH : Q1616
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deltaS : Q1616
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deriving Repr, Inhabited
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/-- Admissibility check for a patch on a cell. -/
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def cellPatchAdmissible (_cell : Cell) (_patch : CellPatch) : Bool :=
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true -- TODO(lean-port): Define actual admissibility predicate
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/-- Payload carrying both a gossip packet and a patch. -/
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structure KernelPayload where
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packet : GossipPacket
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patch : CellPatch
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deriving Repr, Inhabited
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-- ════════════════════════════════════════════════════════════
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-- §1 Generic Kernel Interface (Domain-Agnostic)
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-- ════════════════════════════════════════════════════════════
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/-- Generic kernel input — all domains reduce to this. -/
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structure KernelInput where
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cell : Cell
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payloads : Array KernelPayload
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signal : CoarseSignal
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visibility : Visibility
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topo : TopoState
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self : Q1616
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nbrMean : Q1616
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prev : Q1616
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budget : Nat
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lambda : Q1616 -- phantom coupling parameter
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deriving Repr, Inhabited
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/-- Generic kernel output — trajectory decision. -/
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structure KernelOutput where
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chosen : Option KernelPayload
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applied : Option CellPatch
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score : Q1616
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coupling : Q1616
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promoted : Bool
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tunneled : Bool
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admissible : Bool
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budgetNext : Nat
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deriving Repr, Inhabited
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/-- The generic kernel step — domain-agnostic pathing engine.
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Implements: evaluate → propagate → prune → promote -/
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def stepKernel (x : KernelInput) : KernelOutput :=
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-- §1.1 Generate candidates from payloads
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let candidates := x.payloads.filter (fun p =>
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cellPatchAdmissible x.cell p.patch)
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-- §1.2 Score candidates via PhantomTideQ
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let scored := candidates.map (fun p =>
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let j := couplingPhantom x.lambda p.packet.energy x.signal.payload.energy x.signal.coherence
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let score := finalScorePhantom p.packet.energy j
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(p, score, j))
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-- §1.3 Select best (argmax via foldl)
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let best := scored.foldl (fun best (p, s, j) =>
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if s.raw > best.2.1.raw then (some p, s, j) else best
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) (none, Q1616.zero, Q1616.zero)
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-- §1.4 Route decision
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let chosen := best.1
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let score := best.2.1
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let coupling := best.2.2
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let admissible := chosen.isSome &&
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cellPatchAdmissible x.cell (chosen.get!.patch)
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let promoted := admissible &&
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shouldPromotePhantom x.lambda Q1616.one score x.signal.payload.energy Q1616.zero x.prev
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let tunneled := admissible &&
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allowTunnelPhantom x.lambda score x.signal.payload.energy x.visibility.trust x.signal.coherence
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let budgetNext :=
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if admissible then dynamicGossipBudget coupling (x.budget + x.topo.epoch)
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else x.budget
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{ chosen := chosen
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, applied := if admissible then chosen.map (·.patch) else none
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, score := score
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, coupling := coupling
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, promoted := promoted
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, tunneled := tunneled
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, admissible := admissible
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, budgetNext := budgetNext
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}
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-- ════════════════════════════════════════════════════════════
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-- §2 Domain Adapter Interface
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-- ════════════════════════════════════════════════════════════
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/-- Domain adapter: each domain implements this to feed the kernel.
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σ = domain-specific state type -/
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structure DomainAdapter (σ : Type) where
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toCell : σ → Cell
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toSignal : σ → CoarseSignal
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toVisibility : σ → Visibility
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toTopo : σ → TopoState
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selfValue : σ → Q1616
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nbrMeanValue : σ → Q1616
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prevValue : σ → Q1616
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payloads : σ → Array KernelPayload
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admissible : σ → KernelPayload → Bool
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/-- Domain input: state + budget. -/
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structure DomainInput (σ : Type) where
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state : σ
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budget : Nat
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lambda : Q1616
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deriving Repr, Inhabited
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/-- Convert domain input to generic kernel input. -/
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def toKernelInput {σ : Type} (a : DomainAdapter σ) (x : DomainInput σ) : KernelInput :=
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{ cell := a.toCell x.state
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, payloads := a.payloads x.state
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, signal := a.toSignal x.state
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, visibility := a.toVisibility x.state
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, topo := a.toTopo x.state
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, self := a.selfValue x.state
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, nbrMean := a.nbrMeanValue x.state
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, prev := a.prevValue x.state
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, budget := x.budget
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, lambda := x.lambda
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}
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/-- Run domain step: adapter feeds generic kernel. -/
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def runDomainStep {σ : Type} (a : DomainAdapter σ) (x : DomainInput σ) : KernelOutput :=
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stepKernel (toKernelInput a x)
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-- ════════════════════════════════════════════════════════════
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-- §3 Astrophysics Adapter
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-- Feeds: mass field, mirror asymmetry, neighborhood coupling
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-- ════════════════════════════════════════════════════════════
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/-- Astrophysical state: galaxy cluster particle. -/
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structure AstroState where
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position : Array Q1616 -- 3D spatial coordinates
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velocity : Array Q1616 -- 3D velocity
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mass : Q1616 -- particle mass
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asymmetry : Q1616 -- mirror asymmetry χ
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neighbors : Nat -- neighbor count for coupling
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pressure : Q1616 -- local pressure field
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curvature : Q1616 -- Ricci scalar approximation
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epoch : UInt8 -- simulation step
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deriving Repr, Inhabited
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def astroAdapter : DomainAdapter AstroState where
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toCell s :=
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{ t := s.epoch
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, sigma := true
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, h := ⟨s.mass.raw / 65536⟩ -- mass as normalized h
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, s := s.position.getD 0 Q1616.zero -- x-coordinate as scalar
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, p_next := 0
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}
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toSignal s :=
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{ payload :=
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{ energy := s.mass
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, sigma := true
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, sVal := s.asymmetry
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, version := 0
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, load := s.pressure
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, deltaH := s.curvature
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}
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, velocity := Q1616.zero
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, coherence := s.pressure
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}
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toVisibility s :=
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{ trust := ⟨255⟩ -- max trust
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, depth := ⟨10⟩ -- deep field
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, nbrCount := s.neighbors
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}
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toTopo s :=
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{ index := ⟨s.epoch.toNat % 16, by omega⟩
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, partition := 0
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, epoch := s.epoch.toNat
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}
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selfValue s := s.mass
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nbrMeanValue s := ⟨s.neighbors * 4096⟩ -- normalized neighbor coupling
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prevValue s := s.velocity.getD 0 Q1616.zero
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payloads s := #[] -- empty for N-body (gravity only)
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admissible _ _ := true -- all mass admissible
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-- ════════════════════════════════════════════════════════════
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-- §4 Neural Adapter
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-- Feeds: spike/state encoding, local activation, topological burst
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-- ════════════════════════════════════════════════════════════
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/-- Neural state: population of neurons. -/
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structure NeuralState where
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membranePot : Array Q1616 -- V_m for each neuron
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spikeHist : Array Q1616 -- recent spike counts
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synWeights : Array Q1616 -- synaptic coupling matrix (flattened)
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burstDetect : Q1616 -- topological burst metric
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learningVis : Q1616 -- learning rate visibility
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topoIndex : UInt16 -- population index
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deriving Repr, Inhabited
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def neuralAdapter : DomainAdapter NeuralState where
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toCell s :=
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let active := decide ((s.membranePot.getD 0 Q1616.zero).raw > 32768)
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{ t := 0
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, sigma := active -- active if V_m > 0.5
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, h := ⟨s.spikeHist.size * 256⟩ -- activity as h
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, s := s.membranePot.getD 0 Q1616.zero
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, p_next := 0
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}
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toSignal s :=
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let active := decide ((s.membranePot.getD 0 Q1616.zero).raw > 32768)
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{ payload :=
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{ energy := s.membranePot.foldl (fun acc v => Q1616.add acc v) Q1616.zero
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, sigma := active
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, sVal := s.burstDetect
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, version := 0
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, load := Q1616.zero
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, deltaH := s.learningVis
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}
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, velocity := Q1616.zero
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, coherence := s.burstDetect
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}
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toVisibility s :=
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{ trust := ⟨200⟩ -- medium-high trust for learned patterns
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, depth := ⟨5⟩ -- intermediate depth
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, nbrCount := 8
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}
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toTopo s :=
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{ index := ⟨s.topoIndex.toNat % 16, by omega⟩
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, partition := 0
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, epoch := 0
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}
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selfValue s := s.membranePot.getD 0 Q1616.zero
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nbrMeanValue s := s.spikeHist.foldl (fun acc v => Q1616.add acc v) Q1616.zero
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prevValue s := s.membranePot.getD 1 Q1616.zero
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payloads s := #[] -- spike packets generated externally
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admissible _ _ := true
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-- ════════════════════════════════════════════════════════════
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-- §5 Maritime Adapter
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-- Feeds: vessel state, tide/noise signal, path visibility
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-- ════════════════════════════════════════════════════════════
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/-- Maritime state: vessel tracking. -/
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structure MaritimeState where
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position : Array Q1616 -- (x, y) surface coordinates
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velocity : Array Q1616 -- (vx, vy)
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massEst : Q1616 -- estimated vessel mass
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tideSignal : Q1616 -- tide pressure gradient
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aisSig : Array Q1616 -- AIS signature vector
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pathVis : Q1616 -- path visibility score
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phantomDet : Bool -- phantom signature detected
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epoch : UInt8 -- tracking step
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deriving Repr, Inhabited
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def maritimeAdapter : DomainAdapter MaritimeState where
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toCell s :=
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{ t := s.epoch
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, sigma := s.phantomDet
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, h := ⟨s.massEst.raw / 65536⟩
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, s := s.position.getD 0 Q1616.zero
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, p_next := 0
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}
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toSignal s :=
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{ payload :=
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{ energy := s.massEst
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, sigma := s.phantomDet
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, sVal := s.pathVis
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, version := 0
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, load := s.tideSignal
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, deltaH := s.pathVis
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}
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, velocity := Q1616.zero
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, coherence := s.tideSignal
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}
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toVisibility s :=
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{ trust := ⟨150⟩ -- moderate trust (noisy environment)
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, depth := ⟨3⟩ -- shallow (surface)
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, nbrCount := 4
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}
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toTopo s :=
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{ index := ⟨s.epoch.toNat % 16, by omega⟩
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, partition := 0
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, epoch := s.epoch.toNat
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}
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selfValue s := s.massEst
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nbrMeanValue s := s.velocity.foldl (fun acc v => Q1616.add acc v) Q1616.zero
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prevValue s := s.position.getD 1 Q1616.zero
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payloads s := #[]
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admissible _ _ := true
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-- ════════════════════════════════════════════════════════════
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-- §5.5 VarDimManifold Adapter
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-- ════════════════════════════════════════════════════════════
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def varDimAdapter : DomainAdapter VarDimManifold where
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toCell s :=
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{ t := 0
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, sigma := s.sigma
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, h := s.energy
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, s := s.center.getD 0 Q1616.zero
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, p_next := 0
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}
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toSignal s :=
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{ payload :=
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{ energy := s.energy
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, sigma := s.sigma
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, sVal := s.center.getD 0 Q1616.zero
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, version := 0
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, load := Q1616.zero
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, deltaH := Q1616.zero
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}
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, velocity := Q1616.zero
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, coherence := Q1616.one
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}
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toVisibility _ :=
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{ trust := ⟨200⟩
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, depth := ⟨5⟩
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, nbrCount := 8
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}
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toTopo s :=
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{ index := ⟨0, by omega⟩
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, partition := 0
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, epoch := 0
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}
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selfValue s := s.energy
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nbrMeanValue s := s.center.getD 0 Q1616.zero
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prevValue s := s.energy
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payloads _ := #[]
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admissible _ _ := true
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-- ════════════════════════════════════════════════════════════
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-- §6 Cross-Domain Benchmarking Interface
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-- ════════════════════════════════════════════════════════════
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/-- Benchmark metrics for kernel evaluation. -/
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structure BenchmarkMetrics where
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admissibleRate : Float -- patches passing admissibility
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appliedRate : Float -- patches actually applied
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promotionRate : Float -- promotions / total
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tunnelRate : Float -- tunnel events / total
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sigmaTotal : Float -- total Σ coupling
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sigmaMean : Float -- mean coupling
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activeRoutes : Nat -- number of active trajectories
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deriving Repr, Inhabited
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/-- Run benchmark on any domain. -/
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def runBenchmark {σ : Type} (a : DomainAdapter σ) (states : Array σ)
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(budget : Nat) (lambda : Q1616) : Array (KernelOutput × BenchmarkMetrics) :=
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states.map (fun s =>
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let input := { state := s, budget := budget, lambda := lambda : DomainInput σ }
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let output := runDomainStep a input
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let metrics :=
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{ admissibleRate := if output.admissible then 1.0 else 0.0
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, appliedRate := if output.applied.isSome then 1.0 else 0.0
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, promotionRate := if output.promoted then 1.0 else 0.0
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, tunnelRate := if output.tunneled then 1.0 else 0.0
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, sigmaTotal := Float.ofInt output.coupling.raw / 65536.0
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, sigmaMean := Float.ofInt output.score.raw / 65536.0
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, activeRoutes := output.budgetNext
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}
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(output, metrics))
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-- ════════════════════════════════════════════════════════════
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-- §7 Self-Typing: Kernel Recognition of Shared Structure
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-- ════════════════════════════════════════════════════════════
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/-- Self-typing predicate: state reduces to same kernel input structure. -/
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def isKernelReducible (σ : Type) (a : DomainAdapter σ) (s : σ) : Prop :=
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-- The adapter successfully produces all required kernel inputs
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∃ (cell : Cell) (sig : CoarseSignal) (vis : Visibility)
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(topo : TopoState) (self nbr prev : Q1616) (ps : Array KernelPayload),
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a.toCell s = cell ∧
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a.toSignal s = sig ∧
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a.toVisibility s = vis ∧
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a.toTopo s = topo ∧
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a.selfValue s = self ∧
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a.nbrMeanValue s = nbr ∧
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a.prevValue s = prev ∧
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a.payloads s = ps
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/-- Theorem: All three domain adapters are kernel-reducible.
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This is the grounded "self-typing" — not universal ontology,
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just observation that domains reduce to same interface. -/
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theorem astroIsKernelReducible (s : AstroState) : isKernelReducible AstroState astroAdapter s := by
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exists astroAdapter.toCell s
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exists astroAdapter.toSignal s
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exists astroAdapter.toVisibility s
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exists astroAdapter.toTopo s
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exists astroAdapter.selfValue s
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exists astroAdapter.nbrMeanValue s
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exists astroAdapter.prevValue s
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exists astroAdapter.payloads s
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all_goals simp [isKernelReducible]
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theorem neuralIsKernelReducible (s : NeuralState) : isKernelReducible NeuralState neuralAdapter s := by
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exists neuralAdapter.toCell s
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exists neuralAdapter.toSignal s
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exists neuralAdapter.toVisibility s
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exists neuralAdapter.toTopo s
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exists neuralAdapter.selfValue s
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exists neuralAdapter.nbrMeanValue s
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exists neuralAdapter.prevValue s
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exists neuralAdapter.payloads s
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all_goals simp [isKernelReducible]
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theorem maritimeIsKernelReducible (s : MaritimeState) : isKernelReducible MaritimeState maritimeAdapter s := by
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exists maritimeAdapter.toCell s
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exists maritimeAdapter.toSignal s
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exists maritimeAdapter.toVisibility s
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exists maritimeAdapter.toTopo s
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exists maritimeAdapter.selfValue s
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exists maritimeAdapter.nbrMeanValue s
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exists maritimeAdapter.prevValue s
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exists maritimeAdapter.payloads s
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all_goals simp [isKernelReducible]
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end Semantics.DomainKernel
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