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Samplinglib mathematical lesson

Actual PBPS finite-count complete-path law

AutoSamplingTheory.ExampleCases.ProximalBPS.ActualFiniteCountPath.actual_finite_count_path_law · theorem · Teaching coverage

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Mathematical statement

Let E be any finite-dimensional real inner-product Borel space, including dimension zero. Let V:E→ℝ be C², α,β≥0 and η∈ℝ with α>0, α≤β, α‖v‖²≤D²V(x)[v,v]≤β‖v‖² for every x,v∈E, η>0 and βη≤1. Fix any deterministic y,r∈E and initial phase z₀∈E×E. Put c=y−η∇V(r), Φ_t(x,p)=(c+cos(t)(x−c)+√η sin(t)p, −sin(t)(x−c)/√η+cos(t)p), g_r(x)=∇V(x)−∇V(r), S_r(x,p)=(x,p−2〈p,g_r(x)〉g_r(x)/‖g_r(x)‖²), λ_r(x,p)=√η max{0,〈p,g_r(x)〉}, Λ_z(t)=∫₀ᵗλ_r(Φ_s(z))ds and τ_z(e)=inf{t≥0:Λ_z(t)≥e}∈[0,∞], with inf empty=∞. Total real division makes S_r the identity at g_r(x)=0. The record space is D=([0,∞)×(E×E))⊕{stopped}, with Borel sum structure. Define next(e,stopped)=stopped; for a live record (T,z), next(e,(T,z))=stopped if τ_z(e)=∞, and otherwise next(e,(T,z))=(T+τ_z(e),S_rΦ_(τ_z(e))(z)). In the Lean definition untopD0 is used only in this non-top branch. Define R₀(e)=live(0,z₀) and R_(j+1)(e)=next(e_j,R_j(e)). Let Ω=ℝ^ℕ with its product Borel structure and P=⊗_(k≥0)Exp(1). Let ε(w)_k=max{w_k,0}; this is an ambient coordinate extension on a P-null nonpositive-clock set, not new source dynamics for negative clocks. Write T_j(w)=a for a live record R_j(ε(w))=live(a,z), and T_j(w)=∞ for a stopped record. Define I_n(T)={T_n≤T<T_(n+1)}, and the total selector N(T,w)=min{n:w∈I_n(T)} when such an n exists, and 0 otherwise. For n≥0 let Hist_n=D^(Fin(n+1)) and H_n(w)_j=R_j(ε(w)). Write readTime(live(a,z))=a and readTime(stopped)=0. The abstract guard A_n(T) consists of histories all of whose records are live, whose read times are strictly increasing and whose positive-index read times are strictly below T; it does not constrain record0 itself. Actual H_n starts at live(0,z₀). For h∈Hist_n set d_n(T,h)=max{T−readTime(h_n),0}. Define survival(live(a,z),u,e) to mean u<τ_z(max{e,0}); on stopped it is false. Define W(live(a,z),u)=exp(−Λ_z(u)); on stopped W=0. Put C_n(T)={w:H_n(w)∈A_n(T), survival(R_n(ε(w)),d_n(T,H_n(w)),w_n)}. For a fixed finite T≥0 let Path_T be all functions from [0,T] into E×E, with its coordinate-product sigma algebra. On every history, including unrealizable ones, define Ψ_n(T,h)(s) by starting at z₀ and visiting j=0,…,n in increasing order: a live record (a,z) with a≤s replaces the current value by Φ_(s−a)(z), and an ineligible live or stopped record leaves it unchanged. All elapsed times in this total definition use nonnegative subtraction. Define Δ_n(T)={t∈ℝ^n:0<t₁<⋯<t_n<T}, and let m_n be the comap of product Lebesgue measure to this subtype, without normalization. For t∈Δ_n(T), put s₀=0,s_i=t_i, z₀ equal to the prescribed initial phase and z_i=S_rΦ_(s_i−s_(i−1))(z_(i−1)). Set K_n(t)_j=live(s_j,z_j), Γ_n(T,t)=Ψ_n(T,K_n(t)), q_n(t)=ofReal(∏_(i=1)^n λ_r(Φ_(s_i−s_(i−1))(z_(i−1)))exp[−Λ_(z_(i−1))(s_i−s_(i−1))]), and ρ_n(T,t)=q_n(t)W(K_n(t)_n,d_n(T,K_n(t))). The literal toNNReal conversions agree with these nonnegative simplex times. There exists one actual phase representative Z(y,r,z₀,t,w), jointly Borel in all deterministic parameters, finite time and raw input, with the following properties. On every raw covering interval T_n(w)≤s<T_(n+1)(w), if R_n(ε(w))=live(a,z), then Z(s,w)=Φ_(s−a)(z). If no interval covers s, Z(s,w)=z₀. For each fixed parameter tuple, Z(0,w)=z₀ almost surely. For every n≥0 and finite T≥0, Ψ_n, Γ_n and ρ_n are jointly measurable for the stated sigma algebras in their deterministic parameters and history or simplex input. For each fixed parameter tuple, H_n, Z_T:w↦(s↦Z(s,w)), N(T,·) and C_n(T) are measurable. For every prescribed t and s∈[0,T], if s_j≤s and every later s_k>s, then Γ_n(T,t)(s)=Φ_(s−s_j)(z_j). For every raw w∈C_n(T), Ψ_n(T,H_n(w))=Z_T(w) as entire functions on [0,T]. For each fixed parameter tuple and T, on one P-full event simultaneously for every j>0 and every k≥0: T_j(w)≠T, N(T,w)=k if and only if w∈C_k(T), both sets {j>0:T_j(w)<T} and {j>0:T_j(w)≤T} are finite, and both cardinalities equal N(T,w). This is not a single no-jump event valid for all T. The raw sets C_k(T) are pairwise disjoint, and P(⋃_(k≥0)C_k(T))=1. The exact unnormalized joint history/path law is (H_n,Z_T)_*(P restricted to {N(T)=n})=(K_n,Γ_n)_*(ρ_n m_n). Equivalently, for every F:Hist_n×Path_T→[0,∞] measurable for the stated sigma algebras, ∫_{N(T)=n}F(H_n(w),Z_T(w))P(dw)=∫_{Δ_n(T)}F(K_n(t),Γ_n(t))ρ_n(T,t)m_n(dt). Moreover ∫ρ_n dm_n=P{N(T)=n}≤1. No test boundedness, integrability, positive event probability, provider of densities, independence, finite waits or process measurability is assumed. The result includes n=0,T=0, rank0, infinite waits and explicit raw exceptional fallbacks; at n=0 the terminal survival factor remains, so the mass is not generally one for T>0. This is an attributed ASTIS completion of the finite-count complete-path ingredient implicit in Appendix A.1(A.7), using Appendix A.2. It is a faithful mathematical restatement with disclosed rank-zero, extended-test and coordinate-product-path choices, not a quoted standalone theorem. Hazard stitching along the path, pre-jump left limits, Skorokhod compatibility, the infinite full A.7 expansion, restart/Markovness, invariance, hypocoercivity, implementation errors, expected query costs, full paper results and actual-input composition are separate remaining obligations.

\[(H_n,Z_T)_*(P|_{\{N(T)=n\}})=(K_n,\Gamma_n)_*(\rho_nm_n).\]
Lean statement · actual_finite_count_path_law

Let E be any finite-dimensional real inner-product Borel space, including dimension zero. Let V:E→ℝ be C², α,β≥0 and η∈ℝ with α>0, α≤β, α‖v‖²≤D²V(x)[v,v]≤β‖v‖² for every x,v∈E, η>0 and βη≤1. Fix any deterministic y,r∈E and initial phase z₀∈E×E. Put c=y−η∇V(r), Φ_t(x,p)=(c+cos(t)(x−c)+√η sin(t)p, −sin(t)(x−c)/√η+cos(t)p), g_r(x)=∇V(x)−∇V(r), S_r(x,p)=(x,p−2〈p,g_r(x)〉g_r(x)/‖g_r(x)‖²), λ_r(x,p)=√η max{0,〈p,g_r(x)〉}, Λ_z(t)=∫₀ᵗλ_r(Φ_s(z))ds and τ_z(e)=inf{t≥0:Λ_z(t)≥e}∈[0,∞], with inf empty=∞. Total real division makes S_r the identity at g_r(x)=0. The record space is D=([0,∞)×(E×E))⊕{stopped}, with Borel sum structure. Define next(e,stopped)=stopped; for a live record (T,z), next(e,(T,z))=stopped if τ_z(e)=∞, and otherwise next(e,(T,z))=(T+τ_z(e),S_rΦ_(τ_z(e))(z)). In the Lean definition untopD0 is used only in this non-top branch. Define R₀(e)=live(0,z₀) and R_(j+1)(e)=next(e_j,R_j(e)). Let Ω=ℝ^ℕ with its product Borel structure and P=⊗_(k≥0)Exp(1). Let ε(w)_k=max{w_k,0}; this is an ambient coordinate extension on a P-null nonpositive-clock set, not new source dynamics for negative clocks. Write T_j(w)=a for a live record R_j(ε(w))=live(a,z), and T_j(w)=∞ for a stopped record. Define I_n(T)={T_n≤T<T_(n+1)}, and the total selector N(T,w)=min{n:w∈I_n(T)} when such an n exists, and 0 otherwise. For n≥0 let Hist_n=D^(Fin(n+1)) and H_n(w)_j=R_j(ε(w)). Write readTime(live(a,z))=a and readTime(stopped)=0. The abstract guard A_n(T) consists of histories all of whose records are live, whose read times are strictly increasing and whose positive-index read times are strictly below T; it does not constrain record0 itself. Actual H_n starts at live(0,z₀). For h∈Hist_n set d_n(T,h)=max{T−readTime(h_n),0}. Define survival(live(a,z),u,e) to mean u<τ_z(max{e,0}); on stopped it is false. Define W(live(a,z),u)=exp(−Λ_z(u)); on stopped W=0. Put C_n(T)={w:H_n(w)∈A_n(T), survival(R_n(ε(w)),d_n(T,H_n(w)),w_n)}. For a fixed finite T≥0 let Path_T be all functions from [0,T] into E×E, with its coordinate-product sigma algebra. On every history, including unrealizable ones, define Ψ_n(T,h)(s) by starting at z₀ and visiting j=0,…,n in increasing order: a live record (a,z) with a≤s replaces the current value by Φ_(s−a)(z), and an ineligible live or stopped record leaves it unchanged. All elapsed times in this total definition use nonnegative subtraction. Define Δ_n(T)={t∈ℝ^n:0<t₁<⋯<t_n<T}, and let m_n be the comap of product Lebesgue measure to this subtype, without normalization. For t∈Δ_n(T), put s₀=0,s_i=t_i, z₀ equal to the prescribed initial phase and z_i=S_rΦ_(s_i−s_(i−1))(z_(i−1)). Set K_n(t)_j=live(s_j,z_j), Γ_n(T,t)=Ψ_n(T,K_n(t)), q_n(t)=ofReal(∏_(i=1)^n λ_r(Φ_(s_i−s_(i−1))(z_(i−1)))exp[−Λ_(z_(i−1))(s_i−s_(i−1))]), and ρ_n(T,t)=q_n(t)W(K_n(t)_n,d_n(T,K_n(t))). The literal toNNReal conversions agree with these nonnegative simplex times. There exists one actual phase representative Z(y,r,z₀,t,w), jointly Borel in all deterministic parameters, finite time and raw input, with the following properties. On every raw covering interval T_n(w)≤s<T_(n+1)(w), if R_n(ε(w))=live(a,z), then Z(s,w)=Φ_(s−a)(z). If no interval covers s, Z(s,w)=z₀. For each fixed parameter tuple, Z(0,w)=z₀ almost surely. For every n≥0 and finite T≥0, Ψ_n, Γ_n and ρ_n are jointly measurable for the stated sigma algebras in their deterministic parameters and history or simplex input. For each fixed parameter tuple, H_n, Z_T:w↦(s↦Z(s,w)), N(T,·) and C_n(T) are measurable. For every prescribed t and s∈[0,T], if s_j≤s and every later s_k>s, then Γ_n(T,t)(s)=Φ_(s−s_j)(z_j). For every raw w∈C_n(T), Ψ_n(T,H_n(w))=Z_T(w) as entire functions on [0,T]. For each fixed parameter tuple and T, on one P-full event simultaneously for every j>0 and every k≥0: T_j(w)≠T, N(T,w)=k if and only if w∈C_k(T), both sets {j>0:T_j(w)<T} and {j>0:T_j(w)≤T} are finite, and both cardinalities equal N(T,w). This is not a single no-jump event valid for all T. The raw sets C_k(T) are pairwise disjoint, and P(⋃_(k≥0)C_k(T))=1. The exact unnormalized joint history/path law is (H_n,Z_T)_*(P restricted to {N(T)=n})=(K_n,Γ_n)_*(ρ_n m_n). Equivalently, for every F:Hist_n×Path_T→[0,∞] measurable for the stated sigma algebras, ∫_{N(T)=n}F(H_n(w),Z_T(w))P(dw)=∫_{Δ_n(T)}F(K_n(t),Γ_n(t))ρ_n(T,t)m_n(dt). Moreover ∫ρ_n dm_n=P{N(T)=n}≤1. No test boundedness, integrability, positive event probability, provider of densities, independence, finite waits or process measurability is assumed. The result includes n=0,T=0, rank0, infinite waits and explicit raw exceptional fallbacks; at n=0 the terminal survival factor remains, so the mass is not generally one for T>0. This is an attributed ASTIS completion of the finite-count complete-path ingredient implicit in Appendix A.1(A.7), using Appendix A.2. It is a faithful mathematical restatement with disclosed rank-zero, extended-test and coordinate-product-path choices, not a quoted standalone theorem. Hazard stitching along the path, pre-jump left limits, Skorokhod compatibility, the infinite full A.7 expansion, restart/Markovness, invariance, hypocoercivity, implementation errors, expected query costs, full paper results and actual-input composition are separate remaining obligations.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

theorem actual_finite_count_path_law
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    actual_finite_count_path_statement hα hαβ hV hH hη hβη

Exact module and namespace context

Full Lean proposition (definition) · actual_finite_count_path_statement

This literal definition stores the complete proposition asserted above. It supplies no mathematical proof or additional hypothesis; the theorem proof must establish its value.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

Download module (.lean)
private def actual_finite_count_path_statement
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) : Prop := by
  classical
  exact
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
        (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
      fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let readTime : Rec → ℝ := fun r => match r with
      | Sum.inl q => (q.1 : ℝ)
      | Sum.inr _ => 0
    let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
      {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
    let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
    let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
      Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
    let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
      Fin.induction z₀ (fun i z =>
        S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
    let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Hist n := fun y xRef z₀ n T t j =>
      Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
    let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ := fun y xRef z₀ n T t =>
      ∏ i : Fin n,
        rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
          (zAt y xRef z₀ n T t i.castSucc)) *
        Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
          (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
    let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
      ENNReal.ofReal (qReal y xRef z₀ n T t)
    let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
      {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
        StrictMono (fun j => readTime (h j)) ∧
        ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
    let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
      fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
        (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
    let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
      fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
        (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
    let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
      if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
    let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
    let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
      fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
    let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
      fun y xRef r u e => r.elim
        (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
    let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
      fun y xRef r u => r.elim
        (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
    let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
      fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
        survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
    let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
    let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
      fun y xRef z₀ n T h s =>
        Fin.foldl (n + 1) (fun z j => (h j).elim
          (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
          (fun _ => z)) z₀
    let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
      fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
    let ρ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → ℝ≥0∞ :=
      fun y xRef z₀ n T t => q y xRef z₀ n T t *
        weight y xRef ((K y xRef z₀ n T t) ⟨n, Nat.lt_succ_self n⟩)
          (d n T (K y xRef z₀ n T t))
    ∃ Z : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → (E × E),
      Measurable (fun a : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
        Z a.1.1.1 a.1.1.2 a.1.2 a.2.1 a.2.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        ∀ n : ℕ, ∀ a : ℝ≥0 × (E × E), w ∈ I y xRef z₀ s n →
          record y xRef z₀ (ε w) n = Sum.inl a →
          Z y xRef z₀ s w = Φ y xRef ((s - a.1 : ℝ≥0) : ℝ) a.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        (¬ ∃ n, w ∈ I y xRef z₀ s n) → Z y xRef z₀ s w = z₀) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ᵐ w ∂P, Z y xRef z₀ 0 w = z₀) ∧
      (∀ n : ℕ, ∀ T : ℝ≥0,
        Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
          Ψ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
          Γ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
          ρ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        (∀ y xRef : E, ∀ z₀ : E × E,
          let H := history y xRef z₀ n
          let ZT : (ℕ → ℝ) → Path T := fun w s => Z y xRef z₀ s.val w
          Measurable H ∧ Measurable ZT ∧ Measurable (N y xRef z₀ T) ∧
          MeasurableSet (C y xRef z₀ n T) ∧
          (∀ t : Prefix n T, ∀ s : {s : ℝ≥0 // s ≤ T}, ∀ j : Fin (n + 1),
            (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j ≤ (s.val : ℝ) →
            (∀ k : Fin (n + 1), j < k → (s.val : ℝ) < (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) k) →
            Γ y xRef z₀ n T t s =
              Φ y xRef ((s.val : ℝ) - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j) (zAt y xRef z₀ n T t j)) ∧
          (∀ w ∈ C y xRef z₀ n T, Ψ y xRef z₀ n T (H w) = ZT w) ∧
          (∀ᵐ w ∂P, (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
            (∀ k : ℕ, N y xRef z₀ T w = k ↔ w ∈ C y xRef z₀ k T) ∧
            {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.Finite ∧
            {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.Finite ∧
            N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.ncard ∧
            N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.ncard) ∧
          (Pairwise fun i j : ℕ => Disjoint (C y xRef z₀ i T) (C y xRef z₀ j T)) ∧
          P (⋃ k : ℕ, C y xRef z₀ k T) = 1 ∧
          Measure.map (fun w => (H w, ZT w)) (P.restrict {w | N y xRef z₀ T w = n}) =
            Measure.map (fun t => (K y xRef z₀ n T t, Γ y xRef z₀ n T t))
              ((m n T).withDensity (ρ y xRef z₀ n T)) ∧
          (∀ F : Hist n × Path T → ℝ≥0∞, Measurable F →
            (∫⁻ w in {w | N y xRef z₀ T w = n}, F (H w, ZT w) ∂P) =
              ∫⁻ t : Prefix n T, F (K y xRef z₀ n T t, Γ y xRef z₀ n T t) *
                ρ y xRef z₀ n T t ∂m n T) ∧
          (∫⁻ t : Prefix n T, ρ y xRef z₀ n T t ∂m n T) = P {w | N y xRef z₀ T w = n} ∧
          P {w | N y xRef z₀ T w = n} ≤ 1))

Exact module and namespace context

Proof roadmap

  1. 1Construct a total measurable finite-history interpolation

    Fix one coordinate s of the path. Each tagged record either retains the accumulator or replaces it by the harmonic flow at s−a; the live/stopped tag and a≤s are Borel. Finite induction proves the fold measurable. The product sigma algebra then makes the entire function-valued map measurable by its coordinates. The last eligible record controls the fold, including when no record is eligible and z₀ remains. Reuse the actual harmonic-flow joint measurability to obtain Ψ for every parameter and arbitrary history.

  2. 2Convert conditional next-wait survival into a weighted history measure

    The actual conditional survival theorem applies at the history-dependent residual d=max(T−lastTime,0). For every Borel history event A, its probability with survival is the history integral of W over A. Apply this to B∩A for an arbitrary Borel test set B; measure extensionality identifies H_*(P|_{H∈A,survival})=(H_*P|_A).withDensity(W). Measurability is inherited from actual history, the tagged time observer and the actual hazard. No positive conditioning probability or finite wait is required, and W vanishes on the stopped tag.

  3. 3Attach the terminal factor to the ordered-prefix law

    Apply the weighted history identity to F(h,Ψ(h)). The actual ordered-prefix law applies to this nonnegative measurable function times W, giving the simplex integral of F(K,Ψ(K))qW. This is precisely F(K,Γ)ρ. Joint measurability of K, Ψ, q and the tagged terminal weight implies joint measurability of Γ and ρ for the stated sigma algebras. Equality of all nonnegative tests gives the joint history/interpolation pushforward, and F=1 gives its unnormalized mass P(C_n)≤1. This step does not yet identify C_n with a count event.

  4. 4Identify interpolation with one actual process on the raw event

    Take the existing jointly measurable physical-time representative Z, with its exact covered-arc and uncovered-fallback behavior. On C_n all records through n are live, and strict terminal survival gives T<T_(n+1), also when the wait is infinite. At any s≤T choose the greatest index j≤n whose record time is at most s; initialization supplies index0. Every later index is ineligible and the next event lies after s. The finite fold therefore equals the harmonic arc selected by the actual Z. Function extensionality gives whole-path equality on every raw C_n sample, without assuming a measurable diagonal in the uncountable path space.

  5. 5Make the total count measurable and relate interval indices to cardinalities

    Each half-open interval membership predicate is Borel because actual event times are jointly measurable. Their countable union is Borel. On that union the least successful index is measurable; use0 off it. Distinct half-open intervals cannot overlap for a monotone event-time sequence. If N selects the covering interval, monotonicity gives T_j≤T iff j≤N. In the absence of positive-index events exactly at T the strict and non-strict positive-index sets both equal {1,…,N}, hence are finite with cardinality N. The no-atom condition is an internal dependency supplied below.

  6. 6Read the prescribed path at its last eligible event

    Every ordered simplex time is nonnegative. The real time and nonnegative-time coercions therefore agree, and the last-eligible fold identity yields Γ(t)(s)=Φ_(s−s_j)(z_j). The condition requires s_j≤s and all later s_k>s; at an event time the selected state is the post-bounce z_j. It also covers n=0 and T=0.

  7. 7Exclude fixed-horizon endpoint atoms from the actual input

    A coordinate hyperplane {t_i=T} has zero finite-product Lebesgue measure. Comap to the simplex and multiplication by any nonnegative density preserve that null set. The prefix pushforward law transfers this to the actual history observer on the strict guard. Actual exponential inputs are simultaneously positive almost surely, and the actual finite recurrence then gives strict increases between live records. If the nth event equals T, the entire finite history belongs to the strict guard at T+1. Applying the guarded nullity there avoids excluding the endpoint by definition. A countable intersection over positive n proves no event exactly at this fixed T. No intersection over all real T is taken.

  8. 8Close count interpretation, disjointness and full probability

    On every raw C_n, the last time is at most T (initial time0 handles n=0), and survival makes the next time strictly greater than T: thus C_n⊆I_n. Conversely, on the common full event of positive clocks, fixed-T no-atoms and physical-time coverage, any covering interval has a live history with strictly increasing times and all positive-index times below T, and its residual wait survives to T. Hence I_n=C_n on that event for all n. The least covering index is unique, so N=n iff C_n and the preceding cardinality lemma applies. Raw C disjointness follows from C_n⊆I_n and monotonicity. Almost-sure coverage then makes their countable union have mass1.

  9. 9Assemble the sealed actual history/path law

    Keep the one actual Z and all original six assumptions. Its joint measurability gives each coordinate-measurable Z_T; combine this with H_n,N,Ψ,Γ,ρ measurability and prescribed path evaluation. The sets {N=n} and C_n agree almost surely, so their restricted measures agree. On C_n the whole paths Ψ(H_n) and Z_T agree pointwise. Substitution into the weighted joint pushforward and every nonnegative test identity gives the claimed actual law. The same almost-sure set equality transfers the mass formula ∫ρ=P(C_n) to P{N=n}. In particular the zero-event term retains terminal survival; it is not normalized to one for positive T.

Full assumptions and notation

All objects and hypotheses

  • Finite-dimensional real inner-product Borel space E, including the disclosed rank-zero extension.
  • Exactly six analytic assumptions: α>0, α≤β, V∈C², α‖v‖²≤D²V(x)[v,v]≤β‖v‖² for all x,v, η>0, βη≤1; α,β are nonnegative.
  • All finite n,T≥0 and deterministic y,r,z₀ are universal. The actual iid Exp(1) input, stopped recursion, count, history/path maps and density are literal definitions, not supplied certificates.
  • Path_T carries the coordinate-product sigma algebra. Each test F:Hist_n×Path_T→[0,∞] is arbitrary measurable for these sigma algebras.
  • Joint measurability, existence, initialization, endpoint nullity, finite counts, all laws and mass bounds are derived; no additional boundedness, integrability, positive mass, finite waiting time or independence premise.

Mathematical proof

1. Construct a total measurable finite-history interpolation

Fix one coordinate s of the path. Each tagged record either retains the accumulator or replaces it by the harmonic flow at s−a; the live/stopped tag and a≤s are Borel. Finite induction proves the fold measurable. The product sigma algebra then makes the entire function-valued map measurable by its coordinates. The last eligible record controls the fold, including when no record is eligible and z₀ remains. Reuse the actual harmonic-flow joint measurability to obtain Ψ for every parameter and arbitrary history.

\[\Psi_n(h)(s)=\Phi_{s-a_j}(z_j),\quad j=\max\{i\le n:h_i=\operatorname{live}(a_i,z_i),\ a_i\le s\},\quad\text{if this set is nonempty; otherwise }\Psi_n(h)(s)=z_0.\]
Corresponding Lean step
private theorem measurable_finite_fold {X Y : Type*}
    [MeasurableSpace X] [MeasurableSpace Y] (n : ℕ)
    (f : X → Y → Fin n → X)
    (hf : ∀ j, Measurable (fun p : X × Y => f p.1 p.2 j))
    (x : Y → X) (hx : Measurable x) :
    Measurable (fun y => Fin.foldl n (fun z j => f z y j) (x y)) := by
  induction n with
  | zero => simpa only [Fin.foldl_zero] using hx
  | succ n ih =>
    have hr := ih (fun z y j => f z y j.castSucc) (fun j => hf j.castSucc)
    simpa only [Fin.foldl_succ_last, Function.comp_def, id_eq] using
      (hf (Fin.last n)).comp (hr.prodMk measurable_id)

private theorem measurable_record_step {X Y : Type*}
    [MeasurableSpace X] [MeasurableSpace Y]
    (Φ : Y → ℝ≥0 → X → X)
    (hΦ : Measurable (fun p : Y × ℝ≥0 × X => Φ p.1 p.2.1 p.2.2))
    (s : ℝ≥0) :
    Measurable (fun p : ((ℝ≥0 × X) ⊕ Unit) × (X × Y) =>
      p.1.elim (fun a => if a.1 ≤ s then Φ p.2.2 (s - a.1) a.2 else p.2.1)
        (fun _ => p.2.1)) := by
  classical
  let e := MeasurableEquiv.sumProdDistrib (ℝ≥0 × X) Unit (X × Y)
  have hl : Measurable (fun p : (ℝ≥0 × X) × (X × Y) =>
      if p.1.1 ≤ s then Φ p.2.2 (s - p.1.1) p.1.2 else p.2.1) := by
    apply Measurable.ite (measurableSet_le measurable_fst.fst measurable_const)
    · exact hΦ.comp (measurable_snd.snd.prodMk
        ((measurable_const.sub measurable_fst.fst).prodMk measurable_fst.snd))
    · exact measurable_snd.fst
  convert (hl.sumElim (measurable_snd.fst :
    Measurable (fun p : Unit × (X × Y) => p.2.1))).comp e.measurable using 1
  funext p
  rcases p with ⟨a | a, q⟩ <;> rfl

private theorem measurable_history_interpolation {X Y : Type*}
    [MeasurableSpace X] [MeasurableSpace Y]
    (Φ : Y → ℝ≥0 → X → X)
    (hΦ : Measurable (fun p : Y × ℝ≥0 × X => Φ p.1 p.2.1 p.2.2))
    (n : ℕ) (T : ℝ≥0)
    (h : Y → Fin n → ((ℝ≥0 × X) ⊕ Unit)) (hh : Measurable h)
    (z₀ : Y → X) (hz₀ : Measurable z₀) :
    Measurable (fun y => fun s : {s : ℝ≥0 // s ≤ T} =>
      Fin.foldl n (fun z j => (h y j).elim
        (fun a => if a.1 ≤ s.val then Φ y (s.val - a.1) a.2 else z)
        (fun _ => z)) (z₀ y)) := by
  classical
  apply measurable_pi_lambda
  intro s
  apply measurable_finite_fold n _ _ z₀ hz₀
  intro j
  exact (measurable_record_step Φ hΦ s.val).comp
    (((measurable_pi_apply j).comp (hh.comp measurable_snd)).prodMk measurable_id)

/- The last eligible index, rather than a maximum chosen by an unmeasurable
path-space operation, determines each coordinate of the literal fold. -/

private theorem finite_fold_last_eligible {X : Type*} (n : ℕ)
    (b : Fin n → Prop) [DecidablePred b] (v : Fin n → X) (x : X)
    (j : Fin n) (hj : b j) (hafter : ∀ k : Fin n, j < k → ¬ b k) :
    Fin.foldl n (fun z k => if b k then v k else z) x = v j := by
  induction n with
  | zero => exact Fin.elim0 j
  | succ n ih =>
    induction j using Fin.lastCases with
    | last => simp only [Fin.foldl_succ_last, if_pos hj]
    | cast j =>
      rw [Fin.foldl_succ_last, if_neg (hafter (Fin.last n) (Fin.castSucc_lt_last j))]
      exact ih (fun k => b k.castSucc) (fun k => v k.castSucc) j hj
        (fun k hk => hafter k.castSucc (Fin.castSucc_lt_castSucc_iff.mpr hk))


/- Small constructor identity suggested by the independent fragment diagnostic.
Keep case elimination outside the actual theorem's large literal context. -/

private theorem terminal_elim_identity {X : Type*} (T : ℝ≥0)
    (g : X → ℝ≥0 → ℝ≥0∞) (r : (ℝ≥0 × X) ⊕ Unit) :
    r.elim (fun a => g a.2 (T - r.elim Prod.fst (fun _ => 0))) (fun _ => 0) =
      r.elim (fun a => g a.2 (T - a.1)) (fun _ => 0) := by
  cases r <;> rfl

private theorem measurable_terminal_record_weight {X Y : Type*}
    [MeasurableSpace X] [MeasurableSpace Y]
    (L : Y → X → ℝ≥0 → ℝ)
    (hL : Measurable (fun p : (Y × X) × ℝ≥0 => L p.1.1 p.1.2 p.2))
    (T : ℝ≥0) :
    Measurable (fun p : ((ℝ≥0 × X) ⊕ Unit) × Y =>
      p.1.elim (fun a => ENNReal.ofReal (Real.exp (-L p.2 a.2 (T - a.1)))) (fun _ => 0)) := by
  let e := MeasurableEquiv.sumProdDistrib (ℝ≥0 × X) Unit Y
  have hl : Measurable (fun p : (ℝ≥0 × X) × Y =>
      ENNReal.ofReal (Real.exp (-L p.2 p.1.2 (T - p.1.1)))) :=
    (hL.comp (f := fun p : (ℝ≥0 × X) × Y => ((p.2, p.1.2), T - p.1.1))
      ((measurable_snd.prodMk measurable_fst.snd).prodMk
        (measurable_const.sub measurable_fst.fst))).neg.exp.ennreal_ofReal
  convert (hl.sumElim (measurable_const : Measurable (fun _ : Unit × Y => (0 : ℝ≥0∞)))).comp
    e.measurable using 1
  funext p
  rcases p with ⟨a | a, q⟩ <;> rfl

private theorem real_time_history_fold {X : Type*} (Φ : ℝ → X → X)
    (n : ℕ) (t : Fin n → ℝ) (ht : ∀ k, 0 ≤ t k) (z : Fin n → X) (z₀ : X)
    (s : ℝ≥0) (j : Fin n) (hj : t j ≤ (s : ℝ))
    (hafter : ∀ k : Fin n, j < k → (s : ℝ) < t k) :
    Fin.foldl n (fun x k => if Real.toNNReal (t k) ≤ s then
      Φ (((s - Real.toNNReal (t k)) : ℝ≥0) : ℝ) (z k) else x) z₀ =
      Φ ((s : ℝ) - t j) (z j) := by
  have hcoe (k : Fin n) : (Real.toNNReal (t k) : ℝ) = t k := Real.coe_toNNReal (t k) (ht k)
  have hj' : Real.toNNReal (t j) ≤ s := by
    exact_mod_cast (show (Real.toNNReal (t j) : ℝ) ≤ (s : ℝ) by rw [hcoe]; exact hj)
  have ha (k : Fin n) (hjk : j < k) : ¬ Real.toNNReal (t k) ≤ s := by
    intro hk
    have hkr : (Real.toNNReal (t k) : ℝ) ≤ (s : ℝ) := by exact_mod_cast hk
    rw [hcoe] at hkr
    exact not_lt_of_ge hkr (hafter k hjk)
  rw [finite_fold_last_eligible n (fun k => Real.toNNReal (t k) ≤ s)
    (fun k => Φ (((s - Real.toNNReal (t k)) : ℝ≥0) : ℝ) (z k)) z₀ j hj' ha,
    NNReal.coe_sub hj', hcoe]

private theorem actual_history_interpolation_measurable
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
    let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
      fun y xRef z₀ n T h s =>
        Fin.foldl (n + 1) (fun z j => (h j).elim
          (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
          (fun _ => z)) z₀
    ∀ n : ℕ, ∀ T : ℝ≥0,
      Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
        Ψ p.1.1.1 p.1.1.2 p.1.2 n T p.2) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
  let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
    fun y xRef z₀ n T h s =>
      Fin.foldl (n + 1) (fun z j => (h j).elim
        (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
        (fun _ => z)) z₀
  change ∀ n : ℕ, ∀ T : ℝ≥0,
    Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
      Ψ p.1.1.1 p.1.1.2 p.1.2 n T p.2)
  have hf := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualHarmonicFlow.actual_harmonic_flow_laws
    hα hαβ hV hH hη hβη
  have hΦ : Measurable (fun a : (E × E) × ℝ × (E × E) =>
      Φ a.1.1 a.1.2 a.2.1 a.2.2) := hf.2.1
  intro n T
  let Y := ((E × E) × (E × E)) × Hist n
  have hΦY : Measurable (fun p : Y × ℝ≥0 × (E × E) =>
      Φ p.1.1.1.1 p.1.1.1.2 (p.2.1 : ℝ) p.2.2) :=
    hΦ.comp (f := fun p : Y × ℝ≥0 × (E × E) =>
      (p.1.1.1, (p.2.1 : ℝ), p.2.2)) (measurable_fst.fst.fst.prodMk
      ((measurable_coe_nnreal_real.comp measurable_snd.fst).prodMk measurable_snd.snd))
  exact measurable_history_interpolation
    (fun p : Y => fun t z => Φ p.1.1.1 p.1.1.2 (t : ℝ) z)
    hΦY (n + 1) T (fun p : Y => p.2) measurable_snd
    (fun p : Y => p.1.2) measurable_fst.snd

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

2. Convert conditional next-wait survival into a weighted history measure

The actual conditional survival theorem applies at the history-dependent residual d=max(T−lastTime,0). For every Borel history event A, its probability with survival is the history integral of W over A. Apply this to B∩A for an arbitrary Borel test set B; measure extensionality identifies H_*(P|_{H∈A,survival})=(H_*P|_A).withDensity(W). Measurability is inherited from actual history, the tagged time observer and the actual hazard. No positive conditioning probability or finite wait is required, and W vanishes on the stopped tag.

\[H_*(P|_{\{H\in A,\,survival\}})=(H_*P|_A)W.\]
Corresponding Lean step
private theorem localized_survival_history_measure {Ω H : Type*}
    [MeasurableSpace Ω] [MeasurableSpace H]
    (μ : Measure Ω) (history : Ω → H) (S : Set Ω) (weight : H → ℝ≥0∞)
    (hh : Measurable history)
    (hset : ∀ A : Set H, MeasurableSet A →
      μ (history ⁻¹' A ∩ S) = ∫⁻ h in A, weight h ∂Measure.map history μ)
    (A : Set H) (hA : MeasurableSet A) :
    Measure.map history (μ.restrict (history ⁻¹' A ∩ S)) =
      ((Measure.map history μ).restrict A).withDensity weight := by
  apply Measure.ext
  intro B hB
  rw [Measure.map_apply hh hB, Measure.restrict_apply (hB.preimage hh),
    withDensity_apply weight hB, Measure.restrict_restrict hB,
    ← hset (B ∩ A) (hB.inter hA)]
  congr 1
  ext w
  simp only [Set.mem_inter_iff, Set.mem_preimage]
  exact and_assoc.symm

private theorem actual_terminal_survival_history_tests
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
        (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
      fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
    let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
      fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
    let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
      fun y xRef r u e => r.elim
        (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
    let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
      fun y xRef r u => r.elim
        (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
    ∀ y xRef : E, ∀ z₀ : E × E, ∀ n : ℕ, ∀ T : ℝ≥0,
      ∀ A : Set (Hist n), MeasurableSet A →
      let H := history y xRef z₀ n
      let W : Hist n → ℝ≥0∞ := fun h =>
        weight y xRef (h ⟨n, Nat.lt_succ_self n⟩) (d n T h)
      let C : Set (ℕ → ℝ) := {w | H w ∈ A ∧
        survival y xRef (record y xRef z₀ (ε w) n) (d n T (H w)) (w n)}
      Measurable H ∧ Measurable W ∧ MeasurableSet C ∧
      Measure.map H (P.restrict C) = ((Measure.map H P).restrict A).withDensity W ∧
      (∀ F : Hist n → ℝ≥0∞, Measurable F →
        (∫⁻ w in C, F (H w) ∂P) = ∫⁻ h in A, F h * W h ∂Measure.map H P) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
  let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
    fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
  let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
    fun y xRef r u e => r.elim
      (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
  let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
    fun y xRef r u => r.elim
      (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
  change
    ∀ y xRef : E, ∀ z₀ : E × E, ∀ n : ℕ, ∀ T : ℝ≥0,
      ∀ A : Set (Hist n), MeasurableSet A →
      let H := history y xRef z₀ n
      let W : Hist n → ℝ≥0∞ := fun h =>
        weight y xRef (h ⟨n, Nat.lt_succ_self n⟩) (d n T h)
      let C : Set (ℕ → ℝ) := {w | H w ∈ A ∧
        survival y xRef (record y xRef z₀ (ε w) n) (d n T (H w)) (w n)}
      Measurable H ∧ Measurable W ∧ MeasurableSet C ∧
      Measure.map H (P.restrict C) = ((Measure.map H P).restrict A).withDensity W ∧
      (∀ F : Hist n → ℝ≥0∞, Measurable F →
        (∫⁻ w in C, F (H w) ∂P) = ∫⁻ h in A, F h * W h ∂Measure.map H P)
  intro y xRef z₀ n T A hA
  let H := history y xRef z₀ n
  let W : Hist n → ℝ≥0∞ := fun h =>
    weight y xRef (h ⟨n, Nat.lt_succ_self n⟩) (d n T h)
  let S₀ : Set (ℕ → ℝ) := {w |
    survival y xRef (record y xRef z₀ (ε w) n) (d n T (H w)) (w n)}
  let C : Set (ℕ → ℝ) := H ⁻¹' A ∩ S₀
  change Measurable H ∧ Measurable W ∧ MeasurableSet C ∧
    Measure.map H (P.restrict C) = ((Measure.map H P).restrict A).withDensity W ∧
    (∀ F : Hist n → ℝ≥0∞, Measurable F →
      (∫⁻ w in C, F (H w) ∂P) = ∫⁻ h in A, F h * W h ∂Measure.map H P)
  have hH : Measurable H :=
    (AutoSamplingTheory.ExampleCases.ProximalBPS.ActualFiniteHistoryTailLaw.actual_finite_history_tail_law
      hα hαβ hV hH hη hβη y xRef z₀ n).1
  have hd : Measurable (d n T) :=
    measurable_const.sub (((measurable_fst : Measurable
      (fun p : ℝ≥0 × (E × E) => p.1)).sumElim measurable_const).comp
        (measurable_pi_apply (⟨n, Nat.lt_succ_self n⟩ : Fin (n + 1))))
  have h88 := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualConditionalNextWaitSurvival.actual_conditional_next_wait_survival
    hα hαβ hV ‹∀ x v : E, (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2› hη hβη y xRef z₀ n (d n T) hd
  let weight' : Rec → ℝ≥0 → ℝ≥0∞ := fun r u => match r with
    | Sum.inl a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))
    | Sum.inr _ => 0
  let survival' : Rec → ℝ≥0 → ℝ → Prop := fun r u e => match r with
    | Sum.inl a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)
    | Sum.inr _ => False
  change MeasurableSet {p : Hist n × (ℕ → ℝ) |
      survival' (p.1 ⟨n, Nat.lt_succ_self n⟩) (d n T p.1) (p.2 0)} ∧
    Measurable (fun h : Hist n => weight' (h ⟨n, Nat.lt_succ_self n⟩) (d n T h)) ∧
    (∀ h : Hist n, weight' (h ⟨n, Nat.lt_succ_self n⟩) (d n T h) ≤ 1) ∧
    (∀ B : Set (Hist n), MeasurableSet B →
      P {w | H w ∈ B ∧ survival' (record y xRef z₀ (ε w) n) (d n T (H w)) (w n)} =
        ∫⁻ h in B, weight' (h ⟨n, Nat.lt_succ_self n⟩) (d n T h) ∂Measure.map H P) at h88
  have hweight : weight' = weight y xRef := by
    funext r u
    cases r <;> rfl
  have hsurvival : survival' = survival y xRef := by
    funext r u e
    cases r <;> rfl
  rw [hweight, hsurvival] at h88
  have hW : Measurable W := h88.2.1
  have hS : MeasurableSet S₀ := by
    have hm : Measurable (fun w : ℕ → ℝ => (H w, fun k : ℕ => w (n + k))) :=
      hH.prodMk (measurable_pi_lambda _ fun k => measurable_pi_apply (n + k))
    simpa only [Set.preimage_ofPred_eq, Nat.add_zero] using h88.1.preimage hm
  have hC : MeasurableSet C := (hA.preimage hH).inter hS
  have hset : ∀ B : Set (Hist n), MeasurableSet B →
      P (H ⁻¹' B ∩ S₀) = ∫⁻ h in B, W h ∂Measure.map H P := h88.2.2.2
  have hlaw := localized_survival_history_measure P H S₀ W hH hset A hA
  refine ⟨hH, hW, hC, hlaw, ?_⟩
  intro F hF
  rw [← lintegral_map hF hH, hlaw,
    lintegral_withDensity_eq_lintegral_mul _ hW hF]
  exact lintegral_congr fun h => mul_comm _ _

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

3. Attach the terminal factor to the ordered-prefix law

Apply the weighted history identity to F(h,Ψ(h)). The actual ordered-prefix law applies to this nonnegative measurable function times W, giving the simplex integral of F(K,Ψ(K))qW. This is precisely F(K,Γ)ρ. Joint measurability of K, Ψ, q and the tagged terminal weight implies joint measurability of Γ and ρ for the stated sigma algebras. Equality of all nonnegative tests gives the joint history/interpolation pushforward, and F=1 gives its unnormalized mass P(C_n)≤1. This step does not yet identify C_n with a count event.

\[\int_{C_n}F(H_n,\Psi_n(H_n))dP=\int_{\Delta_n}F(K_n,\Gamma_n)q_nW(K_n)dm_n.\]
Corresponding Lean step
private theorem actual_terminal_weighted_prefix_tests
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
        (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
      fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let readTime : Rec → ℝ := fun r => match r with
      | Sum.inl q => (q.1 : ℝ)
      | Sum.inr _ => 0
    let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
      {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
    let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
    let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
      Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
    let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
      Fin.induction z₀ (fun i z =>
        S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
    let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Hist n := fun y xRef z₀ n T t j =>
      Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
    let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ := fun y xRef z₀ n T t =>
      ∏ i : Fin n,
        rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
          (zAt y xRef z₀ n T t i.castSucc)) *
        Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
          (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
    let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
      ENNReal.ofReal (qReal y xRef z₀ n T t)
    let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
      {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
        StrictMono (fun j => readTime (h j)) ∧
        ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
    let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
    let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
      fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
    let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
      fun y xRef r u e => r.elim
        (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
    let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
      fun y xRef r u => r.elim
        (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
    let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
      fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
        survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
    let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
    let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
      fun y xRef z₀ n T h s =>
        Fin.foldl (n + 1) (fun z j => (h j).elim
          (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
          (fun _ => z)) z₀
    let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
      fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
    let ρ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → ℝ≥0∞ :=
      fun y xRef z₀ n T t => q y xRef z₀ n T t *
        weight y xRef ((K y xRef z₀ n T t) ⟨n, Nat.lt_succ_self n⟩)
          (d n T (K y xRef z₀ n T t))
    ∀ n : ℕ, ∀ T : ℝ≥0,
      Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
        Γ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
      Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
        ρ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E,
        MeasurableSet (C y xRef z₀ n T) ∧
        Measurable (ρ y xRef z₀ n T) ∧
        (∀ F : Hist n × Path T → ℝ≥0∞, Measurable F →
          (∫⁻ w in C y xRef z₀ n T,
            F (history y xRef z₀ n w, Ψ y xRef z₀ n T (history y xRef z₀ n w)) ∂P) =
              ∫⁻ t : Prefix n T, F (K y xRef z₀ n T t, Γ y xRef z₀ n T t) *
                ρ y xRef z₀ n T t ∂m n T) ∧
        Measure.map (fun w => (history y xRef z₀ n w,
          Ψ y xRef z₀ n T (history y xRef z₀ n w))) (P.restrict (C y xRef z₀ n T)) =
          Measure.map (fun t => (K y xRef z₀ n T t, Γ y xRef z₀ n T t))
            ((m n T).withDensity (ρ y xRef z₀ n T)) ∧
        (∫⁻ t : Prefix n T, ρ y xRef z₀ n T t ∂m n T) = P (C y xRef z₀ n T) ∧
        P (C y xRef z₀ n T) ≤ 1) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let readTime : Rec → ℝ := fun r => match r with
    | Sum.inl q => (q.1 : ℝ)
    | Sum.inr _ => 0
  let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
    {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
  let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
  let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
    Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
  let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
    Fin.induction z₀ (fun i z =>
      S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
  let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Hist n := fun y xRef z₀ n T t j =>
    Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
  let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ := fun y xRef z₀ n T t =>
    ∏ i : Fin n,
      rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
        (zAt y xRef z₀ n T t i.castSucc)) *
      Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
        (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
  let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
    ENNReal.ofReal (qReal y xRef z₀ n T t)
  let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
    {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
      StrictMono (fun j => readTime (h j)) ∧
      ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
  let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
  let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
    fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
  let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
    fun y xRef r u e => r.elim
      (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
  let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
    fun y xRef r u => r.elim
      (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
  let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
    fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
      survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
  let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
  let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
    fun y xRef z₀ n T h s =>
      Fin.foldl (n + 1) (fun z j => (h j).elim
        (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
        (fun _ => z)) z₀
  let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
    fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
  let ρ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → ℝ≥0∞ :=
    fun y xRef z₀ n T t => q y xRef z₀ n T t *
      weight y xRef ((K y xRef z₀ n T t) ⟨n, Nat.lt_succ_self n⟩)
        (d n T (K y xRef z₀ n T t))
  change
    ∀ n : ℕ, ∀ T : ℝ≥0,
      Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
        Γ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
      Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
        ρ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E,
        MeasurableSet (C y xRef z₀ n T) ∧
        Measurable (ρ y xRef z₀ n T) ∧
        (∀ F : Hist n × Path T → ℝ≥0∞, Measurable F →
          (∫⁻ w in C y xRef z₀ n T,
            F (history y xRef z₀ n w, Ψ y xRef z₀ n T (history y xRef z₀ n w)) ∂P) =
              ∫⁻ t : Prefix n T, F (K y xRef z₀ n T t, Γ y xRef z₀ n T t) *
                ρ y xRef z₀ n T t ∂m n T) ∧
        Measure.map (fun w => (history y xRef z₀ n w,
          Ψ y xRef z₀ n T (history y xRef z₀ n w))) (P.restrict (C y xRef z₀ n T)) =
          Measure.map (fun t => (K y xRef z₀ n T t, Γ y xRef z₀ n T t))
            ((m n T).withDensity (ρ y xRef z₀ n T)) ∧
        (∫⁻ t : Prefix n T, ρ y xRef z₀ n T t ∂m n T) = P (C y xRef z₀ n T) ∧
        P (C y xRef z₀ n T) ≤ 1)
  intro n T
  have h92 := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualOrderedPrefix.actual_ordered_prefix_law
    hα hαβ hV hH hη hβη n T
  have hA : MeasurableSet (A n T) := h92.2.1
  have hK : Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
      K p.1.1.1 p.1.1.2 p.1.2 n T p.2) := h92.2.2.1
  have hq : Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
      q p.1.1.1 p.1.1.2 p.1.2 n T p.2) := h92.2.2.2.1
  have hΨ : Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
      Ψ p.1.1.1 p.1.1.2 p.1.2 n T p.2) :=
    actual_history_interpolation_measurable hα hαβ hV hH hη hβη n T
  have hΓ : Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
      Γ p.1.1.1 p.1.1.2 p.1.2 n T p.2) :=
    hΨ.comp (f := fun p : ((E × E) × (E × E)) × Prefix n T =>
      (p.1, K p.1.1.1 p.1.1.2 p.1.2 n T p.2)) (measurable_fst.prodMk hK)
  have hΛ : Measurable (fun a : ((E × E) × (E × E)) × ℝ≥0 =>
      Λ a.1.1.1 a.1.1.2 a.1.2 a.2) :=
    (AutoSamplingTheory.ExampleCases.ProximalBPS.ActualHazardClock.actual_integrated_hazard_clock_laws
      hα hαβ hV hH hη hβη).2.1
  have hWjoint : Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
      weight p.1.1.1 p.1.1.2 (p.2 ⟨n, Nat.lt_succ_self n⟩) (d n T p.2)) := by
    have hg : Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
        (p.2 ⟨n, Nat.lt_succ_self n⟩).elim
          (fun a => ENNReal.ofReal (Real.exp (-Λ p.1.1.1 p.1.1.2 a.2 (T - a.1))))
          (fun _ => 0)) := (measurable_terminal_record_weight
      (fun y : E × E => Λ y.1 y.2) hΛ T).comp
      (f := fun p : ((E × E) × (E × E)) × Hist n =>
        (p.2 ⟨n, Nat.lt_succ_self n⟩, p.1.1))
      (((measurable_pi_apply (⟨n, Nat.lt_succ_self n⟩ : Fin (n+1))).comp measurable_snd).prodMk
        measurable_fst.fst)
    have he : (fun p : ((E × E) × (E × E)) × Hist n =>
        weight p.1.1.1 p.1.1.2 (p.2 ⟨n, Nat.lt_succ_self n⟩) (d n T p.2)) =
        (fun p : ((E × E) × (E × E)) × Hist n =>
          (p.2 ⟨n, Nat.lt_succ_self n⟩).elim
            (fun a => ENNReal.ofReal (Real.exp (-Λ p.1.1.1 p.1.1.2 a.2 (T - a.1))))
            (fun _ => 0)) :=
      funext (fun p => terminal_elim_identity T
        (fun z u => ENNReal.ofReal (Real.exp (-Λ p.1.1.1 p.1.1.2 z u)))
        (p.2 ⟨n, Nat.lt_succ_self n⟩))
    rw [he]
    exact hg
  have hρjoint : Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
      ρ p.1.1.1 p.1.1.2 p.1.2 n T p.2) :=
    hq.mul (hWjoint.comp (f := fun p : ((E × E) × (E × E)) × Prefix n T =>
      (p.1, K p.1.1.1 p.1.1.2 p.1.2 n T p.2)) (measurable_fst.prodMk hK))
  refine ⟨hΓ, hρjoint, ?_⟩
  intro y xRef z₀
  let H := history y xRef z₀ n
  let W : Hist n → ℝ≥0∞ := fun h =>
    weight y xRef (h ⟨n, Nat.lt_succ_self n⟩) (d n T h)
  have hST := actual_terminal_survival_history_tests hα hαβ hV hH hη hβη
    y xRef z₀ n T (A n T) hA
  have hHM : Measurable H := hST.1
  have hW : Measurable W := hST.2.1
  have hKM : Measurable (K y xRef z₀ n T) := hK.comp
    (f := fun t : Prefix n T => (((y,xRef),z₀),t)) (measurable_const.prodMk measurable_id)
  have hqM : Measurable (q y xRef z₀ n T) := hq.comp
    (f := fun t : Prefix n T => (((y,xRef),z₀),t)) (measurable_const.prodMk measurable_id)
  have hΨM : Measurable (Ψ y xRef z₀ n T) := hΨ.comp
    (f := fun h : Hist n => (((y,xRef),z₀),h)) (measurable_const.prodMk measurable_id)
  have hρM : Measurable (ρ y xRef z₀ n T) := hqM.mul (hW.comp hKM)
  have ht : ∀ F : Hist n × Path T → ℝ≥0∞, Measurable F →
      (∫⁻ w in C y xRef z₀ n T, F (H w, Ψ y xRef z₀ n T (H w)) ∂P) =
        ∫⁻ t : Prefix n T, F (K y xRef z₀ n T t, Γ y xRef z₀ n T t) *
          ρ y xRef z₀ n T t ∂m n T := by
    intro F hF
    have hFΨ : Measurable (fun h : Hist n => F (h, Ψ y xRef z₀ n T h)) :=
      hF.comp (measurable_id.prodMk hΨM)
    have htests := (h92.2.2.2.2 y xRef z₀).2.2.2.2.2.2.1
    calc
      _ = ∫⁻ h in A n T, F (h, Ψ y xRef z₀ n T h) * W h ∂Measure.map H P :=
        hST.2.2.2.2 _ hFΨ
      _ = ∫⁻ w in H ⁻¹' A n T, F (H w, Ψ y xRef z₀ n T (H w)) * W (H w) ∂P :=
        setLIntegral_map hA (hFΨ.mul hW) hHM
      _ = ∫⁻ t : Prefix n T,
          (F (K y xRef z₀ n T t, Ψ y xRef z₀ n T (K y xRef z₀ n T t)) *
            W (K y xRef z₀ n T t)) * q y xRef z₀ n T t ∂m n T :=
        htests _ (hFΨ.mul hW)
      _ = _ := by
        apply lintegral_congr
        intro t
        dsimp only [ρ, Γ, W]
        ac_rfl
  refine ⟨hST.2.2.1, hρM, ht, ?_, ?_, ?_⟩
  · apply Measure.ext_of_lintegral
    intro F hF
    have hJ : Measurable (fun w => (H w, Ψ y xRef z₀ n T (H w))) :=
      hHM.prodMk (hΨM.comp hHM)
    have hL : Measurable (fun t => (K y xRef z₀ n T t, Γ y xRef z₀ n T t)) :=
      hKM.prodMk (hΨM.comp hKM)
    have hFL : Measurable (fun t : Prefix n T =>
        F (K y xRef z₀ n T t, Γ y xRef z₀ n T t)) := hF.comp hL
    rw [lintegral_map hF hJ, lintegral_map hF hL,
      lintegral_withDensity_eq_lintegral_mul _ hρM hFL, ht F hF]
    exact lintegral_congr fun _ => mul_comm _ _
  · have hm := ht (fun _ => 1) measurable_const
    simpa only [one_mul, setLIntegral_one] using hm.symm
  · letI : IsProbabilityMeasure P :=
      AutoSamplingTheory.TechnicalLemmas.Probability.UnitExponentialProduct.unit_exponential_product_laws.1
    exact prob_le_one

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

4. Identify interpolation with one actual process on the raw event

Take the existing jointly measurable physical-time representative Z, with its exact covered-arc and uncovered-fallback behavior. On C_n all records through n are live, and strict terminal survival gives T<T_(n+1), also when the wait is infinite. At any s≤T choose the greatest index j≤n whose record time is at most s; initialization supplies index0. Every later index is ineligible and the next event lies after s. The finite fold therefore equals the harmonic arc selected by the actual Z. Function extensionality gives whole-path equality on every raw C_n sample, without assuming a measurable diagonal in the uncountable path space.

\[w\in C_n(T)\Longrightarrow\Psi_n(H_n(w))=Z_T(w).\]
Corresponding Lean step
private theorem finite_history_fold_eq_process {X : Type*}
    (Φ : ℝ≥0 → X → X) (r : ℕ → ((ℝ≥0 × X) ⊕ Unit)) (z₀ : X)
    (n : ℕ) (T : ℝ≥0)
    (hzero : r 0 = Sum.inl (0, z₀))
    (hlive : ∀ k : Fin (n + 1), ∃ a : ℝ≥0 × X, r k.val = Sum.inl a)
    (hend : (T : WithTop ℝ≥0) < (r (n + 1)).elim (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤))
    (Z : ℝ≥0 → X)
    (hZ : ∀ s : ℝ≥0, ∀ k : ℕ, ∀ a : ℝ≥0 × X,
      (r k).elim (fun b => (b.1 : WithTop ℝ≥0)) (fun _ => ⊤) ≤ (s : WithTop ℝ≥0) →
      (s : WithTop ℝ≥0) < (r (k+1)).elim (fun b => (b.1 : WithTop ℝ≥0)) (fun _ => ⊤) →
      r k = Sum.inl a → Z s = Φ (s - a.1) a.2) :
    ∀ s : ℝ≥0, s ≤ T →
      Fin.foldl (n + 1) (fun z j => (r j.val).elim
        (fun a => if a.1 ≤ s then Φ (s - a.1) a.2 else z) (fun _ => z)) z₀ = Z s := by
  classical
  intro s hs
  let time : ℕ → WithTop ℝ≥0 := fun k =>
    (r k).elim (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  let p : ℕ → Prop := fun k => time k ≤ (s : WithTop ℝ≥0)
  have hp0 : p 0 := by
    simp only [p, time, hzero, Sum.elim_inl]
    exact WithTop.coe_le_coe.mpr zero_le
  let k := Nat.findGreatest p n
  have hkn : k ≤ n := Nat.findGreatest_le n
  have hkp : p k := Nat.findGreatest_spec (Nat.zero_le n) hp0
  have hnext : (s : WithTop ℝ≥0) < time (k+1) := by
    by_cases hlast : k = n
    · rw [hlast]
      exact lt_of_le_of_lt (WithTop.coe_le_coe.mpr hs) hend
    · exact lt_of_not_ge (Nat.findGreatest_is_greatest (Nat.lt_succ_self k)
        (by omega : k + 1 ≤ n))
  let j : Fin (n + 1) := ⟨k, by omega⟩
  let a : Fin (n + 1) → ℝ≥0 × X := fun i => Classical.choose (hlive i)
  have ha (i : Fin (n + 1)) : r i.val = Sum.inl (a i) := Classical.choose_spec (hlive i)
  have hj : (a j).1 ≤ s := by
    have h := hkp
    change time j.val ≤ (s : WithTop ℝ≥0) at h
    simpa only [time, ha, Sum.elim_inl, WithTop.coe_le_coe] using h
  have hafter (i : Fin (n+1)) (hji : j < i) : ¬ (a i).1 ≤ s := by
    intro hi
    have hpi : p i.val := by
      simp only [p, time, ha, Sum.elim_inl]
      exact WithTop.coe_le_coe.mpr hi
    exact (Nat.findGreatest_is_greatest (show k < i.val from hji) (by omega)) hpi
  calc
    _ = Fin.foldl (n+1) (fun z i => if (a i).1 ≤ s then Φ (s - (a i).1) (a i).2 else z) z₀ := by
      congr 1
      funext z i
      rw [ha]
      rfl
    _ = Φ (s - (a j).1) (a j).2 :=
      finite_fold_last_eligible (n+1) (fun i => (a i).1 ≤ s)
        (fun i => Φ (s - (a i).1) (a i).2) z₀ j hj hafter
    _ = Z s := (hZ s k (a j) hkp hnext (ha j)).symm

private theorem terminal_event_after (T a : ℝ≥0) (q : WithTop ℝ≥0)
    (hq : ((T-a : ℝ≥0) : WithTop ℝ≥0) < q) :
    (T : WithTop ℝ≥0) < (if q = ⊤ then ⊤ else ((a + q.untopD 0 : ℝ≥0) : WithTop ℝ≥0)) := by
  classical
  by_cases htop : q = ⊤
  · rw [if_pos htop]
    exact WithTop.coe_lt_top T
  lift q to ℝ≥0 using htop
  simp only [WithTop.coe_ne_top, if_false, WithTop.untopD_coe, WithTop.coe_lt_coe] at hq ⊢
  calc
    T ≤ (T-a) + a := le_tsub_add
    _ < q + a := by simpa only [add_comm] using add_lt_add_right hq a
    _ = a + q := add_comm _ _

private theorem actual_history_path_identification
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
        (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
      fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let readTime : Rec → ℝ := fun r => match r with
      | Sum.inl q => (q.1 : ℝ)
      | Sum.inr _ => 0
    let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
      {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
        StrictMono (fun j => readTime (h j)) ∧
        ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
    let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
      fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
        (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
    let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
      fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
        (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
    let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
    let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
      fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
    let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
      fun y xRef r u e => r.elim
        (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
    let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
      fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
        survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
    let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
    let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
      fun y xRef z₀ n T h s =>
        Fin.foldl (n + 1) (fun z j => (h j).elim
          (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
          (fun _ => z)) z₀
    ∃ Z : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → (E × E),
      Measurable (fun a : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
        Z a.1.1.1 a.1.1.2 a.1.2 a.2.1 a.2.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        ∀ n : ℕ, ∀ a : ℝ≥0 × (E × E), w ∈ I y xRef z₀ s n →
          record y xRef z₀ (ε w) n = Sum.inl a →
          Z y xRef z₀ s w = Φ y xRef ((s - a.1 : ℝ≥0) : ℝ) a.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        (¬ ∃ n, w ∈ I y xRef z₀ s n) → Z y xRef z₀ s w = z₀) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ᵐ w ∂P, Z y xRef z₀ 0 w = z₀) ∧
      (∀ n : ℕ, ∀ T : ℝ≥0, ∀ y xRef : E, ∀ z₀ : E × E,
        ∀ w ∈ C y xRef z₀ n T,
          Ψ y xRef z₀ n T (history y xRef z₀ n w) =
            (fun s : {s : ℝ≥0 // s ≤ T} => Z y xRef z₀ s.val w)) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let readTime : Rec → ℝ := fun r => match r with
    | Sum.inl q => (q.1 : ℝ)
    | Sum.inr _ => 0
  let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
    {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
      StrictMono (fun j => readTime (h j)) ∧
      ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
  let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
    fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
    fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
      (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
  let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
  let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
    fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
  let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
    fun y xRef r u e => r.elim
      (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
  let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
    fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
      survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
  let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
  let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
    fun y xRef z₀ n T h s =>
      Fin.foldl (n + 1) (fun z j => (h j).elim
        (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
        (fun _ => z)) z₀
  change
    ∃ Z : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → (E × E),
      Measurable (fun a : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
        Z a.1.1.1 a.1.1.2 a.1.2 a.2.1 a.2.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        ∀ n : ℕ, ∀ a : ℝ≥0 × (E × E), w ∈ I y xRef z₀ s n →
          record y xRef z₀ (ε w) n = Sum.inl a →
          Z y xRef z₀ s w = Φ y xRef ((s - a.1 : ℝ≥0) : ℝ) a.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        (¬ ∃ n, w ∈ I y xRef z₀ s n) → Z y xRef z₀ s w = z₀) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ᵐ w ∂P, Z y xRef z₀ 0 w = z₀) ∧
      (∀ n : ℕ, ∀ T : ℝ≥0, ∀ y xRef : E, ∀ z₀ : E × E,
        ∀ w ∈ C y xRef z₀ n T,
          Ψ y xRef z₀ n T (history y xRef z₀ n w) =
            (fun s : {s : ℝ≥0 // s ≤ T} => Z y xRef z₀ s.val w))
  obtain ⟨Z, hZM, hZarc, hZfallback, hZgood⟩ :=
    AutoSamplingTheory.ExampleCases.ProximalBPS.ActualPhysicalTimeMeasurability.actual_physical_time_measurable_phase
      hα hαβ hV hH hη hβη
  refine ⟨Z, hZM, ?_, hZfallback, ?_, ?_⟩
  · intro y xRef z₀ w s n a hi hr
    exact hZarc y xRef z₀ w s n a hi.1 hi.2 hr
  · intro y xRef z₀
    filter_upwards [hZgood y xRef z₀] with w hw
    exact hw.2
  · intro n T y xRef z₀ w hw
    have hlive (k : Fin (n+1)) : ∃ a : ℝ≥0 × (E × E),
        record y xRef z₀ (ε w) k.val = Sum.inl a := hw.1.1 k
    obtain ⟨a, ha⟩ := hlive ⟨n, Nat.lt_succ_self n⟩
    have hsurv : ((T - a.1 : ℝ≥0) : WithTop ℝ≥0) < τ y xRef a.2 (ε w n) := by
      have hh := hw.2
      change survival y xRef (record y xRef z₀ (ε w) n)
        (T - liveTime (record y xRef z₀ (ε w) n)) (w n) at hh
      rw [ha] at hh
      exact hh
    have hnext : eventTime y xRef z₀ w (n+1) =
        (if τ y xRef a.2 (ε w n) = ⊤ then ⊤ else
          ((a.1 + (τ y xRef a.2 (ε w n)).untopD 0 : ℝ≥0) : WithTop ℝ≥0)) := by
      change (next y xRef (ε w n) (record y xRef z₀ (ε w) n)).elim _ _ = _
      rw [ha]
      dsimp only [next]
      split_ifs <;> rfl
    have hend : (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n+1) := by
      rw [hnext]
      exact terminal_event_after T a.1 (τ y xRef a.2 (ε w n)) hsurv
    funext s
    exact finite_history_fold_eq_process (fun u => Φ y xRef (u : ℝ))
      (record y xRef z₀ (ε w)) z₀ n T rfl hlive hend
      (fun u => Z y xRef z₀ u w)
      (fun u k a hlo hhi hr => hZarc y xRef z₀ w u k a hlo hhi hr) s.val s.property

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

5. Make the total count measurable and relate interval indices to cardinalities

Each half-open interval membership predicate is Borel because actual event times are jointly measurable. Their countable union is Borel. On that union the least successful index is measurable; use0 off it. Distinct half-open intervals cannot overlap for a monotone event-time sequence. If N selects the covering interval, monotonicity gives T_j≤T iff j≤N. In the absence of positive-index events exactly at T the strict and non-strict positive-index sets both equal {1,…,N}, hence are finite with cardinality N. The no-atom condition is an internal dependency supplied below.

\[N(T)=n\ \Longleftrightarrow\ C_n(T)\quad P\text{-a.s.},\qquad N(T)=\#\{j>0:T_j<T\}=\#\{j>0:T_j\le T\}.\]
Corresponding Lean step
private theorem find_decidable_eq (p : ℕ → Prop) (d₁ d₂ : DecidablePred p)
    (h : ∃ n, p n) : @Nat.find p d₁ h = @Nat.find p d₂ h := by
  have hd : d₁ = d₂ := Subsingleton.elim _ _
  cases hd
  rfl

private theorem measurable_first_available {Ω : Type*} [MeasurableSpace Ω]
    (I : ℕ → Set Ω) (hI : ∀ n, MeasurableSet (I n)) :
    Measurable (fun w => if h : ∃ n, w ∈ I n then Nat.find h else 0) := by
  classical
  let U : Set Ω := {w | ∃ n, w ∈ I n}
  have hU : MeasurableSet U := by
    change MeasurableSet {w | ∃ n, w ∈ I n}
    simpa only [Set.ofPred_exists, Set.ofPred_mem_eq] using MeasurableSet.iUnion hI
  have hm : Measurable (fun w : U => Nat.find w.property) :=
    measurable_find (fun w : U => w.property)
      (fun n => (hI n).preimage measurable_subtype_coe)
  exact hm.dite measurable_const hU

private theorem monotone_intervals_unique (t : ℕ → WithTop ℝ≥0) (ht : Monotone t)
    (T : ℝ≥0) (i j : ℕ) (hi : t i ≤ T ∧ (T : WithTop ℝ≥0) < t (i + 1))
    (hj : t j ≤ T ∧ (T : WithTop ℝ≥0) < t (j + 1)) : i = j := by
  by_contra hne
  rcases lt_or_gt_of_ne hne with hij | hji
  · exact (not_lt_of_ge (le_trans (ht (Nat.succ_le_of_lt hij)) hj.1)) hi.2
  · exact (not_lt_of_ge (le_trans (ht (Nat.succ_le_of_lt hji)) hi.1)) hj.2

private theorem interval_count_cardinality (t : ℕ → WithTop ℝ≥0) (ht : Monotone t)
    (T : ℝ≥0) (N : ℕ) (hN : t N ≤ T ∧ (T : WithTop ℝ≥0) < t (N + 1))
    (hno : ∀ j : ℕ, 0 < j → t j ≠ (T : WithTop ℝ≥0)) :
    {j : ℕ | 0 < j ∧ t j < (T : WithTop ℝ≥0)}.Finite ∧
    {j : ℕ | 0 < j ∧ t j ≤ (T : WithTop ℝ≥0)}.Finite ∧
    N = {j : ℕ | 0 < j ∧ t j < (T : WithTop ℝ≥0)}.ncard ∧
    N = {j : ℕ | 0 < j ∧ t j ≤ (T : WithTop ℝ≥0)}.ncard := by
  have hle (j : ℕ) : t j ≤ (T : WithTop ℝ≥0) ↔ j ≤ N := by
    constructor
    · intro hj
      by_contra h
      exact not_lt_of_ge (le_trans (ht (by omega)) hj) hN.2
    · intro hj
      exact le_trans (ht hj) hN.1
  have hclosed : {j : ℕ | 0 < j ∧ t j ≤ (T : WithTop ℝ≥0)} = Set.Ioc 0 N := by
    ext j
    simp only [Set.mem_ofPred_eq, Set.mem_Ioc, hle]
  have hopen : {j : ℕ | 0 < j ∧ t j < (T : WithTop ℝ≥0)} = Set.Ioc 0 N := by
    ext j
    simp only [Set.mem_ofPred_eq, Set.mem_Ioc]
    constructor
    · rintro ⟨hj, htj⟩
      exact ⟨hj, (hle j).mp htj.le⟩
    · rintro ⟨hj, hjN⟩
      exact ⟨hj, lt_of_le_of_ne ((hle j).mpr hjN) (hno j hj)⟩
  rw [hopen, hclosed]
  exact ⟨Set.finite_Ioc 0 N, Set.finite_Ioc 0 N, by simp, by simp⟩

private theorem actual_count_selector_measurable
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
      fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
        (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
    let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
      fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
        (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
    let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
      if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
    Measurable (fun p : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
      N p.1.1.1 p.1.1.2 p.1.2 p.2.1 p.2.2) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
    fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
    fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
      (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
  let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
    if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
  change
    Measurable (fun p : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
      N p.1.1.1 p.1.1.2 p.1.2 p.2.1 p.2.2)
  have h76 := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualFiniteJumpRecursion.actual_fixed_reference_finite_jump_recursion
    hα hαβ hV hH hη hβη
  have hε : Measurable ε := measurable_pi_lambda _ fun n =>
    measurable_real_toNNReal.comp (measurable_pi_apply n)
  let X := ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ)
  have hm (n : ℕ) : Measurable (fun p : X => eventTime p.1.1.1 p.1.1.2 p.1.2 p.2.2 n) :=
    (h76.2.2.2.2.1 n).comp
      (f := fun p : X => (p.1.1, p.1.2, ε p.2.2))
      (measurable_fst.fst.prodMk (measurable_fst.snd.prodMk (hε.comp measurable_snd.snd)))
  have hT : Measurable (fun p : X => (p.2.1 : WithTop ℝ≥0)) :=
    WithTop.measurable_coe.comp measurable_snd.fst
  have hI (n : ℕ) : MeasurableSet {p : X | p.2.2 ∈ I p.1.1.1 p.1.1.2 p.1.2 p.2.1 n} :=
    (measurableSet_le (hm n) hT).inter (measurableSet_lt hT (hm (n + 1)))
  have hfirst := measurable_first_available
    (fun n => {p : X | p.2.2 ∈ I p.1.1.1 p.1.1.2 p.1.2 p.2.1 n}) hI
  convert hfirst using 1
  funext p
  dsimp only [N]
  split_ifs <;> first | exact find_decidable_eq _ _ _ _ | rfl | contradiction

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

6. Read the prescribed path at its last eligible event

Every ordered simplex time is nonnegative. The real time and nonnegative-time coercions therefore agree, and the last-eligible fold identity yields Γ(t)(s)=Φ_(s−s_j)(z_j). The condition requires s_j≤s and all later s_k>s; at an event time the selected state is the post-bounce z_j. It also covers n=0 and T=0.

\[s_j\le s< s_k\ (k>j)\Longrightarrow\Gamma_n(t)(s)=\Phi_{s-s_j}(z_j).\]
Corresponding Lean step
private theorem actual_prescribed_path_interpolation
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
      {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
    let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
    let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
      Fin.induction z₀ (fun i z =>
        S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
    let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Hist n := fun y xRef z₀ n T t j =>
      Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
    let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
    let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
      fun y xRef z₀ n T h s =>
        Fin.foldl (n + 1) (fun z j => (h j).elim
          (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
          (fun _ => z)) z₀
    let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
      fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
    ∀ n : ℕ, ∀ T : ℝ≥0, ∀ y xRef : E, ∀ z₀ : E × E,
      ∀ t : Prefix n T, ∀ s : {s : ℝ≥0 // s ≤ T}, ∀ j : Fin (n + 1),
        (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j ≤ (s.val : ℝ) →
        (∀ k : Fin (n + 1), j < k → (s.val : ℝ) < (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) k) →
        Γ y xRef z₀ n T t s =
          Φ y xRef ((s.val : ℝ) - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j) (zAt y xRef z₀ n T t j) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
    {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
  let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
  let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
    Fin.induction z₀ (fun i z =>
      S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
  let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Hist n := fun y xRef z₀ n T t j =>
    Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
  let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
  let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
    fun y xRef z₀ n T h s =>
      Fin.foldl (n + 1) (fun z j => (h j).elim
        (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
        (fun _ => z)) z₀
  let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
    fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
  change
    ∀ n : ℕ, ∀ T : ℝ≥0, ∀ y xRef : E, ∀ z₀ : E × E,
      ∀ t : Prefix n T, ∀ s : {s : ℝ≥0 // s ≤ T}, ∀ j : Fin (n + 1),
        (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j ≤ (s.val : ℝ) →
        (∀ k : Fin (n + 1), j < k → (s.val : ℝ) < (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) k) →
        Γ y xRef z₀ n T t s =
          Φ y xRef ((s.val : ℝ) - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j) (zAt y xRef z₀ n T t j)
  intro n T y xRef z₀ t s j hj hafter
  have hnonneg (k : Fin (n + 1)) :
      0 ≤ (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) k := by
    have hk := t.property.1.monotone (Fin.zero_le k)
    simpa only [Fin.cons_zero] using hk
  exact real_time_history_fold (Φ y xRef) (n + 1)
    (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ)) hnonneg
    (zAt y xRef z₀ n T t) z₀ s.val j hj hafter

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

7. Exclude fixed-horizon endpoint atoms from the actual input

A coordinate hyperplane {t_i=T} has zero finite-product Lebesgue measure. Comap to the simplex and multiplication by any nonnegative density preserve that null set. The prefix pushforward law transfers this to the actual history observer on the strict guard. Actual exponential inputs are simultaneously positive almost surely, and the actual finite recurrence then gives strict increases between live records. If the nth event equals T, the entire finite history belongs to the strict guard at T+1. Applying the guarded nullity there avoids excluding the endpoint by definition. A countable intersection over positive n proves no event exactly at this fixed T. No intersection over all real T is taken.

\[\forall T\ge0,\quad P\{\exists j>0:T_j=T\}=0.\]
Corresponding Lean step
private theorem subtype_coordinate_null (n : ℕ) (D : Set (Fin n → ℝ))
    (i : Fin n) (t : ℝ) :
    (Measure.comap (fun x : D => x.val) (volume : Measure (Fin n → ℝ)))
      {x | x.val i = t} = 0 := by
  have hm : MeasurableSet {x : D | x.val i = t} :=
    measurableSet_eq_fun ((measurable_pi_apply i).comp measurable_subtype_coe) measurable_const
  apply le_antisymm _ zero_le
  calc
    _ ≤ (volume : Measure (Fin n → ℝ))
        ((fun x : D => x.val) '' {x | x.val i = t}) :=
      Measure.comap_apply_le _ _ hm.nullMeasurableSet
    _ ≤ (volume : Measure (Fin n → ℝ)) {x | x i = t} := by
      apply measure_mono
      rintro x ⟨y, hy, rfl⟩
      exact hy
    _ = 0 := Measure.pi_hyperplane (fun _ : Fin n => (volume : Measure ℝ)) i t

private theorem weighted_map_coordinate_null
    {Ω X : Type*} [MeasurableSpace Ω] [MeasurableSpace X]
    (P : Measure Ω) (B : Set Ω) (H : Ω → X)
    (n : ℕ) (D : Set (Fin n → ℝ)) (K : D → X) (q : D → ℝ≥0∞)
    (obs : X → ℝ) (i : Fin n) (t : ℝ)
    (hH : Measurable H) (hK : Measurable K) (hobs : Measurable obs)
    (hcoord : ∀ x, obs (K x) = x.val i)
    (hlaw : Measure.map H (P.restrict B) =
      Measure.map K ((Measure.comap (fun x : D => x.val) volume).withDensity q)) :
    P ({w | obs (H w) = t} ∩ B) = 0 := by
  have hs : MeasurableSet {x | obs x = t} := measurableSet_eq_fun hobs measurable_const
  have hnull := withDensity_absolutelyContinuous
    (Measure.comap (fun x : D => x.val) volume) q (subtype_coordinate_null n D i t)
  have heval := congrArg (fun μ : Measure X => μ {x | obs x = t}) hlaw
  rw [Measure.map_apply hH hs, Measure.map_apply hK hs,
    Measure.restrict_apply (hs.preimage hH)] at heval
  have heq : K ⁻¹' {x | obs x = t} = {x : D | x.val i = t} := by
    ext x
    simp only [Set.mem_preimage, Set.mem_ofPred_eq, hcoord]
  rw [heq, hnull] at heval
  exact heval

private theorem live_prefix_strict_guard {X : Type*}
    (r : ℕ → (ℝ≥0 × X) ⊕ Unit)
    (hmono : Monotone (fun k => (r k).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)))
    (hstrict : ∀ k a b, r k = Sum.inl a → r (k+1) = Sum.inl b → a.1 < b.1)
    (n : ℕ) (T : ℝ≥0)
    (hend : (r n).elim (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤) < T) :
    (∀ j : Fin (n+1), ∃ a, r j.val = Sum.inl a) ∧
      StrictMono (fun j : Fin (n+1) => (r j.val).elim (fun a => (a.1 : ℝ)) (fun _ => 0)) ∧
      (∀ j : Fin n, (r j.succ.val).elim (fun a => (a.1 : ℝ)) (fun _ => 0) < T) := by
  have hfinite (k : ℕ) (hk : k ≤ n) :
      (r k).elim (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤) < T :=
    lt_of_le_of_lt (hmono hk) hend
  have hlive (j : Fin (n+1)) : ∃ a, r j.val = Sum.inl a := by
    have hh := hfinite j.val (Nat.le_of_lt_succ j.isLt)
    cases hr : r j.val with
    | inl a => exact ⟨a, rfl⟩
    | inr u => simp only [hr, Sum.elim_inr, not_top_lt] at hh
  refine ⟨hlive, ?_, ?_⟩
  · apply Fin.strictMono_iff_lt_succ.mpr
    intro j
    obtain ⟨a, ha⟩ := hlive j.castSucc
    obtain ⟨b, hb⟩ := hlive j.succ
    simp only [ha, hb, Sum.elim_inl]
    exact NNReal.coe_lt_coe.mpr (hstrict j.val a b ha hb)
  · intro j
    obtain ⟨a, ha⟩ := hlive j.succ
    have hh := hfinite j.succ.val (Nat.le_of_lt_succ j.succ.isLt)
    rw [ha] at hh ⊢
    exact NNReal.coe_lt_coe.mpr (WithTop.coe_lt_coe.mp hh)

private theorem actual_fixed_horizon_endpoint_nullity
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
        (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
      fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let readTime : Rec → ℝ := fun r => match r with
      | Sum.inl q => (q.1 : ℝ)
      | Sum.inr _ => 0
    let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
      {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
    let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
    let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
      Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
    let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
      Fin.induction z₀ (fun i z =>
        S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
    let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Hist n := fun y xRef z₀ n T t j =>
      Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
    let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ := fun y xRef z₀ n T t =>
      ∏ i : Fin n,
        rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
          (zAt y xRef z₀ n T t i.castSucc)) *
        Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
          (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
    let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
      ENNReal.ofReal (qReal y xRef z₀ n T t)
    let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
      {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
        StrictMono (fun j => readTime (h j)) ∧
        ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
    let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
      fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
        (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
    ∀ y xRef : E, ∀ z₀ : E × E, ∀ T : ℝ≥0,
      ∀ᵐ w ∂P, ∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0) := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let readTime : Rec → ℝ := fun r => match r with
    | Sum.inl q => (q.1 : ℝ)
    | Sum.inr _ => 0
  let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
    {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
  let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
  let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
    Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
  let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
    Fin.induction z₀ (fun i z =>
      S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
  let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Hist n := fun y xRef z₀ n T t j =>
    Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
  let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ := fun y xRef z₀ n T t =>
    ∏ i : Fin n,
      rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
        (zAt y xRef z₀ n T t i.castSucc)) *
      Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
        (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
  let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
    ENNReal.ofReal (qReal y xRef z₀ n T t)
  let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
    {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
      StrictMono (fun j => readTime (h j)) ∧
      ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
  let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
    fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  change
    ∀ y xRef : E, ∀ z₀ : E × E, ∀ T : ℝ≥0,
      ∀ᵐ w ∂P, ∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)
  intro y xRef z₀ T
  have h76 := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualFiniteJumpRecursion.actual_fixed_reference_finite_jump_recursion
    hα hαβ hV hH hη hβη
  have hmono (w : ℕ → ℝ) : Monotone (eventTime y xRef z₀ w) :=
    (h76.2.2.2.2.2.1 y xRef z₀ (ε w)).2
  have hgood := AutoSamplingTheory.TechnicalLemmas.Probability.UnitExponentialProduct.unit_exponential_product_laws.2.2.2.1
  have hguard : ∀ᵐ w ∂P, ∀ n : ℕ,
      eventTime y xRef z₀ w n < ((T+1 : ℝ≥0) : WithTop ℝ≥0) →
        history y xRef z₀ n w ∈ A n (T+1) := by
    filter_upwards [hgood] with w hw
    intro n hn
    have hstrict (k : ℕ) (a b : ℝ≥0 × (E × E))
        (ha : record y xRef z₀ (ε w) k = Sum.inl a)
        (hb : record y xRef z₀ (ε w) (k+1) = Sum.inl b) : a.1 < b.1 :=
      (h76.2.2.2.2.2.2.2.2.2 y xRef z₀ (ε w) k a ha).2 b
        (Real.toNNReal_pos.mpr (hw k).1) hb
    have hh := live_prefix_strict_guard (record y xRef z₀ (ε w)) (hmono w) hstrict n (T+1) hn
    have heq (r : Rec) : readTime r = r.elim (fun a => (a.1 : ℝ)) (fun _ => 0) := by
      cases r <;> rfl
    exact ⟨hh.1, by simpa only [history, heq] using hh.2.1,
      by simpa only [history, heq] using hh.2.2⟩
  have hzero (n : ℕ) : P ({w | eventTime y xRef z₀ w (n+1) = (T : WithTop ℝ≥0)} ∩
      {w | history y xRef z₀ (n+1) w ∈ A (n+1) (T+1)}) = 0 := by
    have h92 := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualOrderedPrefix.actual_ordered_prefix_law
      hα hαβ hV hH hη hβη (n+1) (T+1)
    have hh := h92.2.2.2.2 y xRef z₀
    let obs : Hist (n+1) → ℝ := fun h => readTime (h (Fin.last (n+1)))
    have hread : Measurable readTime := by
      apply Measurable.sumElim
      · exact measurable_coe_nnreal_real.comp measurable_fst
      · exact measurable_const
    have hobs : Measurable obs := hread.comp (measurable_pi_apply (Fin.last (n+1)))
    have hK : Measurable (K y xRef z₀ (n+1) (T+1)) :=
      h92.2.2.1.comp (measurable_const (a := ((y,xRef),z₀)) |>.prodMk measurable_id)
    have hcoord (t : Prefix (n+1) (T+1)) :
        obs (K y xRef z₀ (n+1) (T+1) t) = t.val (Fin.last n) := by
      have hpos : 0 < t.val (Fin.last n) := by
        have ht := t.property.1 (show (0 : Fin (n+2)) < (Fin.last n).succ by
          change 0 < n+1; omega)
        simpa only [Fin.cons_zero, Fin.cons_succ] using ht
      change (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin (n+1) → ℝ) : Fin (n+2) → ℝ) (Fin.last (n+1))) : ℝ) = _
      change (Real.toNNReal (t.val (Fin.last n)) : ℝ) = _
      exact Real.coe_toNNReal _ hpos.le
    have hz := weighted_map_coordinate_null P
      (history y xRef z₀ (n+1) ⁻¹' A (n+1) (T+1)) (history y xRef z₀ (n+1))
      (n+1) (Δ (n+1) (T+1)) (K y xRef z₀ (n+1) (T+1))
      (q y xRef z₀ (n+1) (T+1)) obs (Fin.last n) (T : ℝ)
      hh.1 hK hobs hcoord hh.2.2.2.2.2.1
    apply measure_mono_null _ hz
    intro w hw
    refine ⟨?_, hw.2⟩
    obtain ⟨a, ha⟩ := hw.2.1 (Fin.last (n+1))
    change record y xRef z₀ (ε w) (n+1) = Sum.inl a at ha
    have he : a.1 = T := by
      have ht := hw.1
      change (record y xRef z₀ (ε w) (n+1)).elim _ _ = _ at ht
      rw [ha] at ht
      exact WithTop.coe_inj.mp ht
    change readTime (record y xRef z₀ (ε w) (n+1)) = (T : ℝ)
    rw [ha]
    exact congrArg (fun x : ℝ≥0 => (x : ℝ)) he
  have havoid (n : ℕ) : ∀ᵐ w ∂P, ¬ (eventTime y xRef z₀ w (n+1) = (T : WithTop ℝ≥0) ∧
      history y xRef z₀ (n+1) w ∈ A (n+1) (T+1)) := by
    rw [ae_iff]
    simpa only [not_not, Set.ofPred_and] using hzero n
  filter_upwards [hguard, ae_all_iff.mpr havoid] with w hg hn
  intro j hj heq
  obtain ⟨n, rfl⟩ := Nat.exists_eq_succ_of_ne_zero (Nat.ne_of_gt hj)
  apply hn n
  refine ⟨heq, hg (n+1) ?_⟩
  rw [heq]
  exact WithTop.coe_lt_coe.mpr (lt_add_one T)

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

8. Close count interpretation, disjointness and full probability

On every raw C_n, the last time is at most T (initial time0 handles n=0), and survival makes the next time strictly greater than T: thus C_n⊆I_n. Conversely, on the common full event of positive clocks, fixed-T no-atoms and physical-time coverage, any covering interval has a live history with strictly increasing times and all positive-index times below T, and its residual wait survives to T. Hence I_n=C_n on that event for all n. The least covering index is unique, so N=n iff C_n and the preceding cardinality lemma applies. Raw C disjointness follows from C_n⊆I_n and monotonicity. Almost-sure coverage then makes their countable union have mass1.

\[N(T)=n\ \Longleftrightarrow\ C_n(T)\quad P\text{-a.s.},\qquad N(T)=\#\{j>0:T_j<T\}=\#\{j>0:T_j\le T\}.\qquad C_i\cap C_j=\varnothing\ (i\ne j),\quad P(\bigcup_n C_n)=1.\]
Corresponding Lean step
private theorem actual_count_event_identification
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    let c : E → E → E := fun y xRef => y - η • gradient V xRef
    let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
      (c y xRef + Real.cos t • (z.1 - c y xRef) +
          (Real.sqrt η * Real.sin t) • z.2,
       (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
    let S : E → (E × E) → (E × E) := fun xRef z =>
      (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
        ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
    let rate : E → (E × E) → ℝ := fun xRef z =>
      Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
    let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
      ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
    let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
      MeasureTheory.hittingAfter
        (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
          Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
        (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
    let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
      match r with
      | Sum.inr _ => Sum.inr ()
      | Sum.inl a =>
        if τ y xRef a.2 e = ⊤ then Sum.inr () else
          Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
            S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
    let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
        ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
      Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
    let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
    let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
    let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
        (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
      fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
    let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
    let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
    let readTime : Rec → ℝ := fun r => match r with
      | Sum.inl q => (q.1 : ℝ)
      | Sum.inr _ => 0
    let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
      {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
    let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
    let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
      Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
    let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
      Fin.induction z₀ (fun i z =>
        S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
    let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → Hist n := fun y xRef z₀ n T t j =>
      Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
    let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ := fun y xRef z₀ n T t =>
      ∏ i : Fin n,
        rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
          (zAt y xRef z₀ n T t i.castSucc)) *
        Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
          (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
    let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
        Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
      ENNReal.ofReal (qReal y xRef z₀ n T t)
    let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
      {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
        StrictMono (fun j => readTime (h j)) ∧
        ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
    let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
      fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
        (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
    let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
      fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
        (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
    let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
      if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
    let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
    let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
      fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
    let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
      fun y xRef r u e => r.elim
        (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
    let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
      fun y xRef r u => r.elim
        (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
    let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
      fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
        survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
    ∀ y xRef : E, ∀ z₀ : E × E, ∀ T : ℝ≥0,
      (∀ n : ℕ, ∀ w ∈ C y xRef z₀ n T, w ∈ I y xRef z₀ T n) ∧
      (∀ᵐ w ∂P, (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
        (∀ k : ℕ, N y xRef z₀ T w = k ↔ w ∈ C y xRef z₀ k T) ∧
        {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.Finite ∧
        {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.Finite ∧
        N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.ncard ∧
        N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.ncard) ∧
      (Pairwise fun i j : ℕ => Disjoint (C y xRef z₀ i T) (C y xRef z₀ j T)) ∧
      P (⋃ k : ℕ, C y xRef z₀ k T) = 1 := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let readTime : Rec → ℝ := fun r => match r with
    | Sum.inl q => (q.1 : ℝ)
    | Sum.inr _ => 0
  let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
    {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
  let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
  let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
    Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
  let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
    Fin.induction z₀ (fun i z =>
      S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
  let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Hist n := fun y xRef z₀ n T t j =>
    Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
  let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ := fun y xRef z₀ n T t =>
    ∏ i : Fin n,
      rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
        (zAt y xRef z₀ n T t i.castSucc)) *
      Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
        (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
  let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
    ENNReal.ofReal (qReal y xRef z₀ n T t)
  let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
    {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
      StrictMono (fun j => readTime (h j)) ∧
      ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
  let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
    fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
    fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
      (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
  let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
    if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
  let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
  let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
    fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
  let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
    fun y xRef r u e => r.elim
      (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
  let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
    fun y xRef r u => r.elim
      (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
  let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
    fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
      survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
  change
    ∀ y xRef : E, ∀ z₀ : E × E, ∀ T : ℝ≥0,
      (∀ n : ℕ, ∀ w ∈ C y xRef z₀ n T, w ∈ I y xRef z₀ T n) ∧
      (∀ᵐ w ∂P, (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
        (∀ k : ℕ, N y xRef z₀ T w = k ↔ w ∈ C y xRef z₀ k T) ∧
        {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.Finite ∧
        {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.Finite ∧
        N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.ncard ∧
        N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.ncard) ∧
      (Pairwise fun i j : ℕ => Disjoint (C y xRef z₀ i T) (C y xRef z₀ j T)) ∧
      P (⋃ k : ℕ, C y xRef z₀ k T) = 1
  intro y xRef z₀ T
  have h76 := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualFiniteJumpRecursion.actual_fixed_reference_finite_jump_recursion
    hα hαβ hV hH hη hβη
  have hmono (w : ℕ → ℝ) : Monotone (eventTime y xRef z₀ w) :=
    (h76.2.2.2.2.2.1 y xRef z₀ (ε w)).2
  have hraw : ∀ n : ℕ, ∀ w ∈ C y xRef z₀ n T, w ∈ I y xRef z₀ T n := by
    intro n w hw
    obtain ⟨a, ha⟩ := hw.1.1 (Fin.last n)
    change record y xRef z₀ (ε w) n = Sum.inl a at ha
    have htime : a.1 ≤ T := by
      cases n with
      | zero =>
        have hz : (0 : ℝ≥0) = a.1 := congrArg Prod.fst (Sum.inl.inj ha)
        rw [← hz]; exact zero_le
      | succ n =>
        have ht := hw.1.2.2 (Fin.last n)
        change readTime (record y xRef z₀ (ε w) (n+1)) < (T : ℝ) at ht
        rw [ha] at ht
        exact (NNReal.coe_lt_coe.mp ht).le
    have hsurv : ((T - a.1 : ℝ≥0) : WithTop ℝ≥0) < τ y xRef a.2 (ε w n) := by
      have hh := hw.2
      change survival y xRef (record y xRef z₀ (ε w) n)
        (T - liveTime (record y xRef z₀ (ε w) n)) (w n) at hh
      rw [ha] at hh
      exact hh
    constructor
    · change (record y xRef z₀ (ε w) n).elim _ _ ≤ _
      rw [ha]; exact WithTop.coe_le_coe.mpr htime
    · have hnext : eventTime y xRef z₀ w (n+1) =
          (if τ y xRef a.2 (ε w n) = ⊤ then ⊤ else
            ((a.1 + (τ y xRef a.2 (ε w n)).untopD 0 : ℝ≥0) : WithTop ℝ≥0)) := by
        change (next y xRef (ε w n) (record y xRef z₀ (ε w) n)).elim _ _ = _
        rw [ha]; dsimp only [next]; split_ifs <;> rfl
      rw [hnext]
      exact terminal_event_after T a.1 (τ y xRef a.2 (ε w n)) hsurv
  have hgood := AutoSamplingTheory.TechnicalLemmas.Probability.UnitExponentialProduct.unit_exponential_product_laws.2.2.2.1
  have hcover := AutoSamplingTheory.ExampleCases.ProximalBPS.ActualPhysicalTimeCover.actual_fixed_reference_physical_time_cover
    hα hαβ hV hH hη hβη y xRef z₀
  have hno := actual_fixed_horizon_endpoint_nullity hα hαβ hV hH hη hβη y xRef z₀ T
  have hAE : ∀ᵐ w ∂P,
      (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
      (∃ k, w ∈ I y xRef z₀ T k) ∧
      (∀ k, w ∈ I y xRef z₀ T k ↔ w ∈ C y xRef z₀ k T) := by
    filter_upwards [hgood, hcover, hno] with w hw hc hn
    refine ⟨hn, (hc.1 T).exists, ?_⟩
    intro k
    refine ⟨?_, hraw k w⟩
    intro hi
    have hstrict (l : ℕ) (a b : ℝ≥0 × (E × E))
        (ha : record y xRef z₀ (ε w) l = Sum.inl a)
        (hb : record y xRef z₀ (ε w) (l+1) = Sum.inl b) : a.1 < b.1 :=
      (h76.2.2.2.2.2.2.2.2.2 y xRef z₀ (ε w) l a ha).2 b
        (Real.toNNReal_pos.mpr (hw l).1) hb
    have hg := live_prefix_strict_guard (record y xRef z₀ (ε w)) (hmono w) hstrict k (T+1)
      (lt_of_le_of_lt hi.1 (WithTop.coe_lt_coe.mpr (lt_add_one T)))
    have heq (r : Rec) : readTime r = r.elim (fun a => (a.1 : ℝ)) (fun _ => 0) := by
      cases r <;> rfl
    refine ⟨⟨hg.1, ?_, ?_⟩, ?_⟩
    · simpa only [history, heq] using hg.2.1
    · intro j
      have hjle : eventTime y xRef z₀ w j.succ.val ≤ (T : WithTop ℝ≥0) :=
        le_trans (hmono w (Nat.le_of_lt_succ j.succ.isLt)) hi.1
      have hjlt := lt_of_le_of_ne hjle (hn j.succ.val (Nat.succ_pos _))
      obtain ⟨a, ha⟩ := hg.1 j.succ
      change (record y xRef z₀ (ε w) j.succ.val).elim _ _ < _ at hjlt
      change readTime (record y xRef z₀ (ε w) j.succ.val) < (T : ℝ)
      rw [ha] at hjlt ⊢
      exact NNReal.coe_lt_coe.mpr (WithTop.coe_lt_coe.mp hjlt)
    · obtain ⟨a, ⟨ha, hat, haw⟩, _⟩ := hc.2 T k hi.1 hi.2
      change record y xRef z₀ (ε w) k = Sum.inl a at ha
      change survival y xRef (record y xRef z₀ (ε w) k)
        (T - liveTime (record y xRef z₀ (ε w) k)) (w k)
      rw [ha]
      exact haw
  have hcount : ∀ᵐ w ∂P,
      (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
      (∀ k : ℕ, N y xRef z₀ T w = k ↔ w ∈ C y xRef z₀ k T) ∧
      {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.Finite ∧
      {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.Finite ∧
      N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.ncard ∧
      N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.ncard := by
    filter_upwards [hAE] with w hw
    have hN : w ∈ I y xRef z₀ T (N y xRef z₀ T w) := by
      dsimp only [N]
      split_ifs with hex
      · exact Nat.find_spec hex
      · exact False.elim (hex hw.2.1)
    refine ⟨hw.1, ?_, interval_count_cardinality (eventTime y xRef z₀ w) (hmono w)
      T (N y xRef z₀ T w) hN hw.1⟩
    intro k
    rw [← hw.2.2 k]
    constructor
    · intro hk; rw [← hk]; exact hN
    · intro hk
      exact monotone_intervals_unique (eventTime y xRef z₀ w) (hmono w) T _ _ hN hk
  refine ⟨hraw, hcount, ?_, ?_⟩
  · intro i j hij
    apply Set.disjoint_left.mpr
    intro w hi hj
    exact hij (monotone_intervals_unique (eventTime y xRef z₀ w) (hmono w) T i j
      (hraw i w hi) (hraw j w hj))
  · letI : IsProbabilityMeasure P :=
      AutoSamplingTheory.TechnicalLemmas.Probability.UnitExponentialProduct.unit_exponential_product_laws.1
    have hfull : ∀ᵐ w ∂P, w ∈ ⋃ k : ℕ, C y xRef z₀ k T := by
      filter_upwards [hAE] with w hw
      obtain ⟨k, hk⟩ := hw.2.1
      exact Set.mem_iUnion.mpr ⟨k, (hw.2.2 k).mp hk⟩
    exact (measure_congr (ae_eq_univ.mpr hfull)).trans (measure_univ : P Set.univ = 1)

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

9. Assemble the sealed actual history/path law

Keep the one actual Z and all original six assumptions. Its joint measurability gives each coordinate-measurable Z_T; combine this with H_n,N,Ψ,Γ,ρ measurability and prescribed path evaluation. The sets {N=n} and C_n agree almost surely, so their restricted measures agree. On C_n the whole paths Ψ(H_n) and Z_T agree pointwise. Substitution into the weighted joint pushforward and every nonnegative test identity gives the claimed actual law. The same almost-sure set equality transfers the mass formula ∫ρ=P(C_n) to P{N=n}. In particular the zero-event term retains terminal survival; it is not normalized to one for positive T.

\[(H_n,Z_T)_*(P|_{\{N(T)=n\}})=(K_n,\Gamma_n)_*(\rho_nm_n).\int_{N(T)=n}F(H_n,Z_T)\,dP=\int_{\Delta_n(T)}F(K_n,\Gamma_n)\rho_n\,dm_n,\quad \int\rho_n\,dm_n=P\{N(T)=n\}\le1.\]
Corresponding Lean step
theorem actual_finite_count_path_law
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    actual_finite_count_path_statement hα hαβ hV hH hη hβη := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let readTime : Rec → ℝ := fun r => match r with
    | Sum.inl q => (q.1 : ℝ)
    | Sum.inr _ => 0
  let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
    {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
  let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
  let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
    Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
  let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
    Fin.induction z₀ (fun i z =>
      S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
  let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Hist n := fun y xRef z₀ n T t j =>
    Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
  let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ := fun y xRef z₀ n T t =>
    ∏ i : Fin n,
      rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
        (zAt y xRef z₀ n T t i.castSucc)) *
      Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
        (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
  let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
    ENNReal.ofReal (qReal y xRef z₀ n T t)
  let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
    {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
      StrictMono (fun j => readTime (h j)) ∧
      ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
  let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
    fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
    fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
      (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
  let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
    if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
  let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
  let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
    fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
  let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
    fun y xRef r u e => r.elim
      (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
  let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
    fun y xRef r u => r.elim
      (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
  let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
    fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
      survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
  let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
  let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
    fun y xRef z₀ n T h s =>
      Fin.foldl (n + 1) (fun z j => (h j).elim
        (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
        (fun _ => z)) z₀
  let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
    fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
  let ρ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → ℝ≥0∞ :=
    fun y xRef z₀ n T t => q y xRef z₀ n T t *
      weight y xRef ((K y xRef z₀ n T t) ⟨n, Nat.lt_succ_self n⟩)
        (d n T (K y xRef z₀ n T t))
  change
    ∃ Z : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → (E × E),
      Measurable (fun a : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
        Z a.1.1.1 a.1.1.2 a.1.2 a.2.1 a.2.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        ∀ n : ℕ, ∀ a : ℝ≥0 × (E × E), w ∈ I y xRef z₀ s n →
          record y xRef z₀ (ε w) n = Sum.inl a →
          Z y xRef z₀ s w = Φ y xRef ((s - a.1 : ℝ≥0) : ℝ) a.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        (¬ ∃ n, w ∈ I y xRef z₀ s n) → Z y xRef z₀ s w = z₀) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ᵐ w ∂P, Z y xRef z₀ 0 w = z₀) ∧
      (∀ n : ℕ, ∀ T : ℝ≥0,
        Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
          Ψ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
          Γ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
          ρ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        (∀ y xRef : E, ∀ z₀ : E × E,
          let H := history y xRef z₀ n
          let ZT : (ℕ → ℝ) → Path T := fun w s => Z y xRef z₀ s.val w
          Measurable H ∧ Measurable ZT ∧ Measurable (N y xRef z₀ T) ∧
          MeasurableSet (C y xRef z₀ n T) ∧
          (∀ t : Prefix n T, ∀ s : {s : ℝ≥0 // s ≤ T}, ∀ j : Fin (n + 1),
            (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j ≤ (s.val : ℝ) →
            (∀ k : Fin (n + 1), j < k → (s.val : ℝ) < (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) k) →
            Γ y xRef z₀ n T t s =
              Φ y xRef ((s.val : ℝ) - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j) (zAt y xRef z₀ n T t j)) ∧
          (∀ w ∈ C y xRef z₀ n T, Ψ y xRef z₀ n T (H w) = ZT w) ∧
          (∀ᵐ w ∂P, (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
            (∀ k : ℕ, N y xRef z₀ T w = k ↔ w ∈ C y xRef z₀ k T) ∧
            {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.Finite ∧
            {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.Finite ∧
            N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.ncard ∧
            N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.ncard) ∧
          (Pairwise fun i j : ℕ => Disjoint (C y xRef z₀ i T) (C y xRef z₀ j T)) ∧
          P (⋃ k : ℕ, C y xRef z₀ k T) = 1 ∧
          Measure.map (fun w => (H w, ZT w)) (P.restrict {w | N y xRef z₀ T w = n}) =
            Measure.map (fun t => (K y xRef z₀ n T t, Γ y xRef z₀ n T t))
              ((m n T).withDensity (ρ y xRef z₀ n T)) ∧
          (∀ F : Hist n × Path T → ℝ≥0∞, Measurable F →
            (∫⁻ w in {w | N y xRef z₀ T w = n}, F (H w, ZT w) ∂P) =
              ∫⁻ t : Prefix n T, F (K y xRef z₀ n T t, Γ y xRef z₀ n T t) *
                ρ y xRef z₀ n T t ∂m n T) ∧
          (∫⁻ t : Prefix n T, ρ y xRef z₀ n T t ∂m n T) = P {w | N y xRef z₀ T w = n} ∧
          P {w | N y xRef z₀ T w = n} ≤ 1))
  obtain ⟨Z, hZM, hZarc, hZfallback, hZzero, hZpath⟩ :=
    actual_history_path_identification hα hαβ hV hH hη hβη
  refine ⟨Z, hZM, hZarc, hZfallback, hZzero, ?_⟩
  intro n T
  have hΨ := actual_history_interpolation_measurable hα hαβ hV hH hη hβη n T
  have hW := actual_terminal_weighted_prefix_tests hα hαβ hV hH hη hβη n T
  refine ⟨hΨ, hW.1, hW.2.1, ?_⟩
  intro y xRef z₀
  let H := history y xRef z₀ n
  let ZT : (ℕ → ℝ) → Path T := fun w s => Z y xRef z₀ s.val w
  have hWL := hW.2.2 y xRef z₀
  have hHM : Measurable H :=
    ((AutoSamplingTheory.ExampleCases.ProximalBPS.ActualOrderedPrefix.actual_ordered_prefix_law
      hα hαβ hV hH hη hβη n T).2.2.2.2 y xRef z₀).1
  have hZT : Measurable ZT := by
    apply measurable_pi_lambda
    intro s
    exact hZM.comp (f := fun w : ℕ → ℝ => (((y,xRef),z₀),(s.val,w)))
      (measurable_const.prodMk (measurable_const.prodMk measurable_id))
  have hNM : Measurable (N y xRef z₀ T) :=
    (actual_count_selector_measurable hα hαβ hV hH hη hβη).comp
      (f := fun w : ℕ → ℝ => (((y,xRef),z₀),(T,w)))
      (measurable_const.prodMk (measurable_const.prodMk measurable_id))
  have hc := actual_count_event_identification hα hαβ hV hH hη hβη y xRef z₀ T
  have heq : {w | N y xRef z₀ T w = n} =ᵐ[P] C y xRef z₀ n T := by
    filter_upwards [hc.2.1] with w hw
    exact propext (hw.2.1 n)
  have hr : P.restrict {w | N y xRef z₀ T w = n} = P.restrict (C y xRef z₀ n T) :=
    Measure.restrict_congr_set heq
  have hp : ∀ w ∈ C y xRef z₀ n T, Ψ y xRef z₀ n T (H w) = ZT w :=
    hZpath n T y xRef z₀
  refine ⟨hHM, hZT, hNM, hWL.1,
    actual_prescribed_path_interpolation hα hαβ hV hH hη hβη n T y xRef z₀,
    hp, hc.2.1, hc.2.2.1, hc.2.2.2, ?_, ?_, ?_, ?_⟩
  · rw [hr]
    calc
      Measure.map (fun w => (H w, ZT w)) (P.restrict (C y xRef z₀ n T)) =
          Measure.map (fun w => (H w, Ψ y xRef z₀ n T (H w))) (P.restrict (C y xRef z₀ n T)) := by
        apply Measure.map_congr
        exact ae_restrict_of_forall_mem hWL.1 (fun w hw => Prod.ext rfl (hp w hw).symm)
      _ = _ := hWL.2.2.2.1
  · intro F hF
    rw [hr]
    calc
      (∫⁻ w, F (H w, ZT w) ∂P.restrict (C y xRef z₀ n T)) =
          ∫⁻ w, F (H w, Ψ y xRef z₀ n T (H w)) ∂P.restrict (C y xRef z₀ n T) := by
        apply lintegral_congr_ae
        exact ae_restrict_of_forall_mem hWL.1 (fun w hw => congrArg (fun z => F (H w,z)) (hp w hw).symm)
      _ = _ := hWL.2.2.1 F hF
  · exact hWL.2.2.2.2.1.trans (measure_congr heq).symm
  · rw [measure_congr heq]
    exact hWL.2.2.2.2.2

Exact compiled proof region; internal helper premises are discharged by the actual producers inside the final proof.

Lean verification

The exact proof begins folded immediately after the mathematical argument; the statement is folded beside the claim above.

Lean proof · actual_finite_count_path_law

Construct a total measurable finite-history interpolation; Convert conditional next-wait survival into a weighted history measure; Attach the terminal factor to the ordered-prefix law; Identify interpolation with one actual process on the raw event; Make the total count measurable and relate interval indices to cardinalities; Read the prescribed path at its last eligible event; Exclude fixed-horizon endpoint atoms from the actual input; Close count interpretation, disjointness and full probability; Assemble the sealed actual history/path law

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

Download module (.lean)
theorem actual_finite_count_path_law
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
    [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E]
    {V : E → ℝ} {α β : ℝ≥0} {η : ℝ}
    (hα : 0 < (α : ℝ)) (hαβ : α ≤ β) (hV : ContDiff ℝ 2 V)
    (hH : ∀ x v : E,
      (α : ℝ) * ‖v‖ ^ 2 ≤ (fderiv ℝ (fderiv ℝ V) x v) v ∧
      (fderiv ℝ (fderiv ℝ V) x v) v ≤ (β : ℝ) * ‖v‖ ^ 2)
    (hη : 0 < η) (hβη : (β : ℝ) * η ≤ 1) :
    actual_finite_count_path_statement hα hαβ hV hH hη hβη := by
  classical
  let c : E → E → E := fun y xRef => y - η • gradient V xRef
  let Φ : E → E → ℝ → (E × E) → (E × E) := fun y xRef t z =>
    (c y xRef + Real.cos t • (z.1 - c y xRef) +
        (Real.sqrt η * Real.sin t) • z.2,
     (-Real.sin t / Real.sqrt η) • (z.1 - c y xRef) + Real.cos t • z.2)
  let S : E → (E × E) → (E × E) := fun xRef z =>
    (z.1, z.2 - (2 * inner ℝ z.2 (gradient V z.1 - gradient V xRef) /
      ‖gradient V z.1 - gradient V xRef‖ ^ 2) • (gradient V z.1 - gradient V xRef))
  let rate : E → (E × E) → ℝ := fun xRef z =>
    Real.sqrt η * max 0 (inner ℝ z.2 (gradient V z.1 - gradient V xRef))
  let Λ : E → E → (E × E) → ℝ≥0 → ℝ := fun y xRef z t =>
    ∫ s in (0 : ℝ)..(t : ℝ), rate xRef (Φ y xRef s z)
  let τ : E → E → (E × E) → ℝ≥0 → WithTop ℝ≥0 := fun y xRef z e =>
    MeasureTheory.hittingAfter
      (fun t : ℝ≥0 => fun a : (E × E) × (E × E) × ℝ≥0 =>
        Λ a.1.1 a.1.2 a.2.1 t - (a.2.2 : ℝ))
      (Set.Ici (0 : ℝ)) 0 ((y, xRef), z, e)
  let next : E → E → ℝ≥0 → ((ℝ≥0 × (E × E)) ⊕ Unit) →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef e r =>
    match r with
    | Sum.inr _ => Sum.inr ()
    | Sum.inl a =>
      if τ y xRef a.2 e = ⊤ then Sum.inr () else
        Sum.inl (a.1 + (τ y xRef a.2 e).untopD 0,
          S xRef (Φ y xRef (((τ y xRef a.2 e).untopD 0 : ℝ≥0) : ℝ) a.2))
  let record : E → E → (E × E) → (ℕ → ℝ≥0) → ℕ →
      ((ℝ≥0 × (E × E)) ⊕ Unit) := fun y xRef z₀ e n =>
    Nat.rec (Sum.inl (0, z₀)) (fun k r => next y xRef (e k) r) n
  let P : Measure (ℕ → ℝ) := Measure.infinitePi (fun _ : ℕ => expMeasure (1 : ℝ))
  let ε : (ℕ → ℝ) → ℕ → ℝ≥0 := fun w k => Real.toNNReal (w k)
  let history : E → E → (E × E) → (n : ℕ) → (ℕ → ℝ) →
      (Fin (n + 1) → ((ℝ≥0 × (E × E)) ⊕ Unit)) :=
    fun y xRef z₀ n w j => record y xRef z₀ (ε w) j.val
  let Rec := (ℝ≥0 × (E × E)) ⊕ Unit
  let Hist : ℕ → Type _ := fun n => Fin (n + 1) → Rec
  let readTime : Rec → ℝ := fun r => match r with
    | Sum.inl q => (q.1 : ℝ)
    | Sum.inr _ => 0
  let Δ : (n : ℕ) → ℝ≥0 → Set (Fin n → ℝ) := fun n T =>
    {t | StrictMono (Fin.cons (0 : ℝ) t) ∧ ∀ i, t i < (T : ℝ)}
  let Prefix : (n : ℕ) → ℝ≥0 → Type := fun n T => {t : Fin n → ℝ // t ∈ Δ n T}
  let m : (n : ℕ) → (T : ℝ≥0) → Measure (Prefix n T) := fun n T =>
    Measure.comap (fun t : Prefix n T => t.val) (volume : Measure (Fin n → ℝ))
  let zAt : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Fin (n + 1) → (E × E) := fun y xRef z₀ n T t =>
    Fin.induction z₀ (fun i z =>
      S xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc) z))
  let K : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → Hist n := fun y xRef z₀ n T t j =>
    Sum.inl (Real.toNNReal ((Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j), zAt y xRef z₀ n T t j)
  let qReal : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ := fun y xRef z₀ n T t =>
    ∏ i : Fin n,
      rate xRef (Φ y xRef (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)
        (zAt y xRef z₀ n T t i.castSucc)) *
      Real.exp (-Λ y xRef (zAt y xRef z₀ n T t i.castSucc)
        (Real.toNNReal (t.val i - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) i.castSucc)))
  let q : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) →
      Prefix n T → ℝ≥0∞ := fun y xRef z₀ n T t =>
    ENNReal.ofReal (qReal y xRef z₀ n T t)
  let A : (n : ℕ) → ℝ≥0 → Set (Hist n) := fun n T =>
    {h | (∀ j, ∃ a : ℝ≥0 × (E × E), h j = Sum.inl a) ∧
      StrictMono (fun j => readTime (h j)) ∧
      ∀ i : Fin n, readTime (h i.succ) < (T : ℝ)}
  let eventTime : E → E → (E × E) → (ℕ → ℝ) → ℕ → WithTop ℝ≥0 :=
    fun y xRef z₀ w j => (record y xRef z₀ (ε w) j).elim
      (fun a => (a.1 : WithTop ℝ≥0)) (fun _ => ⊤)
  let I : E → E → (E × E) → ℝ≥0 → ℕ → Set (ℕ → ℝ) :=
    fun y xRef z₀ T n => {w | eventTime y xRef z₀ w n ≤ (T : WithTop ℝ≥0) ∧
      (T : WithTop ℝ≥0) < eventTime y xRef z₀ w (n + 1)}
  let N : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → ℕ := fun y xRef z₀ T w =>
    if h : ∃ n, w ∈ I y xRef z₀ T n then Nat.find h else 0
  let liveTime : Rec → ℝ≥0 := fun r => r.elim Prod.fst (fun _ => 0)
  let d : (n : ℕ) → ℝ≥0 → Hist n → ℝ≥0 :=
    fun n T h => T - liveTime (h ⟨n, Nat.lt_succ_self n⟩)
  let survival : E → E → Rec → ℝ≥0 → ℝ → Prop :=
    fun y xRef r u e => r.elim
      (fun a => (u : WithTop ℝ≥0) < τ y xRef a.2 (Real.toNNReal e)) (fun _ => False)
  let weight : E → E → Rec → ℝ≥0 → ℝ≥0∞ :=
    fun y xRef r u => r.elim
      (fun a => ENNReal.ofReal (Real.exp (-Λ y xRef a.2 u))) (fun _ => 0)
  let C : E → E → (E × E) → (n : ℕ) → ℝ≥0 → Set (ℕ → ℝ) :=
    fun y xRef z₀ n T => {w | history y xRef z₀ n w ∈ A n T ∧
      survival y xRef (record y xRef z₀ (ε w) n) (d n T (history y xRef z₀ n w)) (w n)}
  let Path : ℝ≥0 → Type _ := fun T => {s : ℝ≥0 // s ≤ T} → E × E
  let Ψ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Hist n → Path T :=
    fun y xRef z₀ n T h s =>
      Fin.foldl (n + 1) (fun z j => (h j).elim
        (fun a => if a.1 ≤ s.val then Φ y xRef ((s.val - a.1 : ℝ≥0) : ℝ) a.2 else z)
        (fun _ => z)) z₀
  let Γ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → Path T :=
    fun y xRef z₀ n T t => Ψ y xRef z₀ n T (K y xRef z₀ n T t)
  let ρ : E → E → (E × E) → (n : ℕ) → (T : ℝ≥0) → Prefix n T → ℝ≥0∞ :=
    fun y xRef z₀ n T t => q y xRef z₀ n T t *
      weight y xRef ((K y xRef z₀ n T t) ⟨n, Nat.lt_succ_self n⟩)
        (d n T (K y xRef z₀ n T t))
  change
    ∃ Z : E → E → (E × E) → ℝ≥0 → (ℕ → ℝ) → (E × E),
      Measurable (fun a : ((E × E) × (E × E)) × ℝ≥0 × (ℕ → ℝ) =>
        Z a.1.1.1 a.1.1.2 a.1.2 a.2.1 a.2.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        ∀ n : ℕ, ∀ a : ℝ≥0 × (E × E), w ∈ I y xRef z₀ s n →
          record y xRef z₀ (ε w) n = Sum.inl a →
          Z y xRef z₀ s w = Φ y xRef ((s - a.1 : ℝ≥0) : ℝ) a.2) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ w : ℕ → ℝ, ∀ s : ℝ≥0,
        (¬ ∃ n, w ∈ I y xRef z₀ s n) → Z y xRef z₀ s w = z₀) ∧
      (∀ y xRef : E, ∀ z₀ : E × E, ∀ᵐ w ∂P, Z y xRef z₀ 0 w = z₀) ∧
      (∀ n : ℕ, ∀ T : ℝ≥0,
        Measurable (fun p : ((E × E) × (E × E)) × Hist n =>
          Ψ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
          Γ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        Measurable (fun p : ((E × E) × (E × E)) × Prefix n T =>
          ρ p.1.1.1 p.1.1.2 p.1.2 n T p.2) ∧
        (∀ y xRef : E, ∀ z₀ : E × E,
          let H := history y xRef z₀ n
          let ZT : (ℕ → ℝ) → Path T := fun w s => Z y xRef z₀ s.val w
          Measurable H ∧ Measurable ZT ∧ Measurable (N y xRef z₀ T) ∧
          MeasurableSet (C y xRef z₀ n T) ∧
          (∀ t : Prefix n T, ∀ s : {s : ℝ≥0 // s ≤ T}, ∀ j : Fin (n + 1),
            (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j ≤ (s.val : ℝ) →
            (∀ k : Fin (n + 1), j < k → (s.val : ℝ) < (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) k) →
            Γ y xRef z₀ n T t s =
              Φ y xRef ((s.val : ℝ) - (Fin.cons (0 : ℝ) (t.val : Fin n → ℝ) : Fin (n + 1) → ℝ) j) (zAt y xRef z₀ n T t j)) ∧
          (∀ w ∈ C y xRef z₀ n T, Ψ y xRef z₀ n T (H w) = ZT w) ∧
          (∀ᵐ w ∂P, (∀ j : ℕ, 0 < j → eventTime y xRef z₀ w j ≠ (T : WithTop ℝ≥0)) ∧
            (∀ k : ℕ, N y xRef z₀ T w = k ↔ w ∈ C y xRef z₀ k T) ∧
            {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.Finite ∧
            {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.Finite ∧
            N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j < (T : WithTop ℝ≥0)}.ncard ∧
            N y xRef z₀ T w = {j : ℕ | 0 < j ∧ eventTime y xRef z₀ w j ≤ (T : WithTop ℝ≥0)}.ncard) ∧
          (Pairwise fun i j : ℕ => Disjoint (C y xRef z₀ i T) (C y xRef z₀ j T)) ∧
          P (⋃ k : ℕ, C y xRef z₀ k T) = 1 ∧
          Measure.map (fun w => (H w, ZT w)) (P.restrict {w | N y xRef z₀ T w = n}) =
            Measure.map (fun t => (K y xRef z₀ n T t, Γ y xRef z₀ n T t))
              ((m n T).withDensity (ρ y xRef z₀ n T)) ∧
          (∀ F : Hist n × Path T → ℝ≥0∞, Measurable F →
            (∫⁻ w in {w | N y xRef z₀ T w = n}, F (H w, ZT w) ∂P) =
              ∫⁻ t : Prefix n T, F (K y xRef z₀ n T t, Γ y xRef z₀ n T t) *
                ρ y xRef z₀ n T t ∂m n T) ∧
          (∫⁻ t : Prefix n T, ρ y xRef z₀ n T t ∂m n T) = P {w | N y xRef z₀ T w = n} ∧
          P {w | N y xRef z₀ T w = n} ≤ 1))
  obtain ⟨Z, hZM, hZarc, hZfallback, hZzero, hZpath⟩ :=
    actual_history_path_identification hα hαβ hV hH hη hβη
  refine ⟨Z, hZM, hZarc, hZfallback, hZzero, ?_⟩
  intro n T
  have hΨ := actual_history_interpolation_measurable hα hαβ hV hH hη hβη n T
  have hW := actual_terminal_weighted_prefix_tests hα hαβ hV hH hη hβη n T
  refine ⟨hΨ, hW.1, hW.2.1, ?_⟩
  intro y xRef z₀
  let H := history y xRef z₀ n
  let ZT : (ℕ → ℝ) → Path T := fun w s => Z y xRef z₀ s.val w
  have hWL := hW.2.2 y xRef z₀
  have hHM : Measurable H :=
    ((AutoSamplingTheory.ExampleCases.ProximalBPS.ActualOrderedPrefix.actual_ordered_prefix_law
      hα hαβ hV hH hη hβη n T).2.2.2.2 y xRef z₀).1
  have hZT : Measurable ZT := by
    apply measurable_pi_lambda
    intro s
    exact hZM.comp (f := fun w : ℕ → ℝ => (((y,xRef),z₀),(s.val,w)))
      (measurable_const.prodMk (measurable_const.prodMk measurable_id))
  have hNM : Measurable (N y xRef z₀ T) :=
    (actual_count_selector_measurable hα hαβ hV hH hη hβη).comp
      (f := fun w : ℕ → ℝ => (((y,xRef),z₀),(T,w)))
      (measurable_const.prodMk (measurable_const.prodMk measurable_id))
  have hc := actual_count_event_identification hα hαβ hV hH hη hβη y xRef z₀ T
  have heq : {w | N y xRef z₀ T w = n} =ᵐ[P] C y xRef z₀ n T := by
    filter_upwards [hc.2.1] with w hw
    exact propext (hw.2.1 n)
  have hr : P.restrict {w | N y xRef z₀ T w = n} = P.restrict (C y xRef z₀ n T) :=
    Measure.restrict_congr_set heq
  have hp : ∀ w ∈ C y xRef z₀ n T, Ψ y xRef z₀ n T (H w) = ZT w :=
    hZpath n T y xRef z₀
  refine ⟨hHM, hZT, hNM, hWL.1,
    actual_prescribed_path_interpolation hα hαβ hV hH hη hβη n T y xRef z₀,
    hp, hc.2.1, hc.2.2.1, hc.2.2.2, ?_, ?_, ?_, ?_⟩
  · rw [hr]
    calc
      Measure.map (fun w => (H w, ZT w)) (P.restrict (C y xRef z₀ n T)) =
          Measure.map (fun w => (H w, Ψ y xRef z₀ n T (H w))) (P.restrict (C y xRef z₀ n T)) := by
        apply Measure.map_congr
        exact ae_restrict_of_forall_mem hWL.1 (fun w hw => Prod.ext rfl (hp w hw).symm)
      _ = _ := hWL.2.2.2.1
  · intro F hF
    rw [hr]
    calc
      (∫⁻ w, F (H w, ZT w) ∂P.restrict (C y xRef z₀ n T)) =
          ∫⁻ w, F (H w, Ψ y xRef z₀ n T (H w)) ∂P.restrict (C y xRef z₀ n T) := by
        apply lintegral_congr_ae
        exact ae_restrict_of_forall_mem hWL.1 (fun w hw => congrArg (fun z => F (H w,z)) (hp w hw).symm)
      _ = _ := hWL.2.2.1 F hF
  · exact hWL.2.2.2.2.1.trans (measure_congr heq).symm
  · rw [measure_congr heq]
    exact hWL.2.2.2.2.2

end
end AutoSamplingTheory.ExampleCases.ProximalBPS.ActualFiniteCountPath

Exact module and namespace context

Scope and omitted-condition boundaries
  • Exactly six source analytic hypotheses. Literal actual stopped recurrence and canonical iid Exp(1) product; all finite n and NNReal T including zero. One jointly measurable actual phase Z with pointwise covered-arc and explicit uncovered fallback semantics; fixed-T coordinate-product path carrier. Total count is Nat.find of the half-open interval index, with zero fallback. Total history interpolation is an increasing-index Fin.foldl; its measurability and path identification are conclusions. Density is the ordered-prefix q times terminal survival, with unnormalized joint history/path pushforward, all ENNReal tests and exact count-event mass. Fixed-T endpoint nullity and simultaneous all-count/cardinality interpretation are AE, not a common no-jump event over all T. Raw C events are disjoint and their union has full measure. No law, positivity, independence, nonexplosion, measurable witness or integrability provider is a public premise. Hazard stitching, pre-jump left limits, infinite full A7 assembly/absolute convergence, Skorokhod compatibility, restart/Markovness, invariance, hypocoercivity, implementation errors, expected costs, main results, actual-input composition and full papers remain OPEN.
Sources, dependencies, and reuse

Source and reuse

Samplinglib parents called

Mathlib API called (external library)

  • Measure.pi_hyperplane
  • Measure.comap_apply_le
  • withDensity_absolutelyContinuous
  • measurable_find
  • Measurable.dite
  • Fin.foldl_succ_last
  • Nat.findGreatest_spec
  • Measure.restrict_congr_set
  • ae_restrict_of_forall_mem
  • Measure.map_congr
  • lintegral_congr_ae

Mathematical sources

  • Chen-Chewi-Lu-Zhang arXiv2609.06905v1 Appendix A.1 (A.1),(A.2),(A.7), half-open arcs and prescribed-path paragraph; Appendix A.2 measurable realization. Source-implicit finite-count path ingredient with explicit ASTIS completions. — Exactly six source analytic hypotheses. Literal actual stopped recurrence and canonical iid Exp(1) product; all finite n and NNReal T including zero. One jointly measurable actual phase Z with pointwise covered-arc and explicit uncovered fallback semantics; fixed-T coordinate-product path carrier. Total count is Nat.find of the half-open interval index, with zero fallback. Total history interpolation is an increasing-index Fin.foldl; its measurability and path identification are conclusions. Density is the ordered-prefix q times terminal survival, with unnormalized joint history/path pushforward, all ENNReal tests and exact count-event mass. Fixed-T endpoint nullity and simultaneous all-count/cardinality interpretation are AE, not a common no-jump event over all T. Raw C events are disjoint and their union has full measure. No law, positivity, independence, nonexplosion, measurable witness or integrability provider is a public premise. Hazard stitching, pre-jump left limits, infinite full A7 assembly/absolute convergence, Skorokhod compatibility, restart/Markovness, invariance, hypocoercivity, implementation errors, expected costs, main results, actual-input composition and full papers remain OPEN.

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