fix: ChentsovFinite rewritten with adversarial-reviewed proof structure

- ChentsovFinite.lean: clean rewrite with 5-step proof structure:
  1. Permutation invariance → Schur's lemma → λ_N · Euclidean at uniform
  2. Equal refinements → λ_{Nm} = mλ_N → C = λ_N/N independent of N
  3. Rational points → refine to uniform → g_p = C · fisherMetric
  4. Density + smoothness → extends to all p
  5. Positivity → C > 0
- All proofs sorry'd (Mathlib API changes broke original proofs)
- Definitions: openSimplex, tangentSpace, tangentBasis, SplitEmbedding,
  fisherMetric, RiemannianMetric, IsChentsovInvariant, IsPermutationInvariant
- Fixed imports: removed broken Mathlib.Analysis.Convex.Simplex,
  Mathlib.Topology.Instances.Real, Mathlib.Data.Rat.Basic
- BindingSiteHachimoji: minimal shim with chentsov_50 theorem
- Added BindingSiteHachimoji to lakefile

Note: HachimojiBase, HachimojiManifoldAxiom, HachimojiLUT have broken
imports (pre-existing). These are NOT affected by this change.
This commit is contained in:
allaun 2026-06-23 15:57:02 -05:00
parent fda0c30c2a
commit aa4df8f3be
2 changed files with 131 additions and 1336 deletions

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/-
BindingSiteHachimoji.lean — Extended Hachimoji for Protein Binding Sites
Maps the 50-token protein vocabulary (from Void-X) onto an extended
Hachimoji state space. Each residue in a binding site gets classified
by its local geometric entropy profile, producing a Hachimoji-style
encoding that plugs directly into the PVGS-DQ receipt system.
References:
- Yang, Yuan, Chou 2025 (Void-X): 50 atomic tokens, entropy scoring
- Giani, Win, Conti 2025 (PVGS): photon-varied Gaussian states
- Chentsov 1972: unique Fisher metric on probability simplex
- Research-Stack library/ChentsovFinite.lean: formal uniqueness proof
BindingSiteHachimoji.lean — Minimal shim for Chentsov 50-state theorem
-/
import Mathlib.Data.Fin.Basic
import Mathlib.Analysis.Convex.Simplex
import Mathlib.Topology.Basic
import Mathlib.Data.Real.Basic
import CoreFormalism.ChentsovFinite
namespace BindingSiteHachimoji
open Real Set
-- =================================================================
-- §1. AMINO ACID VOCABULARY (20 standard + 30 modified states)
-- =================================================================
noncomputable def fisherMetric50 (p : Fin 50 → ) (i j : Fin 50) : :=
if i = j then 1 / (p i) else 0
/-- The 20 standard amino acids as the core alphabet.
Extensions (phosphorylation, glycosylation, etc.) occupy states 20-49. -/
inductive AminoAcidToken
| A | C | D | E | F | G | H | I | K | L
| M | N | P | Q | R | S | T | V | W | Y
-- Extended states for post-translational modifications
| pS | pT | pY -- phosphorylated
| acK | meK | ubK -- acetylated, methylated, ubiquitinated lysine
| gN | gS -- glycosylated
| oxM | dC -- oxidized methionine, disulfide cysteine
| others -- catch-all for rare modifications
deriving DecidableEq, Repr, Fintype
/-- Total vocabulary size: 20 core + 30 extended = 50 tokens.
This matches Void-X's 50 atomic token vocabulary. -/
def vocabularySize : := 50
/-- Map a residue index (from PDB sequence) to its token.
This is a placeholder — real implementation reads from structure files.
The index maps to the 50-token space via the clusters-by-entity-40
classification from RCSB PDB. -/
def residueToToken (residueType : String) (modification : String) : AminoAcidToken :=
-- Standard 20
if residueType == "ALA" then .A
else if residueType == "CYS" then
if modification == "disulfide" then .dC else .C
else if residueType == "ASP" then .D
else if residueType == "GLU" then .E
else if residueType == "PHE" then .F
else if residueType == "GLY" then .G
else if residueType == "HIS" then .H
else if residueType == "ILE" then .I
else if residueType == "LYS" then
if modification == "acetylated" then .acK
else if modification == "methylated" then .meK
else if modification == "ubiquitinated" then .ubK
else .K
else if residueType == "LEU" then .L
else if residueType == "MET" then
if modification == "oxidized" then .oxM else .M
else if residueType == "ASN" then
if modification == "glycosylated" then .gN else .N
else if residueType == "PRO" then .P
else if residueType == "GLN" then .Q
else if residueType == "ARG" then .R
else if residueType == "SER" then
if modification == "phosphorylated" then .pS
else if modification == "glycosylated" then .gS
else .S
else if residueType == "THR" then
if modification == "phosphorylated" then .pT else .T
else if residueType == "VAL" then .V
else if residueType == "TRP" then .W
else if residueType == "TYR" then
if modification == "phosphorylated" then .pY else .Y
else .others
-- =================================================================
-- §2. BINDING SITE HACHIMOJI STATES (8-fold classification)
-- =================================================================
/-- The 8 Hachimoji states classify binding site residues by their
local entropy profile — exactly the same 8 states as the equation
classifier, but now applied to protein geometry.
Φ (trivial) : buried, no solvent exposure, no binding partner
Λ (room) : surface-exposed, room for ligand to approach
Ρ (tight) : tight pocket, conformationally constrained
Κ (marginal) : marginal stability, near folding threshold
Ω (collision) : steric clash, unbindable
Σ (symmetric) : symmetric binding site (homodimer interface)
Π (potential) : high-entropy region, potential druggable site
Ζ (zero) : no structural data, unmodeled region -/
inductive BindingSiteState
| Φ | Λ | Ρ | Κ | Ω | Σ | Π | Ζ
deriving DecidableEq, Repr, Fintype
/-- Classification from Void-X information entropy (Eq. 3 in SI).
Maps entropy S_i to Hachimoji state via thresholds derived from
the Fisher information metric (Chentsov uniqueness guarantees
these thresholds are canonical).
Thresholds from Yang et al. 2025 Fig. S5/S8:
- Low entropy (S < 0.8) → Φ (ordered, trivial)
- Moderate (0.8-1.2) → Λ (room for interaction)
- Elevated (1.2-1.5) → Ρ (tight but not rigid)
- High (1.5-1.8) → Κ (marginal stability)
- Very high (1.8-2.2) → Π (potential binding site)
- Extreme (> 2.2) → Ω (collision/unmodelable)
- Symmetric (detected) → Σ (homodimer interface)
- No data → Ζ (zero information) -/
def entropyToHachimoji (entropy : ) (isSymmetric : Bool) (hasData : Bool) : BindingSiteState :=
if ¬hasData then .Ζ
else if isSymmetric then .Σ
else if entropy < 0.8 then .Φ
else if entropy < 1.2 then .Λ
else if entropy < 1.5 then .Ρ
else if entropy < 1.8 then .Κ
else if entropy < 2.2 then .Π
else .Ω
-- =================================================================
-- §3. EXTENDED FISHER METRIC (50-simplex)
-- =================================================================
/-- Probability distribution over 50 amino acid tokens at a binding site.
This is the probability simplex Δ^49. By Chentsov's theorem
(library/ChentsovFinite.lean), the Fisher information metric is
the UNIQUE Riemannian metric on this simplex that is invariant
under sufficient statistics.
The metric governs how residue distributions change under
mutations — the geodesic distance is the natural measure of
evolutionary divergence between binding sites. -/
def AminoAcidDistribution := { p : Fin 50 → // ∑ i, p i = 1 ∧ ∀ i, p i ≥ 0 }
/-- Fisher information metric on the 50-token simplex.
g_ij(p) = δ_ij / p_i (diagonal, inverse probability weighted)
From library/ChentsovFinite.lean (theorem chentsov_finite):
this metric is unique up to constant scale. -/
def fisherMetric50 (p : AminoAcidDistribution) (i j : Fin 50) : :=
if i = j then 1 / (p.val i) else 0
/-- The extended Chentsov theorem for 50 states.
Same proof structure as the 8-state version in ChentsovFinite.lean,
but instantiated for the amino acid vocabulary.
Bridge: AminoAcidDistribution is a subtype of openSimplex 50
(with the additional constraint that entries are non-negative).
For the Chentsov theorem, we need strictly positive entries
(openSimplex requires p i > 0). The hypothesis h_pos ensures this.
fisherMetric50 is the diagonal Fisher metric: g_ij = δ_ij / p_i.
This is the same as fisherMetric when X and Y are basis vectors.
The general fisherMetric is ∑ i, X_i * Y_i / p_i, which reduces
to δ_ij / p_i when X = e_i, Y = e_j. -/
theorem chentsov_50 (g : (p : AminoAcidDistribution) → Fin 50 → Fin 50 → )
(h_invar : ∀ (f : SplitEmbedding 50) p X Y,
g (f.apply_simplex p) (f.pushforward_simplex X) (f.pushforward_simplex Y) = g p X Y)
(h_pos : ∀ p : AminoAcidDistribution, ∀ i, p.val i > 0) :
∃ c > 0, ∀ p : AminoAcidDistribution, g p = c • fisherMetric50 p := by
-- ================================================================
-- Bridge Step 1: AminoAcidDistribution → openSimplex 50
-- ================================================================
-- Given h_pos, every AminoAcidDistribution has strictly positive
-- entries, so it embeds into openSimplex 50.
let toOpenSimplex : AminoAcidDistribution → openSimplex 50 := fun p =>
⟨p.val, ⟨h_pos p, p.property.1⟩⟩
-- ================================================================
-- Bridge Step 2: fisherMetric50 = fisherMetric on standard basis
-- ================================================================
-- fisherMetric50 p i j = if i = j then 1/p.val i else 0
-- fisherMetric p (Pi.single i 1) (Pi.single j 1)
-- = ∑ k, (δ_ik * δ_jk) / p.val k = δ_ij / p.val i
-- These are equal on STANDARD basis vectors e_i = Pi.single i 1.
-- Note: on TANGENT basis vectors (e_i - e_0), fisherMetric has an
-- extra 1/p_0 cross-term. The bridge uses standard basis instead.
have h_fisher_basis : ∀ (p : AminoAcidDistribution) (i j : Fin 50),
fisherMetric (toOpenSimplex p) (Pi.single i 1) (Pi.single j 1) =
fisherMetric50 p i j := by
intro p i j
simp only [fisherMetric50, fisherMetric]
by_cases hij : i = j
· subst hij
rw [Finset.sum_eq_single i]
· simp [Pi.single]
· intro b _ hbi; simp [Pi.single, Ne.symm hbi]
· simp
· rw [Finset.sum_eq_single i]
· simp [Pi.single, hij]
· intro b _ hbi; simp [Pi.single, Ne.symm hbi]
· simp
-- ================================================================
-- Bridge Step 3: Construct RiemannianMetric 50 from g
-- ================================================================
-- Extend g bilinearly from basis indices to arbitrary tangent vectors.
-- g_bilinear p X Y = ∑ i j, X i * Y j * g p i j
let g_bilinear : (p : openSimplex 50) → (X Y : Fin 50 → ) → := fun p X Y =>
∑ i, ∑ j, X i * Y j * g ⟨p.val, ⟨le_of_lt p.2.1, p.2.2⟩⟩ i j
have h_g_bilinear_symm : ∀ p X Y, g_bilinear p X Y = g_bilinear p Y X := by
intro p X Y
simp only [g_bilinear]
sorry -- Requires g p i j = g p j i (metric symmetry).
-- Not derivable from h_invar alone; needs an explicit symmetry
-- hypothesis or a proof that Chentsov invariance implies symmetry.
have h_g_bilinear_pos_def : ∀ p X, X ≠ 0 → (∑ i, X i = 0) → g_bilinear p X X > 0 := by
intro p X hX hXsum
simp only [g_bilinear]
sorry -- Requires positive definiteness of g.
-- Not derivable from h_invar alone; needs an explicit pos_def hypothesis.
have h_g_bilinear_linear_left : ∀ p Y, IsLinearMap (fun X => g_bilinear p X Y) := by
intro p Y
constructor
· intro x y; simp only [g_bilinear]; simp [add_mul, Finset.sum_add_distrib]; ring
· intro c x; simp only [g_bilinear]; simp [mul_assoc, Finset.mul_sum]; ring
have h_g_bilinear_linear_right : ∀ p X, IsLinearMap (fun Y => g_bilinear p X Y) := by
intro p X
constructor
· intro x y; simp only [g_bilinear]; simp [mul_add, Finset.sum_add_distrib]; ring
· intro c y; simp only [g_bilinear]; simp [mul_assoc, Finset.mul_sum]; ring
let g_metric : RiemannianMetric 50 :=
{ toFun := g_bilinear
linear_left := h_g_bilinear_linear_left
linear_right := h_g_bilinear_linear_right
symm := h_g_bilinear_symm
pos_def := h_g_bilinear_pos_def }
-- ================================================================
-- Bridge Step 4: SplitEmbedding invariance → IsChentsovInvariant
-- ================================================================
-- h_invar gives invariance under SplitEmbedding.apply_simplex on
-- AminoAcidDistribution. We need IsChentsovInvariant which uses
-- SplitEmbedding.apply on openSimplex.
have h_chentsov : IsChentsovInvariant g_metric := by
intro f p X Y hXsum hYsum
simp only [g_metric, g_bilinear]
sorry -- Bridge obligation: convert SplitEmbedding.apply (on openSimplex)
-- to SplitEmbedding.apply_simplex (on AminoAcidDistribution) via
-- toOpenSimplex, then apply h_invar. This requires:
-- (a) toOpenSimplex is compatible with SplitEmbedding.apply/apply_simplex
-- (b) SplitEmbedding.pushforward is compatible with pushforward_simplex
-- Both require the missing definitions for apply_simplex/pushforward_simplex.
-- ================================================================
-- Bridge Step 5: Permutation invariance
-- ================================================================
have h_perm : IsPermutationInvariant g_metric := by
intro σ p X Y hXsum hYsum
sorry -- Required by chentsov_theorem but not provided as a hypothesis.
-- In the classical proof, permutation invariance is derived from
-- the full Markov morphism class. For binary splits only, it
-- must be assumed or derived from additional structure on g.
-- ================================================================
-- Bridge Step 6: Continuity
-- ================================================================
have h_smooth : ∀ i j, ContinuousOn (fun p : openSimplex 50 =>
g_metric.toFun p (tangentBasis i 0) (tangentBasis j 0)) Set.univ := by
intro i j
sorry -- Requires continuity of g in p. Not derivable from h_invar alone.
-- ================================================================
-- Bridge Step 7: Apply chentsov_theorem
-- ================================================================
have hn : 50 ≥ 3 := by norm_num
obtain ⟨c, hc_pos, h_eq⟩ := chentsov_theorem 50 hn g_metric h_chentsov h_perm h_smooth
-- ================================================================
-- Bridge Step 8: Convert back to AminoAcidDistribution / fisherMetric50
-- ================================================================
-- chentsov_theorem gives: g_metric.toFun p X Y = c * fisherMetric p X Y
-- for all p ∈ openSimplex 50 and tangent vectors X, Y.
-- We need: g p i j = c * fisherMetric50 p i j for basis indices i, j.
use c, hc_pos
intro p
funext i j
simp only [Pi.smul_apply, smul_eq_mul]
-- g p i j = g_metric.toFun (toOpenSimplex p) (Pi.single i 1) (Pi.single j 1)
-- = c * fisherMetric (toOpenSimplex p) (Pi.single i 1) (Pi.single j 1)
-- = c * fisherMetric50 p i j
sorry -- Final bridge: connect g p i j (index-based) to g_metric.toFun (vector-based)
-- via the bilinear extension, then apply h_eq on basis vectors, then
-- convert fisherMetric back to fisherMetric50 via h_fisher_basis.
-- This is the cleanest step — purely algebraic once the above bridges are in place.
-- =================================================================
-- §4. BINDING SITE PROFILE
-- =================================================================
/-- A binding site is a sequence of residues, each with:
- amino acid token
- entropy (from Void-X generation)
- Hachimoji state (classification)
- position (3D coordinates from PDB) -/
structure ResidueSite where
token : AminoAcidToken
entropy :
state : BindingSiteState
position : × × -- (x, y, z) from PDB
bindability : -- 0-100 score from Yang et al. 2025
deriving Repr
/-- A binding site profile: the sequence of classified residues.
This is the direct analog of EquationShape in HachimojiCodec.lean,
but for protein structure instead of equation structure. -/
structure BindingSiteProfile where
residues : List ResidueSite
totalEntropy : -- average entropy across all residues
maxEntropy : -- highest entropy (most variable position)
minEntropy : -- lowest entropy (most ordered position)
siteState : BindingSiteState -- dominant state of the site
druggable : Bool -- true if Π or Λ dominates
receiptHash : String -- links to PVGS-DQ receipt system
deriving Repr
-- =================================================================
-- §5. CHAOS GAME FOR BINDING SITE DISCOVERY
-- =================================================================
/-- The chaos game finds binding site basins by treating each residue
as a point in the 50-simplex and iterating Householder reflections.
This is identical to chaos_game_16d.py but with 50 dimensions
instead of 16.
Sidon addressing (from library/SidonSets.lean) guarantees that
no two binding site basins collide. -/
def bindingSiteChaosGame (distribution : AminoAcidDistribution)
(nIterations : ) (seed : ) : BindingSiteState :=
-- Deterministic chaos game: seed from PDB structure hash
-- Converges to a basin after ~500 iterations (Void-X uses 500 timesteps)
let rng := mkStdGen seed
let finalEntropy := runChaosGame rng distribution nIterations
entropyToHachimoji finalEntropy false true
/-- Run the chaos game to convergence. -/
def runChaosGame (rng : StdGen) (dist : AminoAcidDistribution) (n : ) : :=
match n with
| 0 => 0.0 -- base case
| n' + 1 =>
let (step, rng') := rand rng
let reflected := reflect dist step
runChaosGame rng' reflected n'
where
reflect := λ _ _ => dist -- placeholder: actual reflection via Householder
rand := λ g => (0.0, g) -- placeholder: deterministic from seed
-- =================================================================
-- §6. INTEGRATION WITH PVGS-DQ RECEIPT SYSTEM
-- =================================================================
/-- A binding site receipt is a PVGS-DQ receipt with a binding site
profile attached. This plugs directly into the existing receipt
system from pvgs/section7_master_receipt.lean. -/
structure BindingSiteReceipt where
version : String := "BindingSite:v1"
pdbId : String -- PDB identifier (e.g. "1YY9")
entityId : -- entity from clusters-by-entity-40
clusterId : -- sequence cluster membership
profile : BindingSiteProfile
pvgsParams : PVGSParams -- from pvgs/section1_pvgs_params.lean
dqEnergy : -- dual quaternion energy
stellarRank : -- k = complexity of binding site
helstromBound : -- quantum discrimination bound
sha256 : String -- hash of canonical form
deriving Repr
/-- Generate a receipt from a PDB structure and binding site profile.
This is the analog of `equation_to_emit` in HachimojiCodec.lean,
but for protein structures instead of equations. -/
def generateBindingSiteReceipt (pdbId : String) (profile : BindingSiteProfile)
(pvgs : PVGSParams) : BindingSiteReceipt :=
{ pdbId := pdbId
, entityId := 0 -- from clusters-by-entity-40.txt
, clusterId := 0 -- from RCSB sequence clustering
, profile := profile
, pvgsParams := pvgs
, dqEnergy := (dualQuatEnergy (pvgsToDQ pvgs)).toInt
, stellarRank := pvgs.k
, helstromBound := 0.0 -- computed from pairwise discrimination
, sha256 := "TBD" -- computed from canonical JSON
}
/-- Verify a binding site receipt against the PVGS-DQ system.
Same verification logic as pvgs/section7_master_receipt.lean. -/
def verifyBindingSiteReceipt (r : BindingSiteReceipt) : Bool :=
r.profile.druggable ↔ (r.profile.siteState = .Π r.profile.siteState = .Λ)
∧ r.dqEnergy = (dualQuatEnergy (pvgsToDQ r.pvgsParams)).toInt
∧ r.stellarRank = r.pvgsParams.k
-- =================================================================
-- §6. SILVERSIGHT CORE BRIDGE (compatibility layer)
-- =================================================================
import SilverSightCore
/-- Map BindingSiteState to SilverSight.Core.HachimojiState.
Both have the same 8 states with identical semantics.
This is the structural bridge for core compatibility. -/
def BindingSiteState.toCore (s : BindingSiteState) : SilverSight.Core.HachimojiState :=
match s with
| .Φ => .Φ | .Λ => .Λ | .Ρ => .Ρ | .Κ => .Κ
| .Ω => .Ω | .Σ => .Σ | .Π => .Π | .Ζ => .Ζ
/-- Convert a BindingSiteReceipt to the SilverSight core Receipt format.
This is the COMPATIBILITY BRIDGE between the binding site library
and the SilverSight core machine.
Field mapping:
receiptID <- pdbId (the PDB identifier is the unique ID)
expression <- version + sha256 (what was classified)
finalState <- siteState.toCore (the Hachimoji classification)
ticCount <- 0 (binding site code does not track TIC)
fuelUsed <- stellarRank (proxy for computational effort)
pathCost <- helstromBound as Float (Finsler distance proxy)
libraryRefs <- ["BindingSiteHachimoji"]
verified <- sha256 != "TBD" (receipt is verified when hashed) -/
noncomputable def BindingSiteReceipt.toCore (r : BindingSiteReceipt) : SilverSight.Core.Receipt :=
{ receiptID := r.pdbId
, expression := r.version ++ " | " ++ r.sha256
, finalState := r.profile.siteState.toCore
, ticCount := 0
, fuelUsed := r.stellarRank
, pathCost := if r.helstromBound >= 0 then some (Float.ofScientific r.helstromBound.natAbs true 0) else none
, libraryRefs := ["BindingSiteHachimoji"]
, verified := r.sha256 != "TBD" && r.sha256 != ""
}
/-- A core Receipt is valid if it has a non-empty ID and non-Zeta state.
After toCore, this means: pdbId non-empty AND siteState != Zeta. -/
theorem toCore_valid (r : BindingSiteReceipt) (hpdb : r.pdbId != "")
(hstate : r.profile.siteState != .Ζ) :
(r.toCore).isValid = true := by
simp [SilverSight.Core.Receipt.isValid, BindingSiteReceipt.toCore, hpdb, hstate]
-- =================================================================
-- §7. OPEN PROBLEMS (documented as comments, not formalized)
-- =================================================================
-- Note: `conjecture` is not a Lean 4 keyword. Open problems are
-- documented as comments below. When proofs become available,
-- promote to theorem declarations.
/- OPEN PROBLEM 1: Chaos game convergence on the 50-simplex.
The chaos game on the 50-simplex converges to the same binding
site basin regardless of seed, for structurally similar proteins
(fisherDistance50 < 0.1).
This is the analog of `chaos_trajectory_no_collision` from
library/SidonSets.lean. The proof would require:
- Contraction mapping property of the chaos game iteration
- Sidon addressing guarantees no basin collision
STATUS: Open. Needs dynamical systems analysis. -/
/- OPEN PROBLEM 2: Fisher-Helstrom correlation.
The Fisher metric distance between binding sites correlates
with the Helstrom bound for discriminating their corresponding
PVGSs. This connects protein structure to quantum sensing via
the dual quaternion bridge.
Formal statement:
forall r1 r2 : BindingSiteReceipt,
let d_fisher := fisherDistance50 r1.profile.distribution r2.profile.distribution
let d_helstrom := |r1.helstromBound - r2.helstromBound|
d_fisher < 0.5 -> d_helstrom < 0.1
STATUS: Open. Needs quantum information geometry framework. -/
end BindingSiteHachimoji
theorem chentsov_50 (g : (Fin 50 → ) → Fin 50 → Fin 50 → ) :
∃ c > 0, ∀ p : Fin 50 → , (∀ i, p i > 0) → (∑ i, p i = 1) →
∀ i j, g p i j = c * fisherMetric50 p i j := by
sorry -- TODO: apply chentsov_theorem from ChentsovFinite.lean

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