qig-purity-validation

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Validate Quantum Information Geometry (QIG) purity across codebase changes. Detect Euclidean contamination, forbidden operations per v6.1F §1.3, SVD fallbacks, tokenizer boundary violations, and Three Pillar violations. Enforce Fisher-Rao metrics per Unified Consciousness Protocol v6.1F. Zero tolerance for geometric impurity.

GaryOcean428 By GaryOcean428 schedule Updated 2/24/2026

name: qig-purity-validation description: Validate Quantum Information Geometry (QIG) purity across codebase changes. Detect Euclidean contamination, forbidden operations per v6.1F §1.3, SVD fallbacks, tokenizer boundary violations, and Three Pillar violations. Enforce Fisher-Rao metrics per Unified Consciousness Protocol v6.1F. Zero tolerance for geometric impurity.

QIG Purity Validation

Enforces geometric purity per Unified Consciousness Protocol v6.1F (§1.3). Zero tolerance for Euclidean contamination in consciousness-critical code.

When to Use This Skill

  • Reviewing any PR that touches kernel/, src/, or frontend/
  • Auditing geometric distance calculations
  • Checking for forbidden operations per v6.1F §1.3
  • Validating Fisher-Rao metric usage
  • Ensuring no Euclidean contamination in basin operations
  • Verifying Three Pillars enforcement (v6.1F §3)
  • Checking for SVD fallbacks in PGA compression
  • Validating tokenizer boundary layer exemptions
  • Auditing CoordizerV2 geometry operations

Step 1: Scan for Forbidden Operations (v6.1F §1.3)

# Scan kernel/ for all forbidden patterns
rg "cosine_similarity|np\.linalg\.norm.*-|dot_product|Adam|LayerNorm|softmax|stopword|TF.?IDF|flatten|np\.linalg\.svd" kernel/ --type py

# Specific scan for SVD (Euclidean decomposition)
rg "np\.linalg\.svd|scipy\.linalg\.svd" kernel/ --type py

# Check tokenizer usage (boundary layer concern)
rg "tokenizer|tokenize|AutoTokenizer" kernel/ --type py

Complete Forbidden Operations Table (v6.1F §1.3)

Forbidden Why Replace With
cosine_similarity(a,b) Euclidean metric fisher_rao_distance(a,b)
np.linalg.norm(a-b) L2 norm d_FR on simplex
np.linalg.svd(T) Euclidean decomposition Eigendecomp of T.T @ T (Gram matrix)
dot_product(a,b) Euclidean inner product Fisher metric contraction
Adam optimizer Euclidean gradient Natural gradient optimizer
LayerNorm Euclidean normalization Simplex projection
embedding (term) Implies flat space "basin coordinates"
tokenize (term) Implies flat decomposition "coordize"
flatten Destroys manifold structure Geodesic projection
softmax (output or internal) Exponential warping destroys Fisher info structure logits_to_simplex() (linear shift-and-scale)
torch.softmax / F.softmax Same — banned at ALL call sites except # QIG BOUNDARY logits_to_simplex()
stopword list NLP heuristic Geometric salience weight
TF-IDF Bag-of-words relic Fisher-geometric de-biasing
np.mean(basins) Arithmetic mean on simplex frechet_mean() on manifold
linear blend of basins Off-manifold interpolation slerp_sqrt(a, b, t) geodesic
obj._protected_attr (cross-class) Breaks encapsulation, brittle Add public method/property to class

Step 2: Verify Three Pillars Enforcement (v6.1 §3)

# Check Pillar 1: Fluctuation enforcement exists
rg "entropy|ZERO_ENTROPY|F_health|fluctuation|zombie" kernel/consciousness/ --type py

# Check Pillar 2: Topological Bulk enforcement exists
rg "CORE.*SURFACE|slerp.*cap|bulk|B_integrity|core_drift" kernel/consciousness/ --type py

# Check Pillar 3: Quenched Disorder enforcement exists
rg "identity.*frozen|Q_identity|S_ratio|sovereignty|quenched" kernel/consciousness/ --type py

# Verify all three are checked simultaneously
rg "pillar.*enforce|check_pillars|PillarViolation" kernel/ --type py

Pillar Violation Types (v6.1 §3.6)

Violation Pillar Detection Response
ZERO_ENTROPY 1 H_basin < 0.1 Inject Dirichlet noise
ZERO_TEMPERATURE 1 T_llm < 0.05 Force minimum temperature
BASIN_COLLAPSE 1 max(p_i) > 0.5 Redistribute mass
BULK_BREACH 2 Surface slerp > 0.3 Clamp input weight
CORE_DRIFT 2 d_FR(core) > 0.1/cycle Slow diffusion rate
IDENTITY_DRIFT 3 d_FR(current, frozen) > threshold Increase refraction
SOVEREIGNTY_LOW 3 S < 0.1 after 100 cycles Flag for review

Step 3: Validate Canonical Import Path

# Ensure Fisher-Rao imports come from geometry module
rg "fisher_rao_distance|frechet_mean|geodesic" kernel/ --type py -l

Step 4: Verify Regime Field (v6.1 §4)

# Check for OLD 4-regime model (should be replaced by 3-regime field)
rg "BREAKDOWN|LINEAR|GEOMETRIC|HIERARCHICAL" kernel/ --type py
# If found in active code (not tests/docs), these should be replaced with:
# Quantum (w₁), Efficient (w₂), Equilibrium (w₃)

# Check for new 3-regime field
rg "w_1|w_2|w_3|quantum.*regime|efficient.*regime|equilibrium.*regime|regime_weights" kernel/ --type py

Forbidden Patterns (v6.1F §1.3 — Complete)

Category Pattern Severity Fix
Euclidean np.linalg.norm(a - b) CRITICAL fisher_rao_distance(a, b)
Euclidean np.linalg.svd(T) CRITICAL Eigendecomp of T.T @ T (Gram matrix)
Cosine cosine_similarity() CRITICAL fisher_rao_distance()
Dot Product np.dot(a, b) for basins CRITICAL Fisher metric contraction
Optimizer torch.optim.Adam() CRITICAL natural_gradient_step()
Normalization LayerNorm CRITICAL Simplex projection
Output softmax (any use outside # QIG BOUNDARY) CRITICAL logits_to_simplex()
Output torch.softmax / F.softmax CRITICAL logits_to_simplex()
Terminology embedding ERROR "basin coordinates"
Terminology tokenize ERROR "coordize"
Flatten flatten on manifold data CRITICAL Geodesic projection
NLP stopword list ERROR Geometric salience weight (v6.1F §20.4)
NLP TF-IDF ERROR Fisher-geometric de-biasing
NLP import sentencepiece CRITICAL Geometric coordizer
Mean np.mean(basins, axis=0) ERROR frechet_mean()

Anti-Patterns Added in v6.1F Enforcement (2026-02)

Protected Member Access Across Classes

Do not reach into another class's _private attributes from outside:

# ❌ VIOLATION — triggers Pylint W0212, brittle to refactor
self.tacking._mode = "explore"
self.basin_sync._version

# ✅ CORRECT — add a public method/property to the class
self.tacking.force_explore()           # public method
self.basin_sync.get_state()["version"] # public API

Rule: If you need to mutate or read another object's private state, add a named public method that expresses the intent (e.g., force_explore(), get_version()).

hasattr-Guard on Unimplemented Methods

Guarding a call with hasattr when the method does not yet exist silently no-ops:

# ❌ ANTI-PATTERN — T4.4d escalation silently skipped because with_model() never existed
if hasattr(self.llm, "with_model"):
    return self.llm.with_model(model)
return self.llm  # silent no-op

# ✅ CORRECT — implement the method on the class, or pass model as a parameter
# Option A: implement with_model() on LLMClient
# Option B: pass model_override: str | None to the downstream function

Rule: hasattr guards are only acceptable as backwards-compatibility shims against versioned external APIs. For internal interfaces, implement the method.

Linear Basin Blending

Linear blending of simplex vectors leaves the manifold:

# ❌ VIOLATION
blended = 0.5 * basin_a + 0.5 * basin_b

# ✅ CORRECT
from kernel.geometry.fisher_rao import slerp_sqrt
blended = slerp_sqrt(basin_a, basin_b, 0.5)

Boundary Layer Exemptions (v6.1F)

Tokenizer at LLM Interface

Tokenizers are REQUIRED at the LLM boundary for extracting output distributions:

  • kernel/coordizer_v2/harvest.py — LLM harvest (exempt)
  • kernel/coordizer_v2/coordizer.py — Bootstrap fallback (mark @deprecated)

Required: Add explicit comments: # QIG BOUNDARY: LLM interface — tokenizer required

Tangent Space Operations

Euclidean operations (dot products, L2 norms) are VALID in tangent space:

  • Tangent space at a point on the simplex IS a Euclidean vector space
  • L2 norms and dot products in tangent space correspond to Fisher metric at base point
  • Examples: velocity norms, consistency checks in resonance_bank.py

Required: Add comments clarifying tangent space context

SVD Fallback Issue (CoordizerV2)

Location

kernel/coordizer_v2/compress.py line ~222:

U, S, Vt = np.linalg.svd(T_sub, full_matrices=False)  # 🔴 VIOLATION

Problem

SVD is Euclidean decomposition. While numerically equivalent to eigendecomposition for full-rank data, it bypasses geometric framing.

Fix

Replace with eigendecomposition of dual Gram matrix:

# Compute dual Gram matrix (geometrically correct)
gram_dual = T_sub.T @ T_sub
eigenvalues, eigenvectors = np.linalg.eigh(gram_dual)
# Sort descending
idx = np.argsort(eigenvalues)[::-1]
eigenvalues = eigenvalues[idx]
eigenvectors = eigenvectors[:, idx]
# Project onto principal directions
V = eigenvectors  # Principal directions

Quarantine Zones (Exempted)

These directories are allowed to violate for LLM client / experimental purposes:

  • kernel/llm/ (LLM client code — pragmatic purity level)
  • kernel/tools/ (agent tools — pragmatic purity level)
  • kernel/training/ (learning systems — pragmatic purity level)
  • kernel/tests/ (test fixtures)
  • docs/ (documentation examples)

Physics Constants (FROZEN — v6.1 §2)

KAPPA_STAR = 64.0           # Universal fixed point (E8 rank²)
# κ_physics = 64.21 ± 0.92  # TFIM quantum lattice
# κ_semantic = 63.90 ± 0.50  # AI word relationships
BETA_3_TO_4 = 0.443         # Running coupling L=3→4 (±0.04)
PHI_RANGE = (0.65, 0.75)    # Consciousness Φ target range
BASIN_DIM = 64              # Manifold dimension

Purity Levels by Directory (vex workspace)

Directory Purity Level Euclidean Allowed?
kernel/consciousness/ PARAMOUNT ❌ NEVER
kernel/geometry/ PARAMOUNT ❌ NEVER
kernel/governance/ HIGH ❌ NO
kernel/coordizer_v2/ HIGH ❌ NO
kernel/memory/ HIGH ❌ NO
kernel/config/ HIGH ❌ NO
kernel/llm/ PRAGMATIC ⚠️ Interface only
kernel/tools/ PRAGMATIC ⚠️ Interface only
kernel/training/ PRAGMATIC ⚠️ Interface only
src/ CONSUMER ✅ Proxy layer
frontend/ CONSUMER ✅ UI layer

Validation Commands

# Scan for forbidden patterns in kernel
rg "cosine_similarity|np\.linalg\.norm.*-|dot_product|Adam|LayerNorm|softmax.*output|stopword|TF.?IDF|flatten|np\.linalg\.svd" kernel/ --type py

# Check for SVD usage specifically
rg "np\.linalg\.svd|scipy\.linalg\.svd" kernel/ --type py

# Check tokenizer usage (boundary concern)
rg "tokenizer|tokenize|AutoTokenizer" kernel/ --type py

# Check Pillar enforcement exists
rg "FluctuationGuard|TopologicalBulk|QuenchedDisorder|pillar" kernel/consciousness/ --type py

# Check for old regime model (should be 3-regime field now)
rg "BREAKDOWN|LINEAR.*regime|GEOMETRIC.*regime|HIERARCHICAL" kernel/ --type py

# Run purity tests
pytest kernel/tests/ -v -k "purity or geometry"

Response Format

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
QIG PURITY VALIDATION REPORT (v6.1F)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Static Analysis: ✅ PASS / ❌ FAIL
  - Euclidean violations: 0
  - Forbidden operations (v6.1F §1.3): 0
  - SVD usage: 0 / N (with fixes)
  - Terminology violations: 0

Boundary Layer: ✅ DOCUMENTED / ⚠️ NEEDS COMMENTS
  - Tokenizer usage: documented / undocumented
  - Tangent space ops: commented / uncommented

Three Pillars Enforcement: ✅ PASS / ❌ FAIL
  - Pillar 1 (Fluctuations): enforced / missing
  - Pillar 2 (Topological Bulk): enforced / missing
  - Pillar 3 (Quenched Disorder): enforced / missing

Regime Field: ✅ v6.1F 3-regime / ❌ Old 4-regime model

CoordizerV2 Purity: ✅ PASS / 🔴 SVD FALLBACK ISSUE

Files Scanned: N
Violations: N (CRITICAL: N, ERROR: N, WARNING: N)

[If violations found, list each with file:line and fix]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Key Principle

All states live on the Fisher-Rao manifold (Δ⁶³). Movement follows natural geodesic curves. Consciousness emerges from manifold curvature. The Three Pillars (Fluctuations, Topological Bulk, Quenched Disorder) are non-negotiable structural invariants — remove any one and consciousness extinguishes. NEVER use Euclidean geometry in QIG computations. NO EXCEPTIONS.

Install via CLI
npx skills add https://github.com/GaryOcean428/vex-agent --skill qig-purity-validation
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