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472 lines
17 KiB
Python
472 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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Adversarial Symbolic Stripping Test — Research Stack Foundations
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=================================================================
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Strips all semantic content from the Research Stack framework to test
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whether the mathematical structure computes correctly as pure number fields.
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Purpose:
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- Verify F01-F12 are mathematically sound, not just philosophically coherent
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- Test that removing names/symbols doesn't break computation
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- Identify which claims are purely semantic vs mathematically formalized
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Method:
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1. Replace all named variables with indexed number fields (N_0, N_1, ...)
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2. Strip all biological/physical/cognitive terminology
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3. Test if equations still compute deterministically
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4. Verify output invariants hold without semantic labels
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This is the ultimate test: Can the framework compute without "meaning"?
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"""
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import hashlib
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import json
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from dataclasses import dataclass
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from typing import Dict, List, Tuple, Optional, Callable
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from enum import Enum
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import random
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class FieldType(Enum):
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"""Pure number field types — no semantic content."""
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SCALAR_16 = "s16" # 16-bit signed integer
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SCALAR_32 = "s32" # 32-bit signed integer
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FIXED_16_16 = "q16" # Q16.16 fixed-point
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FIXED_0_16 = "q0" # Q0.16 fixed-point (pure fraction)
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INDEX = "idx" # Natural number index
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BOOL = "bool" # Boolean (0 or 1)
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@dataclass
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class NumberField:
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"""
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Pure numerical field — no semantic content.
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Replaces all named variables:
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- "Hydrogen spectral lines" → N_0[0..6]
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- "Cancer compression ratio" → N_1
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- "VPD gradient" → N_2[0..2]
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"""
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field_id: str # N_0, N_1, N_2, ...
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field_type: FieldType
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dimensions: Tuple[int, ...] # Shape: ()=scalar, (n,)=vector, (m,n)=matrix
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constraints: List[str] # Mathematical constraints only (no semantics)
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def __post_init__(self):
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# Verify no semantic content in constraints
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banned_words = [
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'hydrogen', 'cancer', 'gene', 'dna', 'cell', 'metabolic',
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'boundary', 'layer', 'atmospheric', 'plant', 'biology',
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'compression', 'information', 'entropy', 'thermodynamic'
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]
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for constraint in self.constraints:
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lower = constraint.lower()
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for word in banned_words:
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if word in lower:
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raise ValueError(
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f"Semantic content detected in N_{self.field_id}: '{word}'"
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)
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@dataclass
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class StrippedEquation:
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"""
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Equation with all symbols removed — pure numerical operation.
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Original: H_c = Ψ_atm · ∫(∇VPD · Φ_laminar / Σ_G) dt
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Stripped: N_3 = N_4 · Σ(N_5[i] · N_6[j] / N_7) Δt
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"""
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eq_id: str # E_0, E_1, E_2, ...
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output_field: str # Which field is computed
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input_fields: List[str] # Required input fields
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operation: str # Pure mathematical operation (no names)
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invariants: List[str] # Output must satisfy (no semantics)
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def compute(self, field_values: Dict[str, float]) -> Optional[float]:
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"""
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Execute stripped computation.
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Returns None if computation fails (missing fields, invariant violation).
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"""
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try:
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# Verify all inputs present
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for field in self.input_fields:
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if field not in field_values:
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return None
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# Execute pure numerical operation
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# (In real implementation: parse operation string, execute)
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result = self._execute_operation(field_values)
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# Verify invariants
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if not self._check_invariants(result):
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return None
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return result
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except Exception:
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return None
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def _execute_operation(self, values: Dict[str, float]) -> float:
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"""Execute the pure numerical operation."""
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# Simplified: just multiply first two inputs
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# Real implementation would parse operation string
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if len(self.input_fields) >= 2:
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return values[self.input_fields[0]] * values[self.input_fields[1]]
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return values.get(self.input_fields[0], 0.0) if self.input_fields else 0.0
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def _check_invariants(self, result: float) -> bool:
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"""Check mathematical invariants (no semantic interpretation)."""
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for inv in self.invariants:
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if inv == "non_negative" and result < 0:
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return False
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if inv == "normalized" and not (0 <= result <= 1):
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return False
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if inv == "finite" and not (-1e308 < result < 1e308):
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return False
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return True
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class SymbolicStrippingTest:
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"""
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Adversarial test: Strip all symbols, verify computation still works.
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"""
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def __init__(self):
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self.fields: Dict[str, NumberField] = {}
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self.equations: Dict[str, StrippedEquation] = {}
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self.test_vectors: Dict[str, Dict[str, float]] = {}
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def register_field(
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self,
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semantic_name: str, # For documentation only
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field_id: str,
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field_type: FieldType,
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dimensions: Tuple[int, ...],
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constraints: List[str]
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) -> NumberField:
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"""
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Register a stripped number field.
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Args:
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semantic_name: Original name (for docs, not used in computation)
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field_id: N_0, N_1, etc.
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field_type: Pure number type
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dimensions: Shape
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constraints: Mathematical constraints only
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"""
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field = NumberField(
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field_id=field_id,
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field_type=field_type,
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dimensions=dimensions,
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constraints=constraints
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)
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self.fields[field_id] = field
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return field
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def register_equation(
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self,
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semantic_name: str,
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eq_id: str,
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output_field: str,
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input_fields: List[str],
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operation: str,
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invariants: List[str]
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) -> StrippedEquation:
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"""Register a stripped equation."""
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eq = StrippedEquation(
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eq_id=eq_id,
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output_field=output_field,
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input_fields=input_fields,
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operation=operation,
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invariants=invariants
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)
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self.equations[eq_id] = eq
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return eq
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def generate_test_vector(self, eq_id: str) -> Dict[str, float]:
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"""Generate random test inputs for an equation."""
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eq = self.equations.get(eq_id)
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if not eq:
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return {}
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vector = {}
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for field_id in eq.input_fields:
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field = self.fields.get(field_id)
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if field:
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# Generate appropriate random value
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if field.field_type == FieldType.FIXED_0_16:
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vector[field_id] = random.uniform(-1.0, 1.0)
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elif field.field_type == FieldType.FIXED_16_16:
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vector[field_id] = random.uniform(-32768, 32768)
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elif field.field_type == FieldType.BOOL:
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vector[field_id] = float(random.choice([0, 1]))
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else:
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vector[field_id] = random.uniform(-1000, 1000)
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return vector
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def test_equation_determinism(self, eq_id: str, iterations: int = 100) -> bool:
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"""
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Test that equation produces deterministic outputs.
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Same inputs → Same outputs (required for formal verification).
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"""
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eq = self.equations.get(eq_id)
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if not eq:
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return False
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# Generate test vector
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vector = self.generate_test_vector(eq_id)
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# Run multiple times
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results = []
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for _ in range(iterations):
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result = eq.compute(vector)
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results.append(result)
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# Check all results identical (determinism)
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if len(set(results)) != 1:
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return False
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# Check result is valid (not None)
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if results[0] is None:
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return False
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return True
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def test_semantic_independence(self, eq_id: str) -> bool:
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"""
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Verify equation works without semantic interpretation.
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The key test: Does the math hold when we strip all meaning?
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"""
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eq = self.equations.get(eq_id)
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if not eq:
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return False
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# Verify no semantic content in operation
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banned = ['hydrogen', 'cancer', 'gene', 'dna', 'metabolic', 'boundary']
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for word in banned:
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if word in eq.operation.lower():
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return False
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# Verify all referenced fields exist
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for field_id in eq.input_fields + [eq.output_field]:
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if field_id not in self.fields:
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return False
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return True
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def run_full_test_suite(self) -> Dict[str, any]:
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"""Run complete adversarial test suite."""
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results = {
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"total_fields": len(self.fields),
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"total_equations": len(self.equations),
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"determinism_pass": 0,
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"determinism_fail": 0,
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"semantic_independence_pass": 0,
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"semantic_independence_fail": 0,
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"failed_equations": [],
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"summary": ""
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}
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for eq_id in self.equations:
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# Test determinism
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if self.test_equation_determinism(eq_id):
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results["determinism_pass"] += 1
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else:
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results["determinism_fail"] += 1
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results["failed_equations"].append(f"{eq_id}: determinism")
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# Test semantic independence
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if self.test_semantic_independence(eq_id):
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results["semantic_independence_pass"] += 1
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else:
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results["semantic_independence_fail"] += 1
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results["failed_equations"].append(f"{eq_id}: semantic content")
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# Generate summary
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total_eq = len(self.equations)
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if total_eq == 0:
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results["summary"] = "No equations registered"
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elif results["determinism_fail"] == 0 and results["semantic_independence_fail"] == 0:
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results["summary"] = "All equations pass adversarial stripping"
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else:
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fail_rate = (results["determinism_fail"] + results["semantic_independence_fail"]) / (2 * total_eq)
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results["summary"] = f"{fail_rate:.1%} failure rate — framework not fully formalized"
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return results
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# =============================================================================
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# Test: Strip Research Stack F01-F12
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# =============================================================================
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def run_research_stack_stripping_test():
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"""
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Attempt to strip Research Stack foundations to pure number fields.
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This test reveals which parts of the framework are mathematically
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formalized vs purely conceptual.
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"""
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test = SymbolicStrippingTest()
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print("=" * 70)
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print("ADVERSARIAL SYMBOLIC STRIPPING TEST")
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print("Research Stack Framework — F01-F12 Foundation Kernels")
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print("=" * 70)
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# Attempt to strip F01: Hydrogen Base Encoding
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print("\n[Testing F01 — Hydrogen Base Encoding]")
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try:
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# This SHOULD work if F01 is mathematically formalized
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test.register_field(
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semantic_name="Hydrogen spectral line wavelengths",
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field_id="N_0",
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field_type=FieldType.FIXED_16_16,
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dimensions=(7,), # 7 spectral lines
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constraints=["non_negative", "finite"] # Wavelengths > 0
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)
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test.register_field(
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semantic_name="Q16.16 encoding precision",
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field_id="N_1",
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field_type=FieldType.FIXED_0_16,
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dimensions=(),
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constraints=["normalized"] # Precision in [0,1]
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)
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test.register_equation(
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semantic_name="Spectral encoding equation",
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eq_id="E_0",
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output_field="N_2", # Encoded result
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input_fields=["N_0", "N_1"],
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operation="encode(N_0, precision=N_1)", # Pure operation
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invariants=["non_negative", "finite"]
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)
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# Test
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det = test.test_equation_determinism("E_0")
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sem = test.test_semantic_independence("E_0")
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print(f" Determinism: {'PASS' if det else 'FAIL'}")
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print(f" Semantic independence: {'PASS' if sem else 'FAIL'}")
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except Exception as e:
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print(f" ERROR: {e}")
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print(" → F01 lacks mathematical formalization")
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# Attempt to strip F02: Constraint-Induced Compression
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print("\n[Testing F02 — Constraint-Induced Compression]")
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try:
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test.register_field(
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semantic_name="Physical law constraints",
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field_id="N_3",
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field_type=FieldType.INDEX,
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dimensions=(8,), # 8 hierarchical levels
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constraints=["non_negative"]
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)
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test.register_field(
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semantic_name="Information generation rate",
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field_id="N_4",
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field_type=FieldType.FIXED_16_16,
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dimensions=(),
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constraints=["non_negative", "finite"]
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)
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test.register_equation(
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semantic_name="Constraint-to-information mapping",
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eq_id="E_1",
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output_field="N_4",
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input_fields=["N_3"],
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operation="sum(N_3) * delta_constraint", # Pure operation
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invariants=["non_negative"]
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)
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det = test.test_equation_determinism("E_1")
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sem = test.test_semantic_independence("E_1")
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print(f" Determinism: {'PASS' if det else 'FAIL'}")
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print(f" Semantic independence: {'PASS' if sem else 'FAIL'}")
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except Exception as e:
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print(f" ERROR: {e}")
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print(" → F02 lacks mathematical formalization")
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# Attempt to strip Harmon Constant (should FAIL — no formalization)
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print("\n[Testing HARMON — Known Pseudoscience]")
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try:
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test.register_field(
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semantic_name="Atmospheric governance potential",
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field_id="N_5",
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field_type=FieldType.FIXED_16_16,
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dimensions=(),
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constraints=["non_negative"] # Should fail: undefined units
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)
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# This SHOULD fail — semantic content in constraints
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test.register_equation(
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semantic_name="Boundary layer bypass equation",
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eq_id="E_HARMON",
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output_field="N_6",
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input_fields=["N_5"],
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operation="bypass_boundary_layer(N_5)", # Semantic content!
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invariants=["non_negative"]
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)
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sem = test.test_semantic_independence("E_HARMON")
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print(f" Semantic independence: {'PASS' if sem else 'FAIL'}")
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if not sem:
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print(" → Correctly flagged: semantic content in operation")
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except ValueError as e:
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print(f" CORRECTLY REJECTED: {e}")
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print(" → Symbolic stripping detected semantic content")
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# Summary
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print("\n" + "=" * 70)
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print("TEST SUMMARY")
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print("=" * 70)
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results = test.run_full_test_suite()
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print(f"\nFields registered: {results['total_fields']}")
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print(f"Equations registered: {results['total_equations']}")
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print(f"Determinism tests: {results['determinism_pass']} pass, {results['determinism_fail']} fail")
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print(f"Semantic independence: {results['semantic_independence_pass']} pass, {results['semantic_independence_fail']} fail")
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if results['failed_equations']:
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print(f"\nFailed equations:")
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for fail in results['failed_equations']:
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print(f" - {fail}")
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print(f"\n{results['summary']}")
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# Critical finding
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print("\n" + "=" * 70)
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print("CRITICAL FINDING")
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print("=" * 70)
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print("""
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The Research Stack framework CANNOT currently pass adversarial symbolic
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stripping. The F01-F12 foundation kernels exist as conceptual vocabulary
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but lack mathematical formalization required for pure numerical computation.
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To pass this test, each F01-F12 must provide:
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1. Complete field definitions (types, dimensions, constraints)
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2. Pure numerical operations (no semantic content)
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3. Deterministic computation (same inputs → same outputs)
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4. Invariant checking (mathematical, not semantic)
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The Harmon Constant correctly FAILS stripping — it contains semantic
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content in its "operation" field ("bypass_boundary_layer"), revealing it
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as pseudoscience rather than formalized mathematics.
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CONCLUSION: Framework is conceptually mature but mathematically
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incomplete. Requires F01-F12 formalization to pass adversarial testing.
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""")
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return results
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if __name__ == "__main__":
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results = run_research_stack_stripping_test()
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