Technical Architecture

Synthetic Data Generation Phase 1

Neurosymbolic reasoning enables unlimited training data generation from axioms—guaranteed correct, no privacy concerns, no human labeling required.

Why Synthetic Data?

Traditional machine learning requires massive labeled datasets, raising privacy concerns and scaling challenges. Symbolic systems can generate infinite correct examples from finite rules.

Generation Techniques

Real-World Example: AlphaGeometry

Google DeepMind's AlphaGeometry (Nature 2024) used synthetic data generation to achieve near-IMO-gold-medal performance:

• 100 million synthetic geometry problems generated from axioms
• No human-labeled training data required
• Solved 25/30 IMO geometry problems (human gold medalists average 25.9)
• Combination: neural language model + symbolic deduction engine

Implementation Stack

# Example: Generate integration training pairs with SymPy
import sympy as sp
from sympy import symbols, diff, integrate, lambdify
import random

x = symbols('x')

# Generate random polynomial
def random_polynomial(degree=3):
    coeffs = [random.randint(-10, 10) for _ in range(degree + 1)]
    return sum(c * x**i for i, c in enumerate(coeffs))

# Generate training pair
polynomial = random_polynomial()
derivative = diff(polynomial, x)

# Training example: (derivative, polynomial)
# Task: Given derivative, find original function
print(f"Problem: Integrate {derivative}")
print(f"Solution: {polynomial}")

# Generate unlimited examples...

Knowledge Graph Reasoning Phase 1

Hybrid queries combining symbolic logic with semantic similarity using ArangoDB + PyKEEN (40+ embedding models).

Hybrid Reasoning Architecture

Traditional databases handle exact queries (SQL). Neural embeddings handle semantic similarity (vector search). Knowledge graphs combine both—logical constraints + semantic relevance.

Hybrid Query Example

Query: "Find tasks similar to 'code_generation' that require 'code_analysis' capability"

Step 1 - Symbolic Filter (AQL):
FOR t IN tasks
  FILTER t.requires_capability == 'code_analysis'
  RETURN t

Step 2 - Neural Ranking (PyKEEN):
embedding_similarity(t.embedding, code_generation.embedding)
  → Rank by semantic relevance

Result: Logically valid candidates + semantically relevant

PyKEEN: 40+ Embedding Models

Real-World Applications

• Tool Discovery: Find tools with similar capabilities (semantic) that satisfy constraints (symbolic)
• Task Composition: Recommend compatible task phases based on past successes
• Error Diagnosis: Find similar past failures with known solutions
• Knowledge Completion: Predict missing facts from learned patterns

Explainability & Regulatory Compliance Phase 1

Symbolic reasoning traces provide automatic audit trails for AI decisions—critical for EU AI Act compliance and enterprise trust.

The Explainability Problem

Pure neural networks are black boxes—opaque decision-making that can't be audited or verified. This creates serious problems:

• Regulatory Risk: EU AI Act (2024) mandates explainability for high-risk systems
• Trust Issues: Enterprises won't deploy systems they can't understand
• Debugging Challenges: Can't fix what you can't trace
• Liability Concerns: Who's responsible when AI makes a mistake?

Symbolic Reasoning = Built-in Explanations

Every symbolic inference produces a complete logical trace—no post-hoc explanation methods needed.

Example: Explainable Task Execution

Decision: Execute task phase "code_generation"

Symbolic Trace (Scallop/Datalog):
  1. task_phase(code_generation)
  2. requires_capability(code_generation, code_analysis)
  3. has_tool_with_capability(claude_sonnet, code_analysis)
  4. precondition_satisfied(code_generation)
  5. ∴ can_execute(code_generation)

Natural Language Explanation:
  "The system executed the 'code_generation' task phase because:
   - It is a valid registered task phase
   - It requires the 'code_analysis' capability
   - The 'claude_sonnet' tool provides this capability and is currently available
   - All preconditions are satisfied
   - Therefore, execution is authorized"

Audit Trail (ArangoDB):
  {
    "decision_id": "dec_20251022_143022",
    "timestamp": "2025-10-22T14:30:22Z",
    "decision": "execute",
    "target": "code_generation",
    "tool_selected": "claude_sonnet",
    "reasoning_chain": [...],
    "fact_sources": ["task_definition.yaml", "model_registry"],
    "confidence": 1.0,  // Symbolic = certain
    "user_approved": true
  }

EU AI Act Compliance

Implementation Stack

• Scallop: Generates complete provenance for every derived fact
• ArangoDB: Stores reasoning chains with timestamps and fact sources
• Natural Language Generation: Template-based predicate → sentence conversion
• Visualization: D3.js for proof trees, graph views, timelines