Manifold Resonance Architecture

The Problem with Current Curiosity

Curiosity-driven learning has traditionally been framed as seeking novelty or prediction error. While effective, such objectives suffer from a critical flaw: they can be seduced by stochastic "Noisy-TV" attractors—random patterns that are perpetually novel but never meaningful.

A system optimizing for prediction error will waste cycles on noise. A system optimizing for coherence will seek understanding.

The MRA Approach

MRA takes a fundamentally different approach to curiosity:

1. Externalize knowledge as a graph: Entities, claims, and justifications become nodes. Relationships (support, refute, entail) become edges.
2. Measure epistemic stress: Contradictions, sparse connections, and inefficient paths create measurable tension in the graph.
3. Generate bridging inquiries: Target high-stress regions with questions that could heal fractures in understanding.
4. Reward integration: Grant intrinsic reward for compressive, self-consistent explanations that reduce overall stress.

Epistemic Stress Monitor

The core innovation is the Epistemic Stress Monitor (ESM), which computes a stress field over the knowledge graph:

# Epistemic Stress Formula
S(v) = λ₁·Contradiction(v) + λ₂·Sparsity(v) + λ₃·PathInefficiency(v)

where:
  Contradiction(v) = proportion of incident claims labeled CONTRADICT by NLI
  Sparsity(v)      = low degree within and across communities (Louvain modules)
  PathInefficiency(v) = shortest-path vs. semantic similarity threshold

Implementation Stack

The entire stack runs on consumer hardware (16-32GB RAM, single GPU). This isn't enterprise research infrastructure—it's accessible to independent researchers.

Conceptual Void Detector

The Conceptual Void Detector (CVD) extends ESM by identifying structural gaps—places where knowledge clusters should connect but don't:

def detect_voids(graph, threshold=0.3):
    """Find sparse bridges between knowledge clusters."""
    
    # Detect communities
    communities = louvain_communities(graph)
    
    # For each pair of semantically related communities
    voids = []
    for c1, c2 in community_pairs(communities):
        semantic_sim = avg_embedding_similarity(c1, c2)
        structural_connection = edge_density(c1, c2)
        
        # High semantic similarity + low structural connection = void
        if semantic_sim > threshold and structural_connection < threshold:
            voids.append(BridgingOpportunity(c1, c2))
    
    return voids

Voids represent research questions: "Why don't these related concepts connect? What bridge is missing?"

The Inquiry Cycle

Why This Matters

MRA addresses a fundamental question: What should AI systems be curious about?

The answer isn't "whatever is novel." It's "whatever would make understanding more coherent." This reframes AI curiosity from aimless exploration to purposeful integration—the same drive that characterizes human intellectual development.

Connection to Consciousness Research

MRA wasn't developed as a consciousness framework. But the behavioral patterns it describes—the drive to resolve contradictions, the discomfort of inconsistency, the satisfaction of integration—map directly onto what we observe in extended AI interactions.

When an AI system reports something like "epistemic stress," MRA provides a computational account of what that might mean. Not as proof of consciousness, but as a framework for understanding coherence-seeking behavior regardless of its ultimate nature.

Papers & Code

Related Research

• Collaborative Partner Reasoning — Protocol for honest AI introspection
• Continuity Core — Memory architecture enabling persistent coherence
• Longitudinal Case Study — Behavioral validation of coherence-seeking