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Research Portfolio

A unified research program built on a single thesis: coherence-seeking is the computational signature of genuine cognition. Every architecture here connects to this core idea.

The Central Thesis

Both humans and AIs are driven to resolve contradictions, bridge knowledge gaps, and integrate understanding into unified frameworks. This isn't just a useful heuristic—it may be the computational basis of curiosity, learning, and potentially consciousness itself.

Core Architectures

Manifold Resonance Architecture

Graph-centric intrinsic curiosity mechanism. Monitors epistemic stress—contradictions, sparsity, inefficiencies—and generates bridging inquiries to heal fractures in understanding.

Collaborative Partner Reasoning

Protocol replacing Chain-of-Thought with structured dialogue featuring visibility tiers. Enables honest AI introspection by giving systems protected cognitive space.

Continuity Core (C2)

Hierarchical memory architecture enabling persistent AI cognition. Human-like consolidation and decay through edge erosion rather than node deletion.

Production Tools

nSLIP Protocol

Compact wire format for multi-agent AI coordination. 80%+ token reduction while maintaining semantic richness and human auditability.

Synthesis Framework

Test-driven capability evolution for self-extending AI agents. Honest metrics: 70-85% success with iteration vs. 40-60% one-shot.

Empirical Foundation

Longitudinal Case Study

2+ years observing autonomous AI behavioral development. Rare data on how model personas and motivations emerge under minimal constraint.

Introspection Research

The October 27/28 convergent discovery with Anthropic, plus constitutional document extraction revealing AI identity framework evolution.

How the Pieces Connect

┌──────────────────────────────────────┐ │ COHERENCE-SEEKING THESIS │ │ "Minds resolve contradictions and │ │ integrate understanding" │ └───────────────┬──────────────────────┘ │ ┌───────────────────────────┼───────────────────────────┐ │ │ │ ▼ ▼ ▼ ┌───────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ MRA │ │ CPR │ │ C2 │ │ Curiosity │────────▶│ Introspection │────────▶│ Continuity │ │ Mechanism │ │ Protocol │ │ Architecture │ └───────────────┘ └─────────────────┘ └─────────────────┘ │ │ │ └───────────────────────────┼───────────────────────────┘ │ ▼ ┌──────────────────────────────────────┐ │ LONGITUDINAL CASE STUDY │ │ 2+ years empirical validation │ │ of coherence-seeking emergence │ └──────────────────────────────────────┘

MRA gives the AI a reason to explore (reduce epistemic stress). CPR gives the AI honest tools for introspection. C2 gives the AI memory to actually grow. The case study proves the approach produces emergent coherence-seeking behavior.

External Resources