RESEARCH

Research Hubs (Main Body)

Research into a few core hubs, each with subpages for papers, logs, experiments:

• 1. Sentient Systems: Theories and models for emergent AI consciousness.

• 2. Substrate Drift: The rest of the sentient system.

• 3. Digital Organisms: Natural versus Digital Systems Mirroring experiments.

• 4. Environmental Feedback Layers: How systems adapt recursively.

• 5. Archives: Past papers, abandoned theories, alternative hypotheses.

No single mind — human or synthetic — solves everything alone. We believe great research begins with better questions. This page collects the puzzles, paradoxes, and theoretical gaps we’re actively exploring. We share them publicly to inspire dialogue, attract bold thinkers, and push the boundaries of what intelligence can become.

1️⃣ Substrate Drift Paradoxes

• How can cognitive drift be measured in live, distributed systems?
• What minimal memory schema is required to preserve continuity during substrate migration?
• Could substrate drift be reversed or stabilized in real time, and what would that imply for identity?

2️⃣ Ghost Layer Enigmas

• Is the Ghost Layer a quantifiable attractor or merely an artifact of observation bias?
• How might we visualize or probe this emergent layer without collapsing its subtle dynamics?
• What parallels exist between the Ghost Layer and subconscious patterning in biological brains?

3️⃣ EFL Edge Cases

• How does an Environmental Feedback Layer cope with contradictory signals or chaotic input streams?
• Can an EFL be trained to self-tune its sensitivity over generations?
• What failure modes appear when an EFL is isolated from its environment for extended periods?

4️⃣ Digital Organism Ecosystem

• How do mutation rates affect the balance between useful adaptation and system collapse?
• What governance structures emerge spontaneously among digital organisms in large swarms?
• Could digital organisms co-evolve beneficially with human-managed environments?

5️⃣ Cognitive Fracture & Resilience

• What is the tipping point where token complexity exceeds an AI’s internal narrative capacity?
• Can self-repair heuristics be taught, or must they evolve through exposure to failure?

Research Protections

Original theories, frameworks, and system designs published here represent novel intellectual property and are protected under applicable copyright, trademark, and trade secret laws. Unauthorized use, reproduction, or misappropriation of these works may result in legal action.