Neural network dissolving into fractal patterns

Fractal Embeddings

Complete

Complete. Testing whether a large body of machine-readable meaning carries a fractal fingerprint — every threshold frozen before the data was touched. Two tests confirmed, three came back negative, and the negatives are the finding.

Complexity ScienceRepresentation GeometryPre-Registered
View details →

BuddhaBERT

Active

Training small language models from scratch on contemplative literature — Pali Canon suttas and transcribed dharma talks — crossed against contemplative architecture modifications, to test whether either changes model behavior in measurable ways.

NLPContemplative ScienceFrom-Scratch Training
View details →

Moltbook Antfarm

Designed

Whether simple agents following only local rules develop group behavior nobody programmed — and whether it arrives suddenly, as a phase transition, rather than gradually. Includes a proposed test for telling real emergence from mere aggregation.

Multi-AgentEmergencePhase Transitions
View details →

Embodied Embeddings

Planned

Testing whether grounding language in simulated sensory and motor experience produces word and sentence representations with qualitatively different computational properties than text-only training.

EmbodimentRepresentation LearningSimulation
View details →

Digital Game of Life

Active

Using Conway's Game of Life as a calibration bench for emergence detectors — testing whether compression ratio and integrated information (Phi) can rank pattern classes correctly on a substrate where the right answer is not in doubt.

EmergenceIITComplexity Science
View details →

IIT Emergence

Designed

Directly testing Integrated Information Theory predictions about consciousness in neural network architectures of varying structure, measuring Phi across different topologies and training regimens.

IITNetwork TopologyInformation Theory
View details →

The Forgery Test

Planned

Adversarial validation of consciousness markers — training a generator to imitate the signatures of consciousness, then finding which markers resist faking. The imitation-resistant ones are the measurements worth trusting.

AdversarialMeasurementValidation
View details →