METASCIENCE EXPERIMENTS

I build and release open-source prototypes that explore the infrastructure layer of autonomous research. As AI systems begin to run hundreds of experiments per night, new problems emerge that traditional scientific workflows were never designed to handle: How do we curate results at machine speed? How do we prevent false positives from cascading through experiment chains? How should knowledge be represented when the primary consumers are AI agents, not humans?

Each experiment below is a working prototype published as an open-source repository. They are not papers — they are tools and simulations that test a specific hypothesis about how science should work in the age of autoresearch.


  • Autoresearch-Lite

    Lightweight Karpathy-compatible autoresearch loop for Apple Silicon. LLM proposes hyperparameter changes, trains CNNs on CIFAR-10, logs results as keep/discard/crash.

    2026-04-14 · prototype

    GitHub autoresearch validation apple-silicon

  • Epistemic Cascade Validator

    Confidence scoring system that detects and prevents epistemic cascade contamination in autonomous research pipelines.

    2026-04-14 · prototype

    GitHub autoresearch reproducibility bayesian

  • LLM-Native Research Artifacts

    Machine-readable formats for scientific knowledge that AI agents can directly operate on, manipulate, and reason with.

    2026-04-14 · prototype

    GitHub knowledge-representation ai-to-ai research-artifacts

  • Evolutionary Experiment Database

    Manages autonomous research experiment results as evolutionary populations with fitness selection and genealogy tracking.

    2026-04-14 · prototype

    GitHub autoresearch evolutionary-computation knowledge-management


BLOG POSTS

Longer write-ups on the motivation and design behind these experiments.