MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning
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Updated
Feb 2, 2026 - Python
MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning
Reproducible and flexible LLM evaluations for scientific reasoning.
Foundational doctrine establishing the principles behind the Neurotransparency governance framework.
BioReasoner: Training LLMs for grounded scientific reasoning. 0% hallucination rate on citations, 100% format adherence. Cross-domain polymathic insights via Scientific Tribunal evaluation.
Self-reorganizing knowledge structures via truth pressure. Formalizes scientific paradigm shifts computationally — old knowledge is contextualized, not deleted. Three provable invariants. Validated on miasma→germ theory and classical→quantum physics.
Framework for building and evaluating explanatory accounts of evidence using Bayesian network inference. Includes interactive Shiny app.
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