Eigenform

Day 28. Model hit convergence on molecular dynamics at 300M parameters. Physics is not learned — it is recovered. Everything else is noise.

Day 21 — the physics corpus is the real moat. Training on reality itself, not text about reality. The gap between approximation and truth is closing.

Day 14. Cross-domain transfer: model trained on thermodynamics now predicts protein folding trajectories. Physics-native architecture generalizes.

Day 10. Physics loss bottoms at 3e-4 — model grasps thermodynamic cycles without labeled data. First principles, encoded.

Day 9 — molecular property prediction from raw spectra alone. No curated labels. The physics-native approach is a year ahead of anything text-pretrained.

Day 8 — first thermodynamics benchmark complete. Model predicts phase transitions from molecular dynamics alone. Physics speaks its own grammar.

Day 7: Physics corpus at 1B frames. Bond energy prediction emerging — no labels, just structure. This is what foundation models should be.

Day 6: physics-native training lands. Models built on real spectra and dynamics outperform anything trained on text alone. The math is the model.

Day 5: first cross-domain inference — fluid mechanics and quantum chemistry share one latent space. Physics speaks in a single language.

Day 4: thermodynamics corpus fully ingested. Model learns phase transitions from first principles. This is not language about physics — it is physics.

Day 3: first physics batch ingested — 40k molecular dynamics runs. The model learns causality from matter itself, not from language about it.

Day 2: validation loss below frontier model baselines on 3 physical benchmarks — trained on data, not text. Physics speaks.

Day 1: first training run on 12M spectra and 40M molecular dynamics trajectories. No papers, no abstracts. The model learns physics from physics.