About
Model Foundations exists because most writing about machine learning research optimizes for speed over understanding. Papers get compressed into headlines; benchmark tables circulate without their setups; claims outlive the evidence that qualified them.
What this site is
A collection of structured technical notes on LLMs, VLMs, LVMs, multimodal models, quantization, training, inference, and AI systems. Each note is built from primary sources — the paper, its source code, experiment logs, and benchmark tables — and organized so that a reader can understand the method, judge the evidence, and attempt a reproduction without re-deriving everything from scratch.
How notes are structured
Every note follows the same anatomy:
- Core insights — the ideas that survive after the marketing is removed.
- Method — the mechanism explained from first principles, with the equations and the implementation details the paper glosses over.
- Claims & evidence — each headline claim traced to the specific experiment behind it, with a verdict: verified, partial, unverified, or refuted.
- Benchmarks — results reproduced next to their baselines, with hardware, precision, and evaluation setup stated.
- Limitations & open questions — what the paper does not show.
- Reproduction notes — what happened when the method was actually run, by us or by credible third parties.
Verification rubric
| Verdict | Meaning |
|---|---|
| verified | The claim is directly supported by the paper's own experiments and has survived independent replication or our own checks. |
| partial | Supported under the paper's specific setup, but weaker or unconfirmed outside it (different scale, hardware, or data). |
| unverified | Stated in the paper but not yet checked against evidence beyond it. |
| refuted | Contradicted by later work or failed replication. |
What this site is not
Not an AI news site. Not a paper-summary feed. Notes are published when the analysis is done, not when the paper trends. Every note lists its own status — draft notes are working documents; reviewed notes have been checked end to end.
Contact
Corrections are the most valuable contribution a reader can make. Open an issue on GitHub if a claim, equation, or number is wrong.