AI Research
New architectures, training methods, evaluation science and safety results — explained from the primary paper.
AI research is the work that pushes the field forward — new model architectures, training and post-training methods, evaluation science and safety results. This section explains the peer-reviewed papers and arXiv preprints that matter, distilling the result and why it is significant, and always linking the primary source so you can read the work yourself rather than take our word for it.
What we cover in AI Research
- Architecture and training advances — attention variants, mixture-of-experts, RLHF and successors.
- Evaluation and interpretability research that changes how we measure or understand models.
- Safety and alignment results, prioritizing reproducible work and independent validation.
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Frequently asked about AI Research
What kind of AI research does this hub cover?
We cover peer-reviewed and preprint research with practical significance — architectures, training, evaluation and safety — and link the primary paper for every claim.
How do you decide which papers to cover?
We prioritize research with reproducible results, independent validation or clear downstream impact, citing the source so readers can verify.
Where can I read the original papers?
Each article links the primary paper (arXiv, a journal or a lab publication) directly.