America is banning AI in schools.
China is using AI to create geniuses.
Introducing Aristotle: The AI tutor that solves America’s broken education system. https://t.co/hqPJN8cke0
Introducing Aster Inference -- The world's fastest inference API created by AI research agents
We serve the world's fastest inference on GPU:
- OpenAI's gpt-oss-120b @ 644 tps
- https://t.co/vu7snkRF0D's GLM 5.2 @ 281 tps
At Aster, we're automating open-ended research, and we use inference optimization as a task to benchmark our agents against. We're creating a product out of the inference discoveries made from our system.
As our agents discover more, we plan to further improve our inference product and ship new, SOTA AI products.
Two papers accepted at the #ICML2026 workshop on the Impact of Memorization on Trustworthy Foundation Models (MemFM), and I'll be presenting both today!
Scale Dependent Data Duplication: (our first debut of this paper), led by @JoshuaK92829 and Noam Levi.
Internal Data Repetition Destroys Language Models: in case you missed it yesterday.
📍Hall A, 3:50-5:00pm (Jul 11)
Hope to see you there!
Pass@k and self-consistency work great for math and code; sample more and verify. So we asked: can the same trick scale truthfulness in domains with no verifier? The answer was no.
Excited to share our #ICML2026 conference paper: Truthfulness Does Not Scale Like Reasoning. https://t.co/RCzcFEPAer. I’ll be at ICML in Seoul to present it!
Co-led by @JoshuaK92829 and @yegordb
Flying to #ICML2026 to present Internal Data Repetition Destroys Language Models, an Oral at Foundations of Deep Gen Models Workshop!
Paper: https://t.co/VyLfnivvUT
You might be curious to know what we mean by “destroys”! Pretraining is now data-constrained, and even aggressively deduplicated corpora keep some repetition. We measured what that repetition actually costs in the currency practitioners care about: compute. The answer, in the worst case, is a third of your FLOPs.