3 quotes filed under model / capabilities, newest first.
Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.
Introducing System One Models & Jev · hn · Diogo Almeida · 15 September 2026
HN discussion
The comparison between LLM speed and Jev speed is misleading, because they're using autoregression to generate all of the type names, all of the schema, etc. A closer comparison would be if the LLM was purely outputting the raw numbers. Even then, comparisons to LLMs are pointless because you could train a transformer on the same sort of task that Jev is doing and get even better performance yet again, and a smaller model. I suspect this is some form of stripped down diffusion language model.
This question is related to the general phenomenon of the spikiness of AI capabilities, meaning that it is already superhuman in some areas, but still lacks something needed to replace human intellectual labor on a massive scale. I have the impression that the most significant thing which is missing is a sort of ability to exercise judgements.
A somewhat optimistic view of AI in mathematics · Robert Wegner · 10 September 2026
For humans, cleverness and intelligence are correlated; for machines they are largely decoupled, and today's tools are best viewed as stochastic generators of sometimes-clever, often-useful outputs — which yields the characteristic "useful yet unsatisfying" feeling of a magic trick once explained. He resists a one-dimensional picture entirely: the space of cognitive tasks is extremely high-dimensional, so no "sub-human to super-human" scale captures it, and the frontier that still eludes machines is data-scarce reasoning — extrapolating from five or six facts and a vague analogy, exactly where human mathematicians excel.
Terence Tao on AI in mathematics (and beyond) · Claude (reviewed and corrected by Terence Tao)