cognitive training
the paradigm: a self-improvement loop where a model creates its own training tasks, keeps the ones worth creating, and learns from them
xent — a transparent
path to AGI
xent is an AI research lab: we improve the cognitive abilities of language models — transparently, toward artificial general intelligence
the idea is cognitive training: a model discovers relevant new skills by creating tasks for itself — with a principled way to measure which tasks are worth creating
done right, this turns a language model into a self-improving system that stays stable and competitive at the same time
what we build
one research program, three concrete parts
xent games
the task space: a surprisingly rich family of games a model can pose to itself and score using nothing but its own probabilities
frost
the algorithm: our reinforcement-learning method, exploiting the differentiable structure of xent games to learn faster than Monte-Carlo methods
how it works
instead of an ever-growing pile of handcrafted environments, cognitive training turns task discovery itself into a measurable loop:
- create — the model proposes a new , drawing on knowledge already implicit in its own distribution
- measure — the values the game by its transfer: how well it captures existing skills while opening genuinely new ones
- train — learns the worthwhile games efficiently
- repeat — the model returns to task creation stronger, and better at discovering what to learn next
counterfactual thinking — try it
one family of xent games asks: which piece of information would most change one's view? here is the human-scale version —
“a new language model improved by 40% on its main benchmark.”
which new fact should change your interpretation the most?
an illustration for visitors — the real games are played, and scored, by the model itself.
our research
the xent games — “Cross-Entropy Games for Language Models”
introduces the task space: games grounded in a model's own measures, from in-filling to counterfactual thinking
cognitive training — “Cognitive Training for Language Models”
formulates the self-improvement loop and derives the meta-objective that decides which games are worth creating
frost — “Cross-Entropy Games and Frost Training”
the training algorithm: exploiting differentiable game structure to move beyond the Monte-Carlo paradigm
notes — Clément's running notes on the xent games at xent.blog
the people building xent
Clément and Franck, with Arthur Jacot, received the 2026 ICBS Frontiers of Science Award for their work on the neural tangent kernel
contact — write to hello@xent.io
the territory
this page is one path through xent's ideas. the map holds all of them — thirteen concepts, connected the way the ideas actually are.