Eleonora Giunchiglia

Generative models can now produce inorganic crystals with oxidation-state distributions mirroring real-world synthesis, significantly boosting discovery potential.

This paper introduces Magenta, a training-free agentic pipeline that, given only a natural-language problem, produces an answer, expresses it as a Lean 4 statement, and constructs a machine-checked proof, which achieves 100% accuracy across all evaluated olympiad benchmarks.

Pythagoras-Prover achieves state-of-the-art performance in formal proving with dramatically fewer parameters, challenging the notion that bigger models always yield better results.

Diffusion language models get a nearly 20% boost on MBPP by strategically planning the order in which they fill in the blanks.