TL;DR: This is a short summary of our paper Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings by Berkant Turan, Suhrab Asadulla, David Steinmann, Kristian Kersting, Wolfgang Stammer, and Sebastian Pokutta. The paper was accepted as a spotlight at ICML 2026 (top ~2.2% of submissions). In a nutshell, we move Prover-Verifier Games from pixel space to concept space: a prover selects a sparse set of human-readable concepts, a verifier must classify using only those concepts, and an adversarial prover tests whether the verifier can be fooled. The result is a scalable route to verifiable, nonlinear concept-based classification on datasets…

AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice....

Context priors plugin for pi . Contribute to pokutta/pi-prior-plugin development by creating an account on GitHub.

TL;DR: AI-driven scientific discovery with systems such as AlphaTensor, AlphaEvolve, etc is en vogue and we are also heavily invested in that space. However, these LLM- and RL-driven approaches, while impressive often in themselves, are not always the right tool to get the job done. In this post I will talk about three recent projects from our group that take different angles on LLM- and RL-driven mathematical discovery systems. Two of them revisit classical problems made famous through these AI systems and show that classical structured search matches or beats them on these specific problem classes; with a tiny fraction of the compute. The third example goes the other way around: AI not as discovery engine, but an AI-accelerated subroutine sits inside a classical structured search and lets us settle a thirty-year-old conjecture on real algebraic plane curves of degree seven. This points to an interesting shift in the recent AI vs. classical discussion: it is not so much about whether

TL;DR: SCIP Optimization Suite 10.0 brings a numerically exact solving mode for rational MILPs, noticeable performance gains for MILP/MINLP, stronger presolving and symmetry handling, better heuristics and conflict analysis, IIS detection, and major updates to GCG, PaPILO, PySCIPOpt, and MIP-DD.