Quantifying math interestingness with Lean: difficulty over description length predicts utility at ρ=0.756
RexDouglass · x · 2026-09-27
A Lean-based proposal defines an 'interestingness' metric for math propositions: proof difficulty divided by description length — easy to state, hard to prove. On mathlib the ranking is intuitive: trivial identities score low while Fermat's Last Theorem variants top the list. Strikingly, this metric predicts downstream utility with ρ = 0.756.
More from Research
- Sakana AI × Pathway Post-Transformers lecture on AI and the brain now online — kaixhin · 2026-11-11
- Defending METR: public data can't second-guess its AI speedup findings — peterwildeford · 2026-11-11
- Adversarial RL envs for backdoor insertion vs. detection: where does it converge? — DimitrisPapail · 2026-11-11
- DiffusionEngine: an open-source modular PyTorch DDPM framework seeking contributors — Euphoric-Incident-93 · 2026-11-11
- UniPath (NeurIPS 2026): adaptively coordinating understanding and generation in one model — jindong_wang92 · 2026-11-11
- Versor pitches real2sim2real: robotics sim needs physical dynamics, not just visuals — abhishekunique7 · 2026-11-11