Persistent-memory denoiser hits 99.9% on Sudoku-Extreme with under 250k parameters
LucaAmb · x · 2026-09-12
Researchers added a persistent memory state and removed timestep conditioning from a denoising model, yielding an anytime iterative solver. Since classic sudoku solvers need memory to track possible states, persistent memory between denoising steps proves key: with fewer than 250k parameters the model scores 99.9% on Sudoku-Extreme and 98.3% on Maze-Unique — a new take on reasoning with atypical diffusion models.
Related event: Denoiser with Persistent Memory Solves Sudoku-Extreme at 99.9%(2 posts)→
More from Research
- ParetoRSI-Gym benchmarks whether AI agents can recursively self-improve LLMs across domains — Haojin Wang · 2026-10-28
- Stanford's Ψ-0.6 is a unified vision-language world model with adaptive reasoning streams — Stanford · 2026-10-28
- Shanghai AI Lab's OpenESM shows energy-steered models can scale as foundation learners — Shanghai-AI-Laboratory · 2026-10-28
- CellTrack-Bench tests VLMs on microscopic cell tracking, exposing reasoning gaps — biohub · 2026-10-28
- Hybrid LLM agent scores 83.2%, beats nine VLA policies on new SO-101 robot arm benchmark — Truman Hickok · 2026-10-28
- DeepMind researcher: all of mathematics will soon be formalizable, with agents hunting proofs — ChrSzegedy · 2026-10-28