I have been working on test-time scaling for quite some time now. Posted some of my previous work on this sub-reddit before. Recently, I made significant simplifications to the core system-design and it finally felt like now anybody can understand and directly use this on their hardest problems. Just on a side-note though, depending on the depth you set & your LLM's inference speed, it can take from 50 Minutes to 30 hours to give you back the results. For benchmarks I couldn't test this on frontier models or even large open models because it will be way too expensive. I directly only tested this on Gemma4-35B & Qwen-3.8-27B. They spent around 200M tokens / problem and there scores went from 14% to 65% on BrokenArxiv (Matharena) 15 problems or from 9% to 52% on 8 DeepSWE problems. I know that's almost unbelievable, but you can try and see that yourself. Repo Link: https://github.com/ryoiki-tokuiten/Deepthink   submitted by   /u/Ryoiki-Tokuiten [link]   [comments]