OpenAI's major results over the last 3 months came from scaling test-time compute with agent swarms. I've been building an open-source equivalent for the past year, and I'd love for you guys to try it.

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 co…

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]