u/Mobile-Pumpkin7944

This algorithm tries to reduce exponential branching in a boolean constraint satisfaction problem, while staying within these tight constraints: Constant memory. guaranteed complete. no randomness. no restarts.

Dataset 1: SAT instances SATLIB uf20, uf50, uf75, uf100

Dataset 2: UNSAT instancs uuf50 & uuf75

Of course this is not beating any established algorithms, but that is not the goal here. Hence the chosen constraints.

I am very protective of the algorithm, but i hope numbers can enable you to analyze this with me. Can share more if needed.

Any serious interest is appreciated. Or point me to a better avenue.

My goal is to identify the invariant at play here. I see that the clause density has a relationship to the decision count. Harder problems get solved faster than loosely constrained ones. Aiming to test this next.

Thanks, and have a nice day!

This algorithm tries to reduce exponential branching in a boolean constraint satisfaction problem, while staying within these tight constraints: Constant memory. guaranteed complete. no heuristics. no randomness. no restarts.

Dataset: SATLIB uf20, uf50, uf75, uf100

Of course this is not beating any established algorithms, but that is not the goal here. Hence the chosen constraints.

Want to have a conversation on this. With a human. If anyone interested.

Update: And yes, Qwen3.8-27B is coming too. Next week!

2.4-trillion-parameter MoE flagship delivering a comprehensive leap in coding and professional work. Autonomously codes and delivers complete projects spanning 10+ days.

Open weights coming soon!

It is live at https://www.qwencloud.com/models/qwen3.8-max