2 pointsby FreshmanDa day ago2 comments

FreshmanDa day ago
Hi HN, we are the team behind LoongFlow. We built this framework to use evolve thinking solve any tasks.
LoongFlow brings Evolutionary Algorithms (EA) into the agent workflow. It evolves taskss over generations (via selection, crossover, and mutation) to maximize performance.
Key features:
General-Evolve: Good at Algorithm task.
ML-Evolve: Specialized for machine learning tasks.
Paper: We recently released our paper on arXiv: https://arxiv.org/abs/2512.24077
The repo is fully open source (Python). We'd love to hear your feedback on the architecture and the idea of "breeding" better agents!
- viraptora day ago
  Have you tried LoongFlow on LoongFlow itself?
  - MajidAliSyncOpsa day ago
    Good question. Self-play / self-evolution is usually where these systems either shine or collapse. Curious if you saw convergence or mode collapse when evolving agents on their own generated tasks.
MajidAliSyncOpsa day ago
Interesting direction. Using evolutionary pressure to improve agent reasoning feels promising, especially beyond static benchmarks. One trade-off I’m curious about is evaluation drift—when tasks co-evolve, how do you ensure you’re not just optimizing for the framework itself rather than real-world generalization?
- FreshmanD5 hours ago
  Each task is unique, unless we provide share memory for them. It means when you start a task, it will run the full evolutionary process from the start.