LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm · HackerPapers discussions

LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm — mentioned by FreshmanD

Thu, 08 Jan 2026 11:46:14 GMT

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!

Paper and discussion history. A mention is not an endorsement.

LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm — mentioned by FreshmanD

Mon, 05 Jan 2026 07:24:54 GMT

Hi HN, we are the team behind LoongFlow. We built this framework to solve the problem of Agent brittleness—where standard ReAct agents often get stuck or fail when prompts aren't perfectly hand-tuned. Instead of manual prompt engineering, LoongFlow brings Evolutionary Algorithms (EA) into the agent workflow. It treats prompts and logic as "populations" that evolve over generations (via selection, crossover, and mutation) to maximize performance. Key features: General-Evolve: Auto-optimizes prompts and code logic. ML-Evolve: Specialized for machine learning tasks (AutoML agent). Paper: We…

Paper and discussion history. A mention is not an endorsement.