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

Key points

• Problem: Among LLM-driven code evolution frameworks, OpenEvolve relies on high-volume random mutations, and ShinkaEvolve gains efficiency through novelty search but has no mechanism to analyze why a mutation failed. As a result, search consumes many tokens, populations converge early to local optima, and the same errors repeat across generations.
• Input and output: It takes a task description, an initial solution program and a scoring evaluation function (for example, accuracy on test cases), edits the code over many generations, and returns the highest scoring solution. This is a framework paper, implemented and tested as a General Agent for algorithmic discovery and an ML Agent for machine learning pipeline optimization.
• Method: In the PES (Plan-Execute-Summarize) loop, the Planner follows the parent solution's lineage, reads earlier plans and summaries, and writes a blueprint (a natural language execution plan). The Executor writes code from the blueprint and fixes minor errors such as syntax typos itself before global evaluation, while the Summarizer compares the plan with the execution result and stores the insight in memory. The population keeps its diversity through ring-topology multi-island evolution, a MAP-Elites feature grid per island (keeping the best solution in each behavior-feature cell), and Boltzmann parent selection whose temperature changes with population entropy.
• Implementation: Experiments used math problems taken from the AlphaEvolve paper for the General Agent and Kaggle competitions from MLE-bench for the ML Agent, with models including DeepSeek-R1-0528, Gemini-3-Pro-Preview, Gemini-3.0-flash and Claude-Opus-4.5. Memory stores one Solution record per solution with its code, parent ID, generating plan, score, execution logs and a summary for the next generation. Per the official README, the release includes the PES framework (PESAgent), a ReAct agent, General, Math and ML agent examples, and a visualization tool for the evolution process.
• Results: On every problem in Table 1, LoongFlow with DeepSeek-R1 beat the AlphaEvolve value, for example 0.9027 against 0.8962 on Autocorrelation II and 2.6359 against 2.6358 on Circle Packing (Square). On MLE-bench the ML Agent earned 14 gold medals. With DeepSeek-R1-0528 and a 24-hour limit on Circle Packing (Square), LoongFlow reached the 0.99 target in all 3 runs with an average of 258 evaluations, OpenEvolve reached it in 1 of 3 runs with 783 evaluations, and ShinkaEvolve peaked at 0.952.
• Limits: The efficiency comparison comes from 3 runs on the single Circle Packing (Square) task, the ablation also uses the Circle Packing task, and Table 1 compares only against AlphaEvolve values. ML Agent results are reported as a list of 14 gold medal competitions, and baseline comparisons with OpenEvolve and ShinkaEvolve cover the General Agent only. The authors name self-configuring Meta-Agents and learned unsupervised diversity metrics for new domains as future work.

Paper links

External research summaries. These are not HDATF publications or measured product results.