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

• URL: http://arxiv.org/abs/2512.24077v1
• Date: Tue, 30 Dec 2025 08:39:28 GMT
• Title: LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm
• Abstract summary: LoongFlow is a self-evolving agent framework that achieves state-of-the-art solution quality with significantly reduced computational costs.<n>Unlike "blind" mutation operators, LoongFlow integrates Large Language Models into a cognitive "Plan-Execute-Summarize" (PES) paradigm.<n>To sustain long-term architectural coherence, we incorporate a hybrid evolutionary memory system.
• Score: 8.050281821865978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transition from static Large Language Models (LLMs) to self-improving agents is hindered by the lack of structured reasoning in traditional evolutionary approaches. Existing methods often struggle with premature convergence and inefficient exploration in high-dimensional code spaces. To address these challenges, we introduce LoongFlow, a self-evolving agent framework that achieves state-of-the-art solution quality with significantly reduced computational costs. Unlike "blind" mutation operators, LoongFlow integrates LLMs into a cognitive "Plan-Execute-Summarize" (PES) paradigm, effectively mapping the evolutionary search to a reasoning-heavy process. To sustain long-term architectural coherence, we incorporate a hybrid evolutionary memory system. By synergizing Multi-Island models with MAP-Elites and adaptive Boltzmann selection, this system theoretically balances the exploration-exploitation trade-off, maintaining diverse behavioral niches to prevent optimization stagnation. We instantiate LoongFlow with a General Agent for algorithmic discovery and an ML Agent for pipeline optimization. Extensive evaluations on the AlphaEvolve benchmark and Kaggle competitions demonstrate that LoongFlow outperforms leading baselines (e.g., OpenEvolve, ShinkaEvolve) by up to 60% in evolutionary efficiency while discovering superior solutions. LoongFlow marks a substantial step forward in autonomous scientific discovery, enabling the generation of expert-level solutions with reduced computational overhead.

Related papers

• MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation [51.824145170051516]
  MobEvolve is a self-evolving framework for human mobility generation.<n>It employs an agent to iteratively evolve its internal logic.<n>It significantly outperforms state-of-the-art deep generative and LLM-based methods.
  arXiv  Detail & Related papers  (2026-06-01T03:46:25Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery [67.47220507982522]
  Large language model (LLM)-based evolution is a promising approach for openended discovery.<n>We present CORAL, the first framework for autonomous multi-agent evolution on open-ended problems.<n>CORAL replaces rigid control with long-running agents that explore, reflect, and collaborate.
  arXiv  Detail & Related papers  (2026-04-02T05:59:06Z)
• FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration [27.617408084179246]
  FlowPIE treats literature exploration and idea generation as a co-evolving process.<n>We show that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity.
  arXiv  Detail & Related papers  (2026-03-31T10:37:47Z)
• Advancing Automated Algorithm Design via Evolutionary Stagewise Design with LLMs [69.91297564489464]
  EvoStage is a novel evolutionary paradigm that bridges the gap between the rigorous demands of industrial-scale algorithm design and the LLM-based algorithm design methods.<n>EvoStage decomposes the algorithm design process into sequential, manageable stages and integrates real-time intermediate feedback to iteratively refine algorithm design directions.<n>When deployed on a commercial-grade 3D chip placement tool, EvoStage significantly surpasses the original performance metrics, achieving record-breaking efficiency.
  arXiv  Detail & Related papers  (2026-03-09T05:13:44Z)
• AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization [61.535567824938205]
  We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem.<n>AdaEvolve consistently outperforms the open-ended baselines across 185 different open-ended optimization problems.
  arXiv  Detail & Related papers  (2026-02-23T18:45:31Z)
• K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model [57.440609834690385]
  Existing approaches treat Large Language Models (LLMs) as rapid code generators within evolutionary loops.<n>We propose Search via Co-Evolving World Model and build K-Search based on this method.<n>We evaluate K-Search on diverse, complex kernels FlashInfer, including GQA, MLA, and MoE kernels.
  arXiv  Detail & Related papers  (2026-02-22T11:06:22Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs [16.59846708454225]
  We propose a novel multi-agent reasoning framework, referred to as Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs (PathWise)<n>PathWise formulates a sequential decision process over an entailment graph serving as a compact, stateful memory of the search trajectory.<n> Experiments across diverse COPs show that PathWise converges faster to better generalizes, generalizes across different LLM backbones, and scales to larger problem sizes.
  arXiv  Detail & Related papers  (2026-01-28T12:34:50Z)
• Large Language Model-Powered Evolutionary Code Optimization on a Phylogenetic Tree [17.08113692977552]
  PhyloEvolve is a system that reframes GPU-oriented algorithm optimization as an In-Context Reinforcement Learning problem.<n>We introduce a phylogenetic tree representation that captures inheritance, divergence, and recombination among algorithm variants.<n>We evaluate PhyloEvolve on scientific computing workloads including PDE solvers, manifold learning, and spectral graph algorithms.
  arXiv  Detail & Related papers  (2026-01-20T22:32:52Z)
• PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution [64.15555230987222]
  PACEvolve is a framework designed to robustly govern the agent's context and search dynamics.<n>We demonstrate that PACEvolve provides a systematic path to consistent, long-horizon self-improvement.
  arXiv  Detail & Related papers  (2026-01-15T18:25:23Z)
• Controlled Self-Evolution for Algorithmic Code Optimization [33.82967000330864]
  Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles.<n>Existing approaches fail to discover solutions with superior complexity within limited budgets.<n>We propose Controlled Self-Evolution (CSE), which consists of three key components.
  arXiv  Detail & Related papers  (2026-01-12T09:23:13Z)
• Learning to Evolve with Convergence Guarantee via Neural Unrolling [37.99564850768798]
  We introduce Learning to Evolve (L2E), a unified bilevel meta-optimization framework.<n>L2E reformulates evolutionary search as a Neural Unrolling process grounded in Krasnosel'skii-Mann (KM) fixed-point theory.<n>Experiments demonstrate the scalability of L2E in high-dimensional spaces and its robust zero-shot generalization across synthetic and real-world control tasks.
  arXiv  Detail & Related papers  (2025-12-12T10:46:25Z)
• Experience-Guided Reflective Co-Evolution of Prompts and Heuristics for Automatic Algorithm Design [124.54166764570972]
  Combinatorial optimization problems are traditionally tackled with handcrafted algorithms.<n>Recent progress has highlighted the potential of automatics design powered by large language models.<n>We propose the Experience-Evolution Reflective Co-Guided of Prompt and Heuristics (EvoPH) for automatic algorithm design.
  arXiv  Detail & Related papers  (2025-09-29T09:24:09Z)
• HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution [13.440964262446558]
  Hierarchical Variable Agent (HiVA) is a novel framework modeling agentic as self-organized graphs with the Semantic-Topological Evolution (STEV) algorithm.<n> Experiments on dialogue, coding, Longcontext Q&A, mathematical, and agentic benchmarks demonstrate improvements of 5-10% in task accuracy and enhanced resource efficiency.
  arXiv  Detail & Related papers  (2025-08-29T18:51:18Z)
• Latent Bayesian Optimization via Autoregressive Normalizing Flows [17.063294409131238]
  We propose a Normalizing Flow-based Bayesian Optimization (NF-BO) to solve the value discrepancy problem.<n>Our method demonstrates superior performance in molecule generation tasks, significantly outperforming both traditional and recent LBO approaches.
  arXiv  Detail & Related papers  (2025-04-21T06:36:09Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization [22.374325061635112]
  This work proposes for the first time a diffusion model that can learn to perform evolutionary multi-objective search, called EmoDM. EmoDM can generate a set of non-dominated solutions for a new MOP by means of its reverse diffusion without further evolutionary search. Experimental results demonstrate the competitiveness of EmoDM in terms of both the search performance and computational efficiency.
  arXiv  Detail & Related papers  (2024-01-29T07:41:44Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.