AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery

• URL: http://arxiv.org/abs/2609.15820v1
• Date: Mon, 14 Sep 2026 16:24:27 GMT
• Title: AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
• Abstract summary: AlgoEvo transforms automated algorithm discovery into an interactive, knowledge-accumulating process.<n>A design skill hub decouples paradigm-specific knowledge from the core discovery engine.<n>A hierarchical experience mechanism organizes search trajectories into a task-level tree to guide exploration.
• Score: 25.96347096124607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and discards valuable execution feedback. We propose AlgoEvo, a unified agentic framework that transforms automated algorithm discovery into an interactive, knowledge-accumulating process. An autonomous agent dynamically inspects, diagnoses, and edits code based on runtime feedback. A design skill hub decouples paradigm-specific knowledge from the core discovery engine, allowing a single workflow to seamlessly handle single-objective, multi-objective, and multi-component design. Meanwhile, a hierarchical experience mechanism organizes search trajectories into a task-level tree to guide exploration and consolidates cross-task patterns into reusable skills. Across six representative benchmark tasks, AlgoEvo matches or surpasses specialized methods with substantially fewer evaluations and reduced token consumption, demonstrating strong intra-task accumulation, cross-task transfer, and the ability to reproduce or exceed existing state-of-the-art performance through flexible skill activation.

Related papers

• Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills [53.15216425100295]
  We introduce Skill Self-Play (Skill-SP), a co-evolutionary framework comprising a proposer, a solver, and a skill controller.<n>The proposer generates challenging tasks conditioned on dynamically sampled skills; the solver explores candidate solutions to push its capability boundaries; and the skill controller collects execution feedback to update and expand the skill library.<n>This interactive co-evolution effectively bridges the gap between structured verification and open-ended exploration.
  arXiv  Detail & Related papers  (2026-07-24T17:59:22Z)
• Coupled Hierarchical Search over Topology and Execution for Agentic Workflow Synthesis [64.2820121997882]
  We introduce HierFlow, a training-free, test-time hierarchical search architecture that automates agentic workflow design.<n> HierFlow maximizes efficiency through an intelligent gating module that selectively triggers execution-level searches based on contextual necessity.<n> Comprehensive testing across question answering, mathematical reasoning, and code generation benchmarks confirms that HierFlow consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2026-05-20T01:56:11Z)
• Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering [50.72456732323037]
  We propose textbfSwarm Skills, a portable specification that extends the Anthropic Skills standard with multi-agent semantics.<n>Swarm Skills turns multi-agent into first-class, distributable assets that consist of roles, execution bounds, and a built-in semantic structure for self-evolution.<n>We show how Swarm Skills achieves zero-adapter cross-agent portability via progressive disclosure, enabling agent teams to self-evolve their coordination strategies without framework lock-in.
  arXiv  Detail & Related papers  (2026-05-11T06:26:58Z)
• AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents [26.380991138110925]
  AutoAgent is a self-evolving multi-agent framework built on evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration.<n>Each agent maintains structured prompt-level cognition over tools, self-capabilities, peer expertise, and task knowledge.<n>AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines.
  arXiv  Detail & Related papers  (2026-03-10T14:23:49Z)
• Synthesizing Procedural Memory: Challenges and Architectures in Automated Workflow Generation [0.5599792629509229]
  This paper operationalizes the transition of Large Language Models from passive tool-users to active workflow architects.<n>We demonstrate that by enforcing a scientific methodology of hypothesize, probe, and code, agents can autonomously write robust, production-grade code skills.
  arXiv  Detail & Related papers  (2025-12-23T11:33:32Z)
• AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizing [1.2617078020344616]
  A novel agentic AI framework for sample-efficient and explainable analog circuit sizing is presented.<n>The AnaFlow framework is demonstrated for two circuits of varying complexity and is able to complete the sizing task fully automatically.<n>The inherent explainability makes this a powerful tool for analog design space exploration and a new paradigm in analog EDA.
  arXiv  Detail & Related papers  (2025-11-05T18:24:01Z)
• Variational Offline Multi-agent Skill Discovery [47.924414207796005]
  We propose two novel auto-encoder schemes to simultaneously capture subgroup- and temporal-level abstractions and form multi-agent skills.<n>Our method can be applied to offline multi-task data, and the discovered subgroup skills can be transferred across relevant tasks without retraining.<n> Empirical evaluations on StarCraft tasks indicate that our approach significantly outperforms existing hierarchical multi-agent reinforcement learning (MARL) methods.
  arXiv  Detail & Related papers  (2024-05-26T00:24:46Z)
• Dynamic Perceiver for Efficient Visual Recognition [87.08210214417309]
  We propose Dynamic Perceiver (Dyn-Perceiver) to decouple the feature extraction procedure and the early classification task. A feature branch serves to extract image features, while a classification branch processes a latent code assigned for classification tasks. Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
  arXiv  Detail & Related papers  (2023-06-20T03:00:22Z)
• Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving [100.3848723827869]
  We present an effective multi-task framework, VE-Prompt, which introduces visual exemplars via task-specific prompting. Specifically, we generate visual exemplars based on bounding boxes and color-based markers, which provide accurate visual appearances of target categories. We bridge transformer-based encoders and convolutional layers for efficient and accurate unified perception in autonomous driving.
  arXiv  Detail & Related papers  (2023-03-03T08:54:06Z)
• Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism [120.1998866178014]
  We present a flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTingAnism (ROSETTA) Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. Experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-05-06T07:31:28Z)

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.