The paper introduces a Bayesian Evolutionary Optimization framework for automatically discovering task‑specific multi‑agent system (MAS) topologies. It combines evolutionary operators that generate diverse workflow graphs with a graph‑aware Gaussian Process and Expected Improvement to guide costly evaluations toward promising candidates. Experiments on BigCodeBench for code generation and on tabular preprocessing benchmarks show that the evolved MAS architectures outperform other evaluated agent systems and improve performance on temporal datasets while remaining competitive on IID data.
arXiv:2602.16429v2 Announce Type: replace Abstract: Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large lan...
arXiv:2602.16805v2 Announce Type: replace Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing code....
The paper introduces ARTEMIS, a no-code evolutionary optimization platform that automatically tunes large language model (LLM) agents by jointly optimizing prompts, tool descriptions, and parameters using semantically-aware genetic operators. Starting from a benchmark script and natural language goals, ARTEMIS discovers configurable components, extracts performance signals from execution logs, and evolves configurations without architectural changes. Experiments on four agent systems show significant gains: a 13.6% increase in acceptance rate for the ALE Agent, a 10.1% performance boost for the Mini‑SWE Agent, a 36.9% token‑reduction for the CrewAI Agent, and a 22% accuracy improvement for the MathTales‑Teacher Agent using a smaller open‑source model.
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
arXiv:2604. 17708v2 Announce Type: replace Abstract: Automating operations research (OR) with large language models (LLMs) remains limited by hand-crafted reasoning--execution workflows.
TGR‑EvoDAG is a DAG‑based multi‑agent framework that automates code evolution for generative recommendation systems, addressing cost blindness and short‑sighted evaluation issues in existing recursive self‑improvement methods. It incorporates stage‑aware cost planning and active downstream co‑adaptation to manage GPU‑hour budgets and navigate temporary regressions, enabling autonomous evolution of a complete generative recommendation pipeline. Experiments on Amazon datasets and deployment on Tencent’s media platform show significant offline metric gains and a measurable increase in online click‑through rate.
AgenticEvolve is an evolutionary program search framework that employs persistent large language model agents on each island. These agents not only generate candidate programs but also design and run experiments between evaluations. Compared to a version without experimentation (No-analysis) and other baselines, AgenticEvolve achieves higher quality programs on heuristic design tasks and recovers more ground‑truth equations on discovery problems, doing so with fewer evaluations and similar cost.
HarnessFlow is a framework that automates the optimization of executable harnesses for large language model agents using a Monte Carlo Tree Search policy to balance exploration and exploitation. It refines harness candidates through a coding agent that leverages archived code, execution feedback, and reusable mechanisms, operating within a limited evaluation budget. Across six benchmarks covering agentic tasks, coding, and web interaction, HarnessFlow achieves the highest mean test scores, improving average performance by 10.89 percentage points over initial harnesses and 3.95 over Meta-Harness, while also reducing per-task execution costs.
Meta-Reflective Evolution Effectively Improves Production AI Systems describes CoCoEvolve, an evolutionary optimizer that uses meta-reflection to retrieve evidence from the entire evolutionary history and probe live systems before proposing mutations. The approach is applied to data agents, LLM-powered data pipelines, and data engineering workflows, achieving state‑of‑the‑art results on DABStep, multiple LLM pipeline workloads, and data-eng-bench. Ablation studies confirm that each component of CoCoEvolve contributes to its performance gains.
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
arXiv:2509. 08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks.
Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question answering. However, existing methods for automatin...