arXiv AI October 12, 2026 By Zipei Yu, Yue-Jiao Gong, Zeyuan Ma, Yuncheng Jiang, Zhiguang Cao

Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero

The paper introduces a dual‑agent framework that automates the evolution of Meta‑Black‑Box Optimization (MetaBBO) algorithms. By treating the design loop as a coding task, a task agent refines the MetaBBO codebase while a hyper agent continually modifies both agents, enabling recursive self‑improvement. The resulting evolved MetaBBO variants outperform current human‑crafted baselines and adapt quickly across different optimization domains, revealing emergent design principles.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

agents

arXiv AI

Oct 1

Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer

The paper introduces a self‑evolving harness framework where a frozen language‑model agent first solves tasks and then edits its own harness based on run records. Using a 49‑line seed harness, the evolved harness improves average scores on in‑distribution benchmarks by 4.48 points and on out‑of‑distribution benchmarks by 12.64 points, surpassing Codex on the former and matching it on the latter. Continued evolution on a specific out‑of‑distribution benchmark further raises performance, and the study analyzes emergent mechanisms such as output truncation and history compaction.

By Qiankai Xu

agents benchmarks

arXiv AI

Jul 7

SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe

arXiv:2607. 03451v1 Announce Type: cross Abstract: While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines.

By Yifei Shen, Bo Li, Xinjie Zhang

llms agents

arXiv AI

5d ago

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

The paper introduces AgenticBBO-Bench, a cross‑domain benchmark for evaluating large language model agents on black‑box optimization tasks such as synthetic functions, hyperparameter tuning, database tuning, chip design, and molecular design. Experiments show that agentic BBO outperforms direct LLM‑based methods in all five domains and beats the best numerical optimizers in four. The study also analyzes the impact of optimization tools, task semantics, prior knowledge, and the LLM’s role, finding that numerical tools are not consistently helpful, task semantics are broadly useful, and numerical optimizers can capitalize on agent‑generated search trajectories.

By Ming Chen, Rong-Xi Tan, Ke Xue, Yu-Jie Zhou, Taiye Lu, Zhi-Xuan Gao, Peng Xie, Zijun Shen, Chen Lu, Haopu Shang, Chao Qian

llms agents benchmarks

arXiv AI

Oct 1

Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

arXiv:2609.38757v1 Announce Type: new Abstract: Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorit...

By Chen Lu, Ke Xue, Siyuan Xu, Mingxuan Yuan, Chao Qian

llms agents efficiency benchmarks

arXiv AI

Jun 3

Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization

arXiv:2604. 17708v2 Announce Type: replace Abstract: Automating operations research (OR) with large language models (LLMs) remains limited by hand-crafted reasoning--execution workflows.

By Jiahao Huang, Peilan Xu, Xiaoya Nan, Wenjian Luo

llms agents benchmarks safety

arXiv Machine Learning

Jul 27

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.

By Shujin Wu, Cheng Qian, Xiusi Chen, Heng Ji

llms reinforcement-learning benchmarks

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Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer

SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization

Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization

Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning