arXiv AI October 19, 2026 By Jian Yao, Zhiyao Zhang, Yichen Li, Xingyu Wu, Kay Chen Tan

Algorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence

The paper introduces Algorithm Modelization, a paradigm that transforms iterative optimization algorithms into trainable models by treating algorithm execution trajectories as computational experience. It models optimization as an autoregressive process, using a dimension‑independent population‑temporal architecture to capture population interactions and iterative dynamics. The resulting model learns from heterogeneous teachers, improves through optimization feedback, and generalizes across problems and larger dimensions, enabling algorithmic capability to accumulate and evolve via learning.

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.

robotics

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

arXiv AI

Jul 20

Recursive Harness Self-Improvement

arXiv:2607. 15524v1 Announce Type: cross Abstract: Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models.

By Hyunin Lee, Jinglue Xu, Jeffrey Seely, Donghyun Lee, Matei Zaharia, Yujin Tang

agents robotics

arXiv AI

Jun 6

MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

arXiv:2606. 06473v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution becomes a key capability.

By Shangheng Du, Xiangchao Yan, Jinxin Shi, Zongsheng Cao, Shiyang Feng, Zichen Liang, Boyuan Sun, Tianshuo Peng, Yifan Zhou, Xin Li, Jie Zhou, Liang He, Bo Zhang, Lei Bai

llms rag agents benchmarks

arXiv AI

Oct 16

MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering

MLExpert is a 35B Mixture-of-Experts agentic model designed to autonomously perform long‑horizon machine learning engineering (MLE) tasks. It uses a post‑training method that combines hierarchical agentic supervised fine‑tuning with Windowed Interaction RL to learn from intermediate outcomes in complex experimentation workflows. On the MLE‑Bench Lite benchmark, MLExpert raises the average medal rate from 37.9% to 72.7%, matching the performance of larger frontier models and demonstrating generalizable optimization capabilities across related tasks.

By Shangheng Du, Jinxin Shi, Shuaiyu Zhang, Yiming Wu, Junpeng Zhao, Shiyang Feng, Xin Li, Liang He, Xiangchao Yan, Bo Zhang

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 30

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?

By Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

llms agents reinforcement-learning fine-tuning 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

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