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
Automatic Heuristic Design (AHD) has emerged as a transformative approach for solving combinatorial optimization problems. While recent Large Language Model (LLM)-based methods have shown promise, they predominantly rely on fixed evolutionary operators and struggle to effectively accumulate and reuse historical search experience.
arXiv:2610.15150v1 Announce Type: new
Abstract: Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback....
By Weichen Yu, Lesly Miculicich, Bhavana Dalvi Mishra, Jiefeng Chen, Shuo Li, Matt Fredrikson, Tomas Pfister, Long T. Le
HGM-Dual is a diagnostic‑guided framework that improves harness optimization for planning agents by representing agent evolution as lineage‑aware context and using error‑conditioned dual‑path child generation. It records ancestor changes, behavior outcomes, and ineffective modifications, enabling explicit edit‑outcome attribution. The method achieves up to 14.45% relative improvement in case accuracy while using 2×–4× fewer evaluation budgets compared to the strongest baseline.
By Sudipta Paul, Pengrui Quan, Aounon Kumar, Nicholas Tzou, Vivek Kulkarni, Srinivas Chappidi
The paper introduces EMO-STA, a two‑stage evolutionary multi‑task optimization framework for large‑language‑model (LLM) guided program discovery. First, it evolves a shared archive of executable programs across a family of related tasks; then it adapts selected shared candidates to each target task using strategies such as warm‑starting, best‑average, or best‑per‑task adaptation. Experiments on eight diverse task families show that EMO‑STA outperforms single‑task evolution under matched budgets, with balanced shared and adaptation budgets yielding the best results and shared evolution helping to reduce overfitting in low‑evidence scenarios.
By Halil Alperen Gozeten, Xuechen Zhang, Emrullah Ildiz, Ege Onur Taga, Tara Javidi, Samet Oymak
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
arXiv:2609.05992v1 Announce Type: new
Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task complet...
By Hengle Jiang, Qijun Cai, Ziying Luo, Ke Tang
OptiCom introduces a unified framework for state-conditioned composition in large language model (LLM)-driven optimization. It models LLM optimizers within a shared configuration space (artifact, query, operator, evaluation, memory, strategy) and uses an Optimization Controller to dynamically compose mechanisms while a Strategy Adapter refines long-term preferences. Experiments on 32 benchmark groups show OptiCom outperforms 14 configurations, achieving the top score in 23 groups.
By Chenxing Wei, Sichen Liu, Lizhao Liu, Ningyuan Sun, Chen Bingzhou, Ying He, Bo Jiang, Fei Yu, Yao Shu
arXiv:2609.14857v1 Announce Type: new
Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve executio...
By Siwei Wu, Jincheng Ren, Yizhi Li, Haau-Sing Li, Chengran Yang, Yuxuan Zhang, Weicheng Gu, Jian Yang, Riza Batista-Navarro, Chuanyi Zhang, Xianglong Liu, Ming Zhou, Bryan Dai, Chenghua Lin
arXiv:2608. 10795v1 Announce Type: new Abstract: Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks and domains.
By Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin, Danil Sivtsov, Nikita Glazkov, Olga Volkova, Konstantin Pchelin, Iaroslav Bespalov, Dmitry V. Dylov, Petr Anokhin, Ivan Oseledets
arXiv:2608. 09629v1 Announce Type: new Abstract: Self-evolving agents are usually built around prescribed optimization pipelines: the framework decides how to gather evidence, revise a persistent artifact, select candidates, and stop.
By Hui Xue, Fan Yang
arXiv:2610.17379v1 Announce Type: new
Abstract: LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an...
By Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong, Faramarz Fekri