The paper introduces RuHarness, a framework that co‑evolves rubrics and harnesses to enable continual self‑improvement of autonomous agents that rely on large language models. RuHarness automatically discovers and reassesses behavioral criteria from execution trajectories, prioritizes unresolved weaknesses, and uses these insights to guide targeted harness updates while keeping the task objective unchanged. Experiments across varied agent tasks and backbone models demonstrate that RuHarness outperforms existing harness‑evolution baselines, especially on unseen held‑out tasks, and that its adaptive rubric prioritization supports sustained improvement and stable generalization over successive evolution rounds.
By Hao Chen, Yukun Yan, Ziyu Han, Ye He, Chuanzhe Guo, Shangzhan Li, Qi Shi, Qingfu Zhu, Maosong Sun, Wanxiang Che
The paper introduces HiMA-Ecom, a hierarchical multi‑agent e‑commerce benchmark with 22.8K instances, where 17.7K include memory entries. It explores training, evaluation, and management of accumulated context in long‑horizon agentic systems, and proposes HiMA‑R1, a policy optimization method that controls trajectory context budgets and uses reward‑driven memory to accelerate convergence. Experiments show that agents built on 3B and 7B open‑source models outperform DeepSeek‑V3 by an average of 6.0 points, while reward‑driven memory cuts convergence steps by about 55%.
By Junxing Hu, Ai Han, Haolan Zhan, Pu Wei, Zhiqian Zhang, Yuhang Guo, Jiawei Lu, Zhen Chen, Haoran Li, Zicheng Zhang, Ling Li
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.
By Yunxiang Zhang, Yiheng Li, Ali Payani, Lu Wang
Darwin‑Claw is a new framework for large language model agents that enables environment‑adaptive, self‑evolving behavior. It systematically distills episodic experiences into prototype vectors through a hierarchical experience consolidation system, providing context‑sensitive perception patches and explicit optimization signals for skill refinement. The method is deployable with mainstream agent systems and, according to experiments on WildClawBench, delivers significant performance gains while maintaining controllability and stability.
By Mingze Yin, Xiaohan Wang, Dian Li, Longfei Luo, Jiahong Yan, Yiheng Zhu, Chang-Yu Hsieh
The paper introduces OPSD, a method that uses on‑policy self‑distillation to train a context‑free student model from a teacher conditioned on user memory, combined with a verify‑then‑regenerate router that directs queries to either weight or text memory. OPSD enables weight memory to compete with or surpass traditional text‑based memory systems on the HaluMem benchmark, reducing token usage per query. The study also explores how different training recipes influence weight memory performance and offers guidance for integrating it with retrieval in persistent, multi‑session agent deployments.
By Zeyu Fang, Jun Yin, Yongsheng Mei, Xu Han, Chaitali Joshi, Zhongkai Sun, Jie Hao, Bin Guo, Chengyuan Ma, Benjamin Z. Yao, Chenlei Guo
arXiv:2606. 06787v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.
By Runzhe Wang, Huilin Lu, Shengjie Liu, Li Dong, Jason Zhu
arXiv:2607. 20668v1 Announce Type: cross Abstract: TextGrad improves language-model systems by revising text from feedback.
By Jaideep Ray, Ankit Goyal
The paper introduces HiPS, a hierarchical strategy co‑evolution framework for memory‑augmented agents that separates memory management into a globally shared foundation and a user‑specific adaptive tier. HiPS uses a Universal Strategy to capture shared principles from cross‑persona trajectories, Persona Delta Distillation to create tailored rules for users deviating from general patterns, and Cross‑Level Rule Flow to dynamically adjust the boundary between global and personal rules. Experiments show that this approach consistently outperforms existing memory‑augmented baselines.
By Yupeng Han, Shuochen Liu, Kai Zhang, Ze Liu, Zhihong Pan, Xianquan Wang
The paper presents a comprehensive evaluation of 11 memory substrates for long‑horizon LLM agents, using a unified set of 26 performance and efficiency metrics across three backbone models and four benchmark suites. It finds that no single substrate consistently outperforms others; retrieval‑heavy substrates aid long‑context factual QA but can impair sequential decision‑making, and scalability can shift performance trade‑offs. The study demonstrates that selecting a memory substrate tailored to the operating regime yields better results than any fixed choice, offering empirical guidance for designing efficient, reliable, and regime‑aware long‑term memory systems.
By Wei-Chieh Huang, Weizhi Zhang, Yuchen Wu, Yankai Chen, Eric Hanchen Jiang, Wooseong Yang, Yiwei Yang, Henry Peng Zou, Hanrong Zhang, Ying Nian Wu, Haolun Wu, Kai-Wei Chang, Philip S. Yu, Xue Liu, Aylin Caliskan
CORAL is an LLM‑native harness that automates continual optimization of production recommender systems. It operates in a closed loop: an agent observes system signals, reasons over past decisions, and uses tools—including a numerical optimizer—to reconfigure the recommender while staying within a fixed operating budget. In A/B experiments on two large social platforms, CORAL improved engagement without extra serving cost on one platform and reduced serving cost without harming engagement on the other, demonstrating that a single agentic loop can replace manual engineering for ongoing system tuning.
By Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang, Yuchen Wang, Rahul Sharma, Matthew DeSousa, Jiayi Liu, Xin Guo, Lizhu Zhang, Xiangjun Fan
arXiv:2607. 17621v1 Announce Type: new Abstract: Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections.
By Yechao Hong, Haiquan Qiu, Yaqing Wang, Quanming Yao
Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient methods. We ask a fundamentally different question: how much of this search policy can be internalized by a single tool-using agent?