We introduce the Evolving Ensemble of Agents (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic discovery. Rather than reinventing the wheel within the ``LLMs as optimizers'' paradigm, EvE fixes the base agent substrate and focuses entirely on evolving the cumulative guidance and skills that dictate agent behaviors. By maintaining two co-evolving populations, namely functional code solvers and agent guidance states, the system evaluates agents through a synchronous race, updating their empirical Elo ratings based on the marginal gains they contribute to the current solver state. When applied to a research bottleneck in In-Context Operator Networks (ICON), EvE autonomously discovered a robust rescale-then-interpolate mechanism that enables reliable example-count generalization. Crucially, controlled ablations reveal the absolute necessity of stage-dependent agent adaptation to navigate the shifting search landscapes of complex codebases. Compared to variants driven by a fixed initial agent or even a frozen ``best-evolved'' agent, EvE uniquely avoids phase mismatch, demonstrating that organizing agents into a self-revising ensemble is the fundamental driver for breaking through static performance ceilings.
University of Pennsylvania（宾夕法尼亚大学）；Brown University（布朗大学）；Nanyang Technological University（南洋理工大学）；University of Notre Dame（圣母大学）；George Mason University（乔治梅森大学）
AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?
University of Chinese Academy of Sciences（中国科学院大学）；Microsoft（微软公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Nanjing University（南京大学）
Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective
School of Computer Science and Engineering, The University of New South Wales（新南威尔士大学计算机科学与工程学院）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；School of Data Science, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）数据科学学院）；Zhejiang Gongshang University（浙江工商大学）
Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Renmin University of China（中国人民大学）；Princeton University（普林斯顿大学）；Hong Kong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）
MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents
Nanyang Technological University（南洋理工大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Tsinghua University（清华大学）
CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学与工程学院）；Department of Computer Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Big Data Institute, Central South University（中南大学大数据研究院）
CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents
Central South University（中南大学）；College of Computer Science, Sichuan University（四川大学计算机学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京高精尖创新中心）；School of Artificial Intelligence（人工智能学院）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence
School of Software Technology, Zhejiang University（浙江大学软件学院）；Laboratory for Statistical Monitoring and Intelligent Governance of Common Prosperity, Zhejiang Gongshang University（浙江工商大学共同富裕统计监测与智能治理实验室）；Tongyi Lab, Alibaba Group（阿里集团通义实验室）；Alibaba Cloud（阿里云）
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；McGill University（麦吉尔大学）；Columbia University（哥伦比亚大学）；Zhejiang University（浙江大学）；University of British Columbia（不列颠哥伦比亚大学）
Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation
Beihang University（北京航空航天大学）；Beijing Institute of Technology（北京理工大学）；Independent Researcher（独立研究者）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Edinburgh（爱丁堡大学）
EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Alan Turing Institute（阿兰·图灵研究所）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；NatWest AI Research（国民西敏寺银行人工智能研究部）；University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）
MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation
The Chinese University of Hong Kong（香港中文大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；South China University of Technology（华南理工大学）；Chinese Academy of Sciences（中国科学院）
This paper presents a technique called evolving self-supervised neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward. The proposed method presents some sort-of paradigm shift, and differs greatly from both traditional gradient-based learning and evolutionary algorithms in that it combines the metaphor of evolution and learning, more specifically self-learning, together, rather than treating these phenomena alternatively. I simulate a multi-agent system in which neural networks are used to control autonomous foraging agents…
Stability is perhaps one of the most desirable features of any engineered system, given the importance of being able to predict its response to various environmental conditions prior to actual deployment. Engineered systems are becoming ever more complex, approaching the same levels of biological ecosystems, and so their stability becomes ever more important, but taking on more and more differential dynamics can make stability an ever more elusive property. The Chli-DeWilde definition of stability views a Multi-Agent System as a discrete time Markov chain with potentially unknown transition pr…
A Multi-Agent System is a distributed system where the agents or nodes perform complex functions that cannot be written down in analytic form. Multi-Agent Systems are highly connected, and the information they contain is mostly stored in the connections. When agents update their state, they take into account the state of the other agents, and they have access to those states via the connections. There is also external, user-generated input into the Multi-Agent System. As so much information is stored in the connections, agents are often memory-less. This memory-less property, together with the…
We demonstrate how an evolutionary algorithm can be extended with a curriculum learning process that selects automatically the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected so to adjust the level of difficulty to the ability level of the current evolving agents and so to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental c…
We investigate the self-organising behaviour of Digital Ecosystems, because a primary motivation for our research is to exploit the self-organising properties of biological ecosystems. We extended a definition for the complexity, grounded in the biological sciences, providing a measure of the information in an organism's genome. Next, we extended a definition for the stability, originating from the computer sciences, based upon convergence to an equilibrium distribution. Finally, we investigated a definition for the diversity, relative to the selection pressures provided by the user requests. …
A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. Self-organisation is perhaps one of the most desirable features in the systems that we engineer, and it is important for us to be able to measure self-organising behaviour. We investigate the self-organising aspects of Digital Ecosystems, created through the application of evolutionary computing to Multi-Agent Systems (MASs), aiming to dete…
A fundamental aspect of behaviour is the ability to encode salient features of experience in memory and use these memories, in combination with current sensory information, to predict the best action for each situation such that long-term objectives are maximized. The world is highly dynamic, and behavioural agents must generalize across a variety of environments and objectives over time. This scenario can be modeled as a partially-observable multi-task reinforcement learning problem. We use genetic programming to evolve highly-generalized agents capable of operating in six unique environments…
As the relative power, performance, and area (PPA) impact of embedded memories continues to grow, proper parameterization of each of the thousands of memories on a chip is essential. When the parameters of all memories of a product are optimized together as part of a single system, better trade-offs may be achieved than if the same memories were optimized in isolation. However, challenges such as a sparse solution space, conflicting objectives, and computationally expensive PPA estimation impede the application of common optimization heuristics. We show how the memory system optimization probl…
