The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing "language agents", which are complex large language models (LLMs) pipelines involving both prompting techniques and tool usage methods. While language agents have demonstrated impressive capabilities for many real-world tasks, a fundamental limitation of current language agents research is that they are model-centric, or engineering-centric. That's to say, the progress on prompts, tools, and pipelines of language agents requires substantial manual engineering efforts from human experts rather than automatically learning from data. We believe the transition from model-centric, or engineering-centric, to data-centric, i.e., the ability of language agents to autonomously learn and evolve in environments, is the key for them to possibly achieve AGI.
In this work, we introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own in a data-centric way using symbolic optimizers. Specifically, we consider agents as symbolic networks where learnable weights are defined by prompts, tools, and the way they are stacked together. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning: back-propagation and gradient descent. Instead of dealing with numeric weights, agent symbolic learning works with natural language simulacrums of weights, loss, and gradients. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks and show that agent symbolic learning enables language agents to update themselves after being created and deployed in the wild, resulting in "self-evolving agents".
LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression
Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington（惠灵顿维多利亚大学数据科学与人工智能中心及工程与计算机科学学院）；Department of Computer Science and Engineering, Michigan State University（密歇根州立大学计算机科学与工程系）
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Baptist Univeristy（香港浸会大学）；Harbin Institude of Technology, Shenzhen（哈尔滨工业大学（深圳））
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）
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
TMLR Group, Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系 TMLR 组）；Beijing National Research Center for Information Science（北京信息科学国家研究中心）；Technology (BNRist), Department of Automation, Tsinghua University, Beijing, P.R. China（技术（BNRist），自动化系，清华大学，北京，中华人民共和国）；Lenovo Research（联想研究）
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）
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization
School of Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）；School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（北京邮电大学计算机学院）
Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning
Idiap Research Institute（Idiap研究 institute）；École Polytechnique Fédérale de Lausanne（瑞士联邦理工学院）；Honda Research Institute Europe（本田欧洲研究院）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；National Biomarker Centre, CRUK-MI, University of Manchester（曼彻斯特大学国家生物标记中心）
MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents
Theta Health Inc.（西塔健康公司）；Chen Frontier Lab for AI and Mental Health, Tianqiao and Chrissy Chen Institute（天桥脑科学研究院陈天桥雒芊芊脑科学研究院AI与精神健康前沿实验室）；Shanghai Mental Health Center（上海市精神卫生中心）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Shanghai Clinical Research Center for Mental Health（上海市精神疾病临床医学中心）；Shanghai Key Laboratory of Psychotic Disorders（上海市精神疾病重点实验室）
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
University of Glasgow（格拉斯哥大学）；University of Sheffield（谢菲尔德大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；National University of Singapore（新加坡国家大学）；University of Cambridge（剑桥大学）；University College London（伦敦大学学院）；University of Aberdeen（阿伯丁大学）；Leiden University（莱顿大学）
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
University of Rochester（罗切斯特大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Surrey（萨里大学）；The University of Manchester（曼彻斯特大学）；Queen Mary University of London（伦敦玛丽女王大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
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）
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）
NEOLAF, an LLM-powered neural-symbolic cognitive architecture
IEEE Artificial Intelligent Standards Committee, IEEE（电气和电子工程师协会人工智能标准委员会）；Macao University of Science and Technology（澳门科技大学）；Carnegie Learning（卡内基学习公司）；University of Sheffield（谢菲尔德大学）；National Institute of Education, Singapore（新加坡国立教育学院）；University of Memphis（孟菲斯大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Rochester（罗切斯特大学）；Stanford University（斯坦福大学）；Beijing Normal University（北京师范大学）
School of Physics, Peking University（北京大学物理系）；Peking University（北京大学）；Center for High Energy Physics, Peking University（北京大学高能物理中心）；University of California, Berkeley（加州大学伯克利分校）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理天文学院）；Beijing Normal University（北京师范大学）；Key Laboratory of Multi-scale Spin Physics, Ministry of Education, Beijing Normal University（北京师范大学多尺度自旋物理重点实验室）；Beijing Computational Science Research Center（北京计算科学研究中心）
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）
