Continual Learning for LLM Agents

LLM: Large Language Model

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23 papers in the last four weeks, up 109% on the four weeks before. 0.2% of all new papers.

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EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
Date pendingXinyu Zhu, Yuzhu Cai, Zexi Liu +20Continual Learning for LLM Agents AI Agents for Scientific Discovery
School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China. · SciLand, Shanghai, China. · DP Technology, Beijing, China.
SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation
Date pendingQi Liu, Qinzheng Wang, Can Li +2Continual Learning for LLM Agents LLM Agent Self-Improvement
School of Transportation, Jilin University, 5988 Renmin St., Changchun, Jilin 130022, China · Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation, Tongji University, 4800 Cao’an Rd., Shanghai 201804, China
EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?
Date pendingZixuan Ke, Vaidehi Patil, Haizhou Shi +9Continual Learning for LLM Agents LLM Agent Harnesses
Salesforce Research · University of North Carolina at Chapel Hill, conducted this work during an internship at Salesforce Research · University of Wisconsin–Madison +1

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EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

Xinyu Zhu, Yuzhu Cai, Zexi Liu, Cheng Wang+19

School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China. · SciLand, Shanghai, China. · DP Technology, Beijing, China.

Continual Learning for LLM Agents AI Agents for Scientific Discovery Autonomous Research Agents Self-Evolving Agents

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The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt across scientific fields (loop fragmentation) and useful evidence and experience are lost in long-horizon research (loop discontinuity), bringing obstacles to Agentic Science at Scale. We introduce EvoMaster, a foundational evolving agent framework for Agentic Science at Scale. EvoMaster handles loop fragmentation and loop discontinuity by implementing Loop Research in which external evidence persists and improves later decisions. Through three nested loops, Execution, Exploration and Evolution (E

^3

), loop research connects research within runs, across experiments and across studies. Across ten benchmarks spanning scientific research, coding and reasoning, EvoMaster achieves the best score among four agents using GPT-5.4, reaching a mean score of 58.02%, and outperforms the strongest competing agent Codex(40.29%) while costing 35.6% less. These results show that a shared loop-research foundation can support diverse scientific agents at scale.

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Latest papers 123

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation

EvoHarnessBench: Can Your Agents Keep Pace with an Evolving Harness?