From the 1 of 4 linked papers with an AI index.
4 papers
Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
Lei Bai, Zongsheng Cao, Yang Chen +50
The paper introduces Agents-A1, a 35B mixture-of-experts agent model that attains trillion-parameter-level performance by extending the length of reasoning horizons and integrating…
MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
Shangheng Du, Xiangchao Yan, Jinxin Shi +11
Large language model (LLM) agents are increasingly applied to long-horizon tasks such as scientific discovery and machine learning engineering (MLE), where sustained self-evolution…
A Survey on the Optimization of Large Language Model-based Agents
Shangheng Du, Jiabao Zhao, Jinxin Shi +4
With the rapid development of Large Language Models (LLMs), LLM-based agents have been widely adopted in various fields, becoming essential for autonomous decision-making and inter…
RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation
Zhentao Xie, Chengcheng Han, Jinxin Shi +4
Although multi-agent systems based on large language models show strong capabilities on multiple tasks, they are still limited by high computational overhead, information loss, and…