Search Results
AFlow: Automating Agentic Workflow Generation
Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences.
Ranked #3 on
Code Generation
on HumanEval
Data Interpreter: An LLM Agent For Data Science
On InfiAgent-DABench, it achieves a 25% performance boost, raising accuracy from 75. 9% to 94. 9%.
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs).
Ranked #12 on
Code Generation
on HumanEval
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks that autonomously build pipelines.
ChatDev: Communicative Agents for Software Development
Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing.
OpenHands: An Open Platform for AI Software Developers as Generalist Agents
OpenDevin), a platform for the development of powerful and flexible AI agents that interact with the world in similar ways to those of a human developer: by writing code, interacting with a command line, and browsing the web.
Experiential Co-Learning of Software-Developing Agents
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents.
Autonomous Agents for Collaborative Task under Information Asymmetry
Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange.
Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
We show that while existing prompted LMAs (gpt-3. 5-turbo or gpt-4) achieve 94. 0% average success rate on base tasks, their performance degrades to 24. 9% success rate on compositional tasks.
Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions
In the absence of navigation instructions, such abilities are vital for the agent to make high-quality decisions in long-range city navigation.
