7 papers
From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs
Jiaxiang Chen, Zhuo Wang, Mingxi Zou +4
Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration,…
SlimRAG: Retrieval without Graphs via Entity-Aware Context Selection
Jiale Zhang, Jiaxiang Chen, Zhucong Li +5
Retrieval-Augmented Generation (RAG) enhances language models by incorporating external knowledge at inference time. However, graph-based RAG systems often suffer from structural o…
ChemAmp: Amplified Chemistry Tools via Composable Agents
Zhucong Li, Powei Chang, Jin Xiao +6
Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool con…
AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
Jiaxiang Chen, Jingwei Shi, Lei Gan +6
As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge du…
ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents
Zhucong Li, Jin Xiao, Bowei Zhang +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific research, particularly in chemistry-related tasks such as molecular design, reaction prediction, a…
Can we only use guideline instead of shot in prompt?
Jiaxiang Chen, Song Wang, Zhucong Li +4
Currently, prompting techniques can be mainly divided into two categories:1)shot method implicitly inspires the model to answer the question by mimicing the steps in the given exam…
AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications
Xin Pang, Zhucong Li, Jiaxiang Chen +3
We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Appli…