works on

From the 1 of 6 linked papers with an AI index.

most citedToolRosella: Translating Code Repositories into Standardized Tools for Scientific Agents

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.MA2026

DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution

Hanghui Guo, Weijie Shi, Zhangze Chen +6

The paper introduces DREvo, a method that improves the self‑evolution of large‑language‑model agents by dynamically reassessing and recalibrating historical trial experience to gui…

cs.SE20261 cited

ToolRosella: Translating Code Repositories into Standardized Tools for Scientific Agents

Shimin Di, Xujie Yuan, Hanghui Guo +10

Large Language Model (LLM)-based agent systems are increasingly used for scientific tasks, yet their practical capability remains constrained by the narrow scope of manually curate…

cs.AI2026

Foam-Agent: A Large Language Model-Based Multi-Agent Framework for Automating Computational Fluid Dynamics Workflows

Ling Yue, Nithin Somasekharan, Tingwen Zhang +4

Computational fluid dynamics (CFD) has been the main workhorse of computational physics, yet its steep learning curve and fragmented, multi-stage workflow create significant barrie…

cs.LG2025

MuPlon: Multi-Path Causal Optimization for Claim Verification through Controlling Confounding

Hanghui Guo, Shimin Di, Pasquale De Meo +2

As a critical task in data quality control, claim verification aims to curb the spread of misinformation by assessing the truthfulness of claims based on a wide range of evidence.…

cs.CV2025

Consistent and Invariant Generalization Learning for Short-video Misinformation Detection

Hanghui Guo, Weijie Shi, Mengze Li +8

Short-video misinformation detection has attracted wide attention in the multi-modal domain, aiming to accurately identify the misinformation in the video format accompanied by the…

cs.CL2025

DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation

Hanghui Guo, Jia Zhu, Shimin Di +3

Dynamic Retrieval-augmented Generation (RAG) has shown great success in mitigating hallucinations in large language models (LLMs) during generation. However, existing dynamic RAG m…