4 citations · 8 across the 7 of their papers we have counts for
8 papers
DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation
Fan Liu, Hao Liu
Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the agent harness that represents…
DSWorld: A Data Science World Model for Efficient Autonomous Agents
Zherui Yang, Fan Liu, Hao Liu
Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computa…
RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations
Leihan Zhang, Wecheng Ye, Xianlong Ma +5
As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and divers…
MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem
Fan Liu, Zherui Yang, Cancheng Liu +3
Mathematical modeling is a cornerstone of scientific discovery and engineering practice, enabling the translation of real-world problems into formal systems across domains such as…
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning
Hang Ni, Fan Liu, Xinyu Ma +5
Large language models (LLMs) have shown promise in automating travel planning, yet they often fall short in addressing nuanced spatiotemporal rationality. While existing benchmarks…
JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework
Fan Liu, Yue Feng, Zhao Xu +4
Despite advancements in enhancing LLM safety against jailbreak attacks, evaluating LLM defenses remains a challenge, with current methods often lacking explainability and generaliz…
Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs
Fan Liu, Zhao Xu, Hao Liu
Although safely enhanced Large Language Models (LLMs) have achieved remarkable success in tackling various complex tasks in a zero-shot manner, they remain susceptible to jailbreak…
Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs
Zhao Xu, Fan Liu, Hao Liu
Although Large Language Models (LLMs) have demonstrated significant capabilities in executing complex tasks in a zero-shot manner, they are susceptible to jailbreak attacks and can…