4 citations · 7 across the 5 of their papers we have counts for
5 papers
When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
Dongsheng Zhu, Xuchen Ma, Yucheng Shen +5
Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark…
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…
Cross-model Control: Improving Multiple Large Language Models in One-time Training
Jiayi Wu, Hao Sun, Hengyi Cai +5
The number of large language models (LLMs) with varying parameter scales and vocabularies is increasing. While they deliver powerful performance, they also face a set of common opt…
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…
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning
Hao Sun, Jiayi Wu, Hengyi Cai +6
Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality and deploying local-based LLMs f…