22 citations · 25 across the 12 of their papers we have counts for
4 papers · 1 filter
Bench2Robust: Scenario-Controlled Recovery for Tool-Using Agents
Chaoran Chen, Vy Nguyen, Ziji Zhang +7
Tool-using LLM agents are typically developed and post-trained in controlled environments with reliable tools, but deployment exposes them to tool failures, API and schema drift, a…
SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation
Yimeng Zhang, Yingying Zhuang, Ziyi Wang +12
Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement in LLM generation. However,…
SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents
Ziyi Wang, Yuxuan Lu, Yimeng Zhang +8
Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use. However, training reliable tool-using agents remains challenging in practic…
Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents
Ziyi Wang, Yuxuan Lu, Yimeng Zhang +12
Tool-calling agents are increasingly deployed in real-world customer-facing workflows. Yet most studies on tool-calling agents focus on idealized settings with general, fixed, and…