7 citations · 11 across the 7 of their papers we have counts for
8 papers
Do LLM Agents Negotiate Rationally? A Mechanism-Design Framework for Verifiable Multi-Agent Interaction over A2A/MCP
Wael Albayaydh, Rui Zhao
Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool access and Google's Agent2Agent (A2A) pro…
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
Wael Albayaydh, Rui Zhao, Ivan Flechais
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons.…
Performance-guided Reinforced Active Learning for Object Detection
Zhixuan Liang, Xingyu Zeng, Rui Zhao +1
Active learning (AL) strategies aim to train high-performance models with minimal labeling efforts, only selecting the most informative instances for annotation. Current approaches…
CLEAR: Can Language Models Really Understand Causal Graphs?
Sirui Chen, Mengying Xu, Kun Wang +4
Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive…
Causal Evaluation of Language Models
Sirui Chen, Bo Peng, Meiqi Chen +7
Causal reasoning is viewed as crucial for achieving human-level machine intelligence. Recent advances in language models have expanded the horizons of artificial intelligence acros…
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Yilun Kong, Jingqing Ruan, Yihong Chen +9
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend…
To be or not to be? an exploration of continuously controllable prompt engineering
Yuhan Sun, Mukai Li, Yixin Cao +4
As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for cu…
Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection
Zhixuan Liang, Xingyu Zeng, Rui Zhao +1
Active learning strategies aim to train high-performance models with minimal labeled data by selecting the most informative instances for labeling. However, existing methods for as…