24 citations · 31 across the 11 of their papers we have counts for
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cs.AI2026
Finding Where the Buck Stops: An Automated Failure Attribution-Based Reflection Framework for Multi-Agent Collaboration
Xiaoqing Wang, Keman Huang, Bin Liang +3
Multi-agent systems (MAS) powered by large language models have shown promise for complex tasks but suffer from high failure rates. Current self-reflection methods for MAS require…
cs.AI2025
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
Jason Zhu, Hongyu Li
Large reasoning models (LRMs) like OpenAI o1 and DeepSeek R1 have demonstrated impressive performance on complex reasoning tasks like mathematics and programming with long Chain-of…