2 citations · 3 across the 3 of their papers we have counts for
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cs.AI2026★ 2 cited
Evaluating Test-Time Scaling of General LLM Agents
Xiaochuan Li, Ryan Ming, Pranav Setlur +6
LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavior in realistic environments remai…
cs.AI2025
Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them
Jiahe Jin, Abhijay Paladugu, Chenyan Xiong
Agentic search requires large language models (LLMs) to perform multi-step search to solve complex information-seeking tasks, imposing unique challenges on their reasoning capabili…