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cs.AI2025
DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models
Ruofan Zhang, Bin Xia, Zhen Cheng +4
Adaptive reasoning is essential for aligning the computational effort of large language models (LLMs) with the intrinsic difficulty of problems. Current chain-of-thought methods bo…
cs.AI2025★ 1 cited
Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks
Aaron Xuxiang Tian, Ruofan Zhang, Jiayao Tang +12
We study multi-turn multi-agent orchestration, where multiple large language model (LLM) agents interact over multiple turns by iteratively proposing answers or casting votes until…