3 citations · 8 across the 23 of their papers we have counts for
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cs.AI2025
Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning
Zhihao Dou, Qinjian Zhao, Zhongwei Wan +10
Large language models (LLMs) demonstrate strong reasoning abilities via Chain-of-Thought (CoT), but their token-level generation encourages local decisions and lacks global plannin…
cs.AI2025★ 3 cited
QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry
Jiaqing Xie, Weida Wang, Ben Gao +5
Quantitative chemistry is central to modern chemical research, yet the ability of large language models (LLMs) to perform its rigorous, step-by-step calculations remains underexplo…
cs.AI2025
Control-R: Towards controllable test-time scaling
Di Zhang, Weida Wang, Junxian Li +10
This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning…