6 papers
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
Zhiyuan Hu, Yucheng Wang, Yufei He +7
Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from explor…
ConfTuner: Training Large Language Models to Express Their Confidence Verbally
Yibo Li, Miao Xiong, Jiaying Wu +1
Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as science, law, and healthcare, where accurate expressions of uncertainty are essential for reli…
JudgeLRM: Large Reasoning Models as a Judge
Nuo Chen, Zhiyuan Hu, Qingyun Zou +4
Large Language Models (LLMs) are increasingly adopted as evaluators, offering a scalable alternative to human annotation. However, existing supervised fine-tuning (SFT) approaches…
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Hui Chen, Miao Xiong, Yujie Lu +7
Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmar…
Efficient Inference for Large Reasoning Models: A Survey
Yue Liu, Jiaying Wu, Yufei He +11
Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason, exhibiting promising performance in solving complex…