4 citations · 19 across the 32 of their papers we have counts for
10 papers · 1 filter
Auxiliary Metrics Help Decoding Skill Neurons in the Wild
Yixiu Zhao, Xiaozhi Wang, Zijun Yao +2
Large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque. In this paper, we introduce a simple…
StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?
Yanxu Chen, Zijun Yao, Yantao Liu +5
Large language models (LLMs) demonstrate strong potential as autonomous agents, with promising capabilities in reasoning, tool use, and sequential decision-making. While prior benc…
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv +167
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…
Are Reasoning Models More Prone to Hallucination?
Zijun Yao, Yantao Liu, Yanxu Chen +5
Recently evolved large reasoning models (LRMs) show powerful performance in solving complex tasks with long chain-of-thought (CoT) reasoning capability. As these LRMs are mostly de…
How do Transformers Learn Implicit Reasoning?
Jiaran Ye, Zijun Yao, Zhidian Huang +8
Recent work suggests that large language models (LLMs) can perform multi-hop reasoning implicitly -- producing correct answers without explicitly verbalizing intermediate steps --…
When Experimental Economics Meets Large Language Models: Evidence-based Tactics
Shu Wang, Zijun Yao, Shuhuai Zhang +3
Advancements in large language models (LLMs) have sparked a growing interest in measuring and understanding their behavior through experimental economics. However, there is still a…