6 papers
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Zixuan Wang, Xingyu Dang, Jason D. Lee +1
Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggests that reweighting or curati…
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
Hengyu Fu, Tianyu Guo, Zixuan Wang +5
Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT). However, many questions require t…
Transformers Provably Learn to Internalize Chain-of-Thought
Yixiao Huang, Hanlin Zhu, Zixuan Wang +4
Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning from exponential to polynomial…
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
Zhaowei Liu, Xin Guo, Zhi Yang +14
In recent years, general-purpose large language models (LLMs) such as GPT, Gemini, Claude, and DeepSeek have advanced at an unprecedented pace. Despite these achievements, their ap…
FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning
Shaoyu Dou, Yutian Shen, Mofan Chen +9
Large Language Models (LLMs) demonstrate significant potential but face challenges in complex financial reasoning tasks requiring both domain knowledge and sophisticated reasoning.…
FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domain
Lingfeng Zeng, Fangqi Lou, Zixuan Wang +18
The booming development of AI agents presents unprecedented opportunities for automating complex tasks across various domains. However, their multi-step, multi-tool collaboration c…