2 citations · 2 across the 10 of their papers we have counts for
10 papers
Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence
Brian Wang, Bin Feng, Xiaoman Pan +26
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objec…
Return Probability for the Switch--Walk--Switch Lamplighter Walk on a Regular Tree
Chenyang An, Minghao Pan
We derive the sharp return-probability asymptotic for the switch--walk--switch lamplighter walk with lamp group over the infinite -regular tree: \[ p_{2n}(e,e) = ρ…
Lower Bounds for Advection-Diffusion Equations: An Exploration with AI-Generated Proofs
Chenyang An, Xiaoqian Xu
We establish explicit lower bounds for advection-diffusion equations in three settings: a polynomial bound for inviscid shears with , a uni…
QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems
Chenyang An, Qihao Ye, Minghao Pan +1
We present QED, an open-source multi-agent system that turns human-provided research questions into complete mathematical proofs without further human guidance. Its pipeline is des…
The Price of Format: Diversity Collapse in LLMs
Longfei Yun, Chenyang An, Zilong Wang +2
Instruction-tuned large language models (LLMs) employ structured templates, such as role markers and special tokens, to enforce format consistency during inference. However, we ide…
Linear Correlation in LM's Compositional Generalization and Hallucination
Letian Peng, Chenyang An, Shibo Hao +2
The generalization of language models (LMs) is undergoing active debates, contrasting their potential for general intelligence with their struggles with basic knowledge composition…
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation
Ali Abbasi, Shima Imani, Chenyang An +6
With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information…
Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation
Chenyang An, Shima Imani, Feng Yao +8
In the field of large language model (LLM)-based proof generation, despite extensive training on large datasets such as ArXiv, LLMs still exhibit only modest performance on proving…
Correlation and Navigation in the Vocabulary Key Representation Space of Language Models
Letian Peng, Chenyang An, Jingbo Shang
Language model (LM) decoding is based on the next-token prediction (NTP) probability distribution. For neural LMs (e.g., Transformer-based), NTP distribution is essentially a softm…
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Chenyang An, Zhibo Chen, Qihao Ye +6
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof s…