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From the 1 of 7 linked papers with an AI index.

collaborators

7 papers

cs.AI2026

ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System

Yutong He, Daibo Li, Guohong Li +15

ReasFlow is an autonomous multi‑agent system that leverages large language models to perform rigorous mathematical reasoning, retrieve relevant knowledge, and generate complete res…

cs.CL2026

CentroidKV: Efficient Long-Context LLM Inference via KV Cache Clustering

Jie Hu, Shengnan Wang, Yutong He +8

Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for…

cs.AI2026

Proof-Refactor: Refactoring Generated Formal Proofs into Modular Artifacts

Yiming Fu, Peixuan Liu, Zichen Wang +1

While Large Language Models (LLMs) have shown strong performance in generating formal proofs, their outputs often remain less readable, modular, maintainable, and reusable than pro…

cs.LG2026

OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving

Chenyi Li, Yanchen Nie, Zhenyu Ming +3

Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization, fundamental to machine learnin…

cs.AI2026

M2F: Automated Formalization of Mathematical Literature at Scale

Zichen Wang, Wanli Ma, Zhenyu Ming +3

Automated formalization of mathematics enables mechanical verification but remains limited to isolated theorems and short snippets. Scaling to textbooks and research papers is larg…

cs.AI2025

Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers

Ran Xin, Zeyu Zheng, Yanchen Nie +2

The integration of Large Language Models (LLMs) into automated theorem proving has shown immense promise, yet is fundamentally constrained by challenges in scaling up both training…