8 citations · 9 across the 10 of their papers we have counts for
11 papers
Argus: A General-Purpose Agentic Reasoning Runtime for Long-Horizon Tasks
Boxiu Li, Zimo Wen, Yijia Fan +24
Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a…
Euclean: Automated Geometry Problem Formalization with Unified Verification in Lean
Linbin Tang, Jingyan You, Zilin Kang +8
Recent formal reasoning systems have reached IMO-level performance, yet they leave a fragmented landscape: algebra and number theory are handled in Lean, while geometry still relie…
STAR-PólyaMath: Multi-Agent Reasoning under Persistent Meta-Strategic Supervision
Jiaao Wu, Xian Zhang, Hanzhang Liu +3
Frontier AI models and multi-agent systems have led to significant improvements in mathematical reasoning. However, for problems requiring extended, long-horizon reasoning, existin…
lit-tag: An app for adding custom tags and notes to a citation database
Paul McElhany, Kalina Grabb, Maddison Wood
To facilitate the review, evaluation and analysis of scientific literature, the lit-tag R Shiny application provides a convenient interface for users to generate a citation databas…
TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Yunchi Lu, Youshan Miao, Cheng Tan +4
Training large language models (LLMs) at scale requires parallel execution across thousands of devices, incurring enormous computational costs. Yet, these costly distributed traini…
Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models
Chenrui Cao, Liangcheng Song, Zenan Li +4
Recent advancements, such as DeepSeek-Prover-V2-671B and Kimina-Prover-Preview-72B, demonstrate a prevailing trend in leveraging reinforcement learning (RL)-based large-scale train…
PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems
Yingfan Deng, Anhao Zhou, Yuan Yuan +3
Multi-Agent Systems have recently emerged as a promising paradigm for collaborative reasoning and solving complex tasks. However, the design of collaborative learning algorithms in…
Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
Zenan Li, Zhaoyu Li, Wen Tang +6
Large language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a.k.a.} tactics) within a proof system. However, the space of possible tact…
Neuro-Symbolic Data Generation for Math Reasoning
Zenan Li, Zhi Zhou, Yuan Yao +5
A critical question about Large Language Models (LLMs) is whether their apparent deficiency in mathematical reasoning is inherent, or merely a result of insufficient exposure to hi…
Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency
Zenan Li, Yifan Wu, Zhaoyu Li +4
Autoformalization, the task of automatically translating natural language descriptions into a formal language, poses a significant challenge across various domains, especially in m…
A Survey on Deep Learning for Theorem Proving
Zhaoyu Li, Jialiang Sun, Logan Murphy +5
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in natural language to rigorous derivations in formal systems. In recent years, the advance…