From the 2 of 6 linked papers with an AI index.
6 papers
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
The paper presents Frontis-MA1, a 35‑billion‑parameter model trained as a meta‑evolution agent for machine learning engineering, using a new OpenMLE stack that combines operator le…
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…
Expanding Reasoning Potential in Foundation Model by Learning Diverse Chains of Thought Patterns
Xuemiao Zhang, Can Ren, Chengying Tu +5
Recent progress in large reasoning models for challenging mathematical reasoning has been driven by reinforcement learning (RL). Incorporating long chain-of-thought (CoT) data duri…
LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points
Xuemiao Zhang, Can Ren, Chengying Tu +4
The advancement of large language models (LLMs) struggles with the scarcity of high-quality, diverse training data. To address this limitation, we propose LinkSyn, a novel knowledg…
Large-Scale Diverse Synthesis for Mid-Training
Xuemiao Zhang, Chengying Tu, Can Ren +4
The scarcity of high-quality, knowledge-intensive training data hinders the development of large language models (LLMs), as traditional corpora provide limited information. Previou…
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
Hongliang Lu, Zhonglin Xie, Yaoyu Wu +3
Despite the rapid development of large language models (LLMs), a fundamental challenge persists: the lack of high-quality optimization modeling datasets hampers LLMs' robust modeli…