7 papers
Deep Research Pretraining via Predictive Navigation
Jiang Zhou, Zhiyuan Fan, Xing Wu +3
Deep research agents are often trained on expensive, environment-grounded tool-use trajectories that require repeated retrieval, document inspection, and report evaluation. We intr…
Towards Knowledgeable Deep Research: Framework and Benchmark
Wenxuan Liu, Zixuan Li, Long Bai +13
Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive reports. In contrast to existing…
PolicyLong: Towards On-Policy Context Extension
Junlong Jia, Ziyang Chen, Xing Wu +4
Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependencies via information-theoretic ve…
WRAP++: Web discoveRy Amplified Pretraining
Jiang Zhou, Yunhao Wang, Xing Wu +2
Synthetic data rephrasing has emerged as a powerful technique for enhancing knowledge acquisition during large language model (LLM) pretraining. However, existing approaches operat…
MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs
Lun Zhan, Feng Xiong, Huanyong Liu +2
Synthesizing high-quality training data is crucial for enhancing domain models' reasoning abilities. Existing methods face limitations in long-tail knowledge coverage, effectivenes…
Towards Solving More Challenging IMO Problems via Decoupled Reasoning and Proving
Zhenwen Liang, Linfeng Song, Yang Li +4
Automated Theorem Proving (ATP) in formal languages is a foundational challenge for AI. While Large Language Models (LLMs) have driven remarkable progress, a significant gap remain…