Title: OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories

Source: http://arxiv.org/abs/2605.04036v1

Summary:

This paper establishes a high-efficiency paradigm for training frontier search agents using only supervised fine-tuning on high-quality synthesized trajectories, challenging resource-intensive industry standards. It provides a foundational methodology for achieving state-of-the-art agentic reasoning and search capabilities with significantly reduced computational requirements.