Still waiting for DeepSeek?

Here comes WideSeek-R1.

Researchers from Tsinghua University and Infinigence AI introduce "width scaling," an innovative lead-agent and subagent framework.

Instead of a single powerful AI working through a problem sequentially, WideSeek-R1… pic.twitter.com/OOb3Azq6G3

— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026

Still waiting for DeepSeek? Here comes WideSeek-R1. Researchers from Tsinghua University and Infinigence AI introduce "width scaling," an innovative lead-agent and subagent framework. Instead of a single powerful AI working through a problem sequentially, WideSeek-R1 orchestrates multiple smaller AIs to work in parallel. This system is trained with multi-agent reinforcement learning, allowing for scalable coordination and simultaneous execution using a shared large language model, but with each sub-agent having specialized tools and isolated contexts. WideSeek-R1-4B achieves an item F1 score of 40.0% on the WideSearch benchmark, a performance comparable to the much larger, single-agent DeepSeek-R1-671B. WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning Paper: arxiv.org/abs/2602.04634 Project: wideseek-r1.github.io Our report: mp.weixin.qq.com/s/qgGe51Rcw… 📬 #PapersAccepted by Jiqizhixin

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