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README.md

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+---
+license: apache-2.0
+language:
+  - en
+base_model:
+  - Qwen/Qwen3-4B
+metrics:
+  - accuracy
+model-index:
+  - name: WideSeek-R1-4B
+    results:
+      - task:
+          type: WideSearch
+        dataset:
+          type: WideSearch
+          name: WideSearch
+        metrics:
+          - type: accuracy
+            value: 40.0
+---
+
+
+# WideSeek-R1-4B
+
+## Overview
+
+![image](fig/scaling.png)
+
+Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. 
+
+In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks. 
+
+Extensive experiments show that WideSeek-R1-4B achieves an item F1 score of 40.0\% on the WideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.
+
+For more details, see our [project page](https://thu-nics.github.io/WideSeek-R1/)
+
+## Citation
+
+If you use this model in your research, please cite our paper:
+
+```bibtex
+To Be Done
+```