Argus: Evidence Assembly for Scalable Deep Research Agents
Zhen Zhang, Liangcai Su, Zhuo Chen, Xiang Lin, Haotian Xu, Simon Shaolei Du, Kaiyu Yang, Bo An, Lidong Bing, Xinyu Wang
Argus reframes deep research agents around evidence assembly rather than parallel redundancy. Instead of running many independent search rollouts that duplicate answers and bloat context windows, a Navigator agent maintains a shared evidence graph, identifies missing pieces, and dispatches Searcher agents to fill gaps. Both components are built on a 35B-A3B MoE backbone; the Navigator is trained with RL to verify, dispatch, and synthesize. With 8 parallel Searchers it adds 12.7 points averaged across 8 benchmarks, and with 64 Searchers hits 86.2 on BrowseComp — reportedly above all proprietary agents tested — while keeping the Navigator's context under 21.5K tokens.
No production traction yet. The GitHub references are all paper-tracking bots and RSS aggregators, not implementations. Zero citations on Semantic Scholar. Worth watching as a design pattern for multi-agent search pipelines, but nothing is shipping from this work at the moment.
Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of the art systems scale inference time compute via parallel search and aggregation. Yet deep research answers are composed of complementary pieces of evidence, which parallel rollouts often duplicate rather than complete, yielding diminishing returns while pushing the aggregation context toward the model's limit. We propose Argus, an agentic system in which a Searcher and a Navigator cooperate to treat deep research as assembling a jigsaw from complementary evidence pieces, rather than brute forcing the whole answer in parallel. The Searcher collects evidence traces for a given sub-query through ReAct-style interaction. The Navigator maintains a shared evidence graph, verifying which pieces are still missing, dispatching Searchers to gather them, and reasoning over the completed graph to produce a source-traced final answer. We train the Navigator with reinforcement learning to verify, dispatch, and synthesize, while independently training the Searcher to remain a standard ReAct agent. The resulting Navigator supports rollouts with a single Searcher or many in parallel without retraining. With both Searcher and Navigator built on a 35B-A3B MoE backbone, Argus gains 5.5 points with a single Searcher and 12.7 points with 8 parallel Searchers, averaged over eight benchmarks. With 64 Searchers it reaches 86.2 on BrowseComp, surpassing every proprietary agent we benchmark, while the Navigator's reasoning context stays under 21.5K tokens.