Argus: AI Agent Treats Deep Research as Evidence Jigsaw Assembly

The recently introduced AI system, Argus, outlined in a preprint on arXiv, reconceptualizes extensive research as piecing together a jigsaw puzzle from complementary evidence rather than relying on exhaustive parallel searches. It includes a Searcher that gathers evidence for sub-queries via ReAct-style interactions and a Navigator that oversees a collective evidence graph, identifies absent pieces, and sends out Searchers to collect them. This methodology seeks to minimize redundancy and the context overload often associated with parallel implementations. The full paper can be found at arXiv:2605.16217.