GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces

Abstract

Deep research agents integrate fragmented evidence through multi-step tool use. BrowseComp offers a text-only testbed for such agents, but existing multimodal benchmarks rarely require both weak visual cues composition and BrowseComp-style multi-hop verification. Geolocation is a natural testbed because answers depend on combining multiple ambiguous visual cues and validating them with open-web evidence. Thus, we introduce GeoBrowse, a geolocation benchmark that combines visual reasoning with knowledge-intensive multi-hop queries. Level 1 tests extracting and composing fragmented visual cues, and Level 2 increases query difficulty by injecting long-tail knowledge and obfuscating key entities. To support evaluation, we provide an agentic workflow GATE with five think-with-image tools and four knowledge-intensive tools, and release expert-annotated stepwise traces grounded in verifiable evidence for trajectory-level analysis. Experiments show that GATE outperforms direct inference and open-source agents, indicating that no-tool, search-only or image-only setups are insufficient. Gains come from coherent, level-specific tool-use plans rather than more tool calls, as they more reliably reach annotated key evidence steps and make fewer errors when integrating into the final decision. The GeoBrowse bernchmark and codes are provided in https://github.com/ornamentt/GeoBrowse

Figures (6)

• Figure 1: GeoBrowse couples a tool-use framework with a geolocation benchmark: Level 1 emphasizes visual cue composition, while Level 2 contains BrowseComp-style queries, all paired with expert-annotated stepwise traces.
• Figure 2: Distribution of cues and hops on GeoBrowse. Cues count visual cues in Level 1 images, and hops count multi-hop steps in Level 2 queries, quantifying difficulty of visual and knowledge-intensive reasoning.
• Figure 3: Distribution of target administrative levels. The # and % signify count and percentage, respectively.
• Figure 4: Geographic coverage of GeoBrowse visual cues. The inner ring shows the percentage of instances by continent and the outer ring lists representative locations within to illustrate the diversity of covered places.
• Figure 5: The pipeline of GATE, our proposed Geolocation Agentic-workflow with Tool Enhancement approach. The input image is first registered into stable img_id references. GATE then follows a ReAct-style loop: <Think> summarizes the latest evidence and plans the next step, <Action> invokes an image or knowledge tool, and the tool response is returned as <Obs> to update the agent state. Any new images in <Obs> are re-registered, and the loop repeats until the final answer.