Academic

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

arXiv:2604.04017v1 Announce Type: new 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 s

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Xinyu Geng, Yanjing Xiao, Yuyang Zhang, Hanwen Wang, Xinyan Liu, Rui Min, Tianqing Fang, Yi R. Fung · · 1 min read · 13 views
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arXiv:2604.04017v1 Announce Type: new 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

Executive Summary

This article introduces GeoBrowse, a geolocation benchmark designed to evaluate the performance of deep research agents in combining fragmented visual cues with multi-hop verification. The GeoBrowse benchmark consists of two levels: Level 1 tests the ability to extract and compose fragmented visual cues, while Level 2 increases query difficulty by injecting long-tail knowledge and obfuscating key entities. To support evaluation, the authors provide an agentic workflow GATE, which includes five think-with-image tools and four knowledge-intensive tools. Expert-annotated stepwise traces are also provided to facilitate trajectory-level analysis. The results show that GATE outperforms direct inference and open-source agents, highlighting the importance of coherent tool-use plans in achieving accurate results. The GeoBrowse benchmark and codes are made available on GitHub.

Key Points

  • ▸ GeoBrowse is a geolocation benchmark designed to evaluate the performance of deep research agents.
  • ▸ The benchmark consists of two levels: Level 1 tests fragmented visual cue extraction and composition, while Level 2 increases query difficulty with long-tail knowledge and obfuscated entities.
  • ▸ The agentic workflow GATE outperforms direct inference and open-source agents, demonstrating the importance of coherent tool-use plans.

Merits

Strength in Evaluating Multi-Modal Reasoning

GeoBrowse addresses the limitations of existing multimodal benchmarks by requiring both weak visual cues composition and BrowseComp-style multi-hop verification, making it a valuable tool for evaluating the performance of deep research agents in complex, real-world scenarios.

Demerits

Limited Scope and Generalizability

The GeoBrowse benchmark is specifically designed for geolocation tasks, which may limit its applicability to other domains. Additionally, the results may not generalize to other types of tasks or agents, highlighting the need for further research to validate its effectiveness in a broader range of scenarios.

Expert Commentary

The introduction of GeoBrowse represents a significant contribution to the field of artificial intelligence, particularly in the area of multi-modal reasoning. By providing a comprehensive benchmark for evaluating the performance of deep research agents, GeoBrowse addresses a critical gap in existing research and opens up new opportunities for investigation. However, its limited scope and generalizability highlight the need for further research to validate its effectiveness in a broader range of scenarios. Nevertheless, GeoBrowse is an important step forward in the development of more accurate and reliable AI systems, and its implications will be felt across a range of domains, from computer vision to natural language processing.

Recommendations

  • ✓ Future research should focus on extending the scope and generalizability of GeoBrowse, exploring its applicability to other domains and tasks.
  • ✓ The development of GeoBrowse should be accompanied by a more comprehensive evaluation of its limitations and potential biases, ensuring that its results are reliable and trustworthy.

Sources

Original: arXiv - cs.CL

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