Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus
arXiv:2606.15345v1 Announce Type: new Abstract: Deep research agents are increasingly evaluated on their ability to search for evidence, reason over retrieved sources, and produce grounded answers. Existing browsing benchmarks, however, largely assume that the user's query and the supporting evidence are written in the same language, leaving open whether agentic search systems can operate when relevant evidence appears in another language. We introduce XBCP (Cross-lingual BrowseComp-Plus), a controlled benchmark that preserves the English question-and-answer space of BrowseComp-Plus but varies
The rapid advancement of AI agents necessitates more robust evaluation benchmarks, and the increasing global deployment of these agents highlights the limitations of monolingual testing.
A strategic reader should care about limitations in AI agent evaluation, as it directly impacts the reliability and global applicability of autonomous systems, especially across diverse linguistic contexts.
The introduction of XBCP allows for assessing deep research agents' capabilities with cross-lingual evidence, moving beyond previous monolingual assumptions.
- · Multilingual AI research
- · Global AI agent developers
- · Users in non-English speaking regions
- · AI evaluation platforms
- · Monolingual AI agent developers (if they don't adapt)
- · Benchmarks lacking cross-lingual capabilities
AI agents will see increased development and testing focused on cross-lingual information retrieval and reasoning.
This could lead to more globally competent and adaptable AI agents, reducing bias towards English-centric data.
Improved cross-lingual capabilities in AI agents could accelerate knowledge transfer and reduce linguistic barriers in research and commerce globally.
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Read at arXiv cs.CL