CocoonBench: Can Multilingual Search Agents Escape Language Information Cocoons?

Multilingual search agents can access the global Web, yet may remain trapped in a language environment with insufficient evidence; we refer to this failure as a language information cocoon. However, existing search benchmarks largely operate within a single language, especially English, overlooking entity connections across multiple languages. We introduce CocoonBench, a benchmark that evaluates whether multilingual search agents can escape such language information cocoons. CocoonBench mines answer-centered relational paths from a frozen Wikidata-derived graph and selects clue sets that are individually necessary and jointly identify a unique answer. Using language-specific entity names, it records each task’s minimum language cover, removes direct identifiers, and renders each task as matched prompts in three languages together with its construction certificate and provenance. Across 10 state-of-the-art models, mean accuracy on CocoonBench ranges from 33.8% (DeepSeek-V4-Flash) to 69.7% (Claude-Opus-4.8), leaving even the strongest system below 70%. The same construction certificates support path-aligned trajectory analysis, enabling diagnosis of model failures in language routing, evidence-path recovery, and answer use. The result shows that escaping a language cocoon requires more than switching languages: successful cross-lingual search depends on both covering the correct languages and connecting the evidence found across them.