LoHoSearch: Benchmarking Long-Horizon Search Agents Beyond the Human Difficulty Ceiling
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Computer Science > Computation and Language
Title:LoHoSearch: Benchmarking Long-Horizon Search Agents Beyond the Human Difficulty Ceiling
Abstract:Search agent benchmarks exemplified by BrowseComp have rapidly saturated over the past year, with the strongest models surpassing 90% accuracy. Since these benchmarks are predominantly human-authored, annotators lack a global perspective on entity statistics and cannot systematically maximize search space size and structural complexity. This creates a difficulty ceiling that is hard to break. To address this, we introduce LoHoSearch (Long-Horizon Search Agents), a challenging benchmark comprising 544 human-verified questions across 11 domains. LoHoSearch is constructed via an automated pipeline built upon a knowledge graph covering over 7 million Wikipedia entities, which selects relations with large search spaces and assembles them into structurally complex questions with KG-verified unique answers. Our evaluation demonstrates that even the strongest model achieves only 34.74% accuracy, and existing context management strategies (best +6.8%) yield far smaller gains than on prior benchmarks. LoHoSearch provides a more demanding standard for evaluating long-horizon reasoning and context management in search agents.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.12837 [cs.CL] |
| (or arXiv:2606.12837v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12837
arXiv-issued DOI via DataCite (pending registration)
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