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        <title>PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts</title>
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            <namePart type="given">Viviane</namePart>
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    <abstract>Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long-context question answering into open-ended exploration. Yet real-world use requires models to discover and synthesize “long-tail” facts from dispersed sources, a capability that remains under-evaluated. We introduce PolitNuggets, a multilingual benchmark for agentic information synthesis via constructing political biographies for 400 global elites, covering over 10000 political facts. We standardize evaluation with an optimized Supervisor–Searcher multi-agent system and propose FactNet, an evidence-conditional protocol that scores discovery, fine-grained accuracy, and efficiency. Across models and settings, we find that current systems often struggle with fine-grained details, and vary substantially in efficiency. Finally, using benchmark diagnostics, we relate agent performance to underlying model capabilities, highlighting the importance of short-context extraction, multilingual robustness, and reliable tool use.</abstract>
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%0 Conference Proceedings
%T PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
%A Zhu, Yifei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhu-2026-politnuggets
%X Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long-context question answering into open-ended exploration. Yet real-world use requires models to discover and synthesize “long-tail” facts from dispersed sources, a capability that remains under-evaluated. We introduce PolitNuggets, a multilingual benchmark for agentic information synthesis via constructing political biographies for 400 global elites, covering over 10000 political facts. We standardize evaluation with an optimized Supervisor–Searcher multi-agent system and propose FactNet, an evidence-conditional protocol that scores discovery, fine-grained accuracy, and efficiency. Across models and settings, we find that current systems often struggle with fine-grained details, and vary substantially in efficiency. Finally, using benchmark diagnostics, we relate agent performance to underlying model capabilities, highlighting the importance of short-context extraction, multilingual robustness, and reliable tool use.
%R 10.18653/v1/2026.acl-long.2085
%U https://aclanthology.org/2026.acl-long.2085/
%U https://doi.org/10.18653/v1/2026.acl-long.2085
%P 45012-45035

Markdown (Informal)

[PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts](https://aclanthology.org/2026.acl-long.2085/) (Zhu, ACL 2026)

• PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts (Zhu, ACL 2026)

ACL

• Yifei Zhu. 2026. PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45012–45035, San Diego, California, United States. Association for Computational Linguistics.