PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts

Computer Science > Artificial Intelligence [Submitted on 13 May 2026] PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts Yifei Zhu 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 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. Comments: 24 pages, 7 figues, accpeted in The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.14002 [cs.AI]   (or arXiv:2605.14002v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.14002 Focus to learn more Submission history From: Yifei Zhu Mr. [view email] [v1] Wed, 13 May 2026 18:09:03 UTC (3,596 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)