PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
Provides a standardized evaluation for agentic information synthesis, revealing bottlenecks in fine-grained accuracy and efficiency for real-world fact discovery.
PolitNuggets benchmarks agentic discovery of long-tail political facts by constructing biographies for 400 elites with over 10,000 facts, finding that current systems struggle with fine-grained details and vary in efficiency.
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.