论文概要
研究领域: ML 作者: Yifei Zhu 发布时间: 2026-05-17 arXiv: 2505.12349
中文摘要
嵌入智能体框架中的大型推理模型（LRM）已将信息检索从静态的长上下文问答转变为开放式探索。然而，现实世界的使用要求模型从分散的来源中发现和合成长尾事实，这一能力仍未得到充分评估。我们引入PolitNuggets，一个多语言基准测试，通过构建400位全球精英的政治传记来评估智能体信息合成，涵盖超过10000条政治事实。我们使用优化的多智能体系统标准化评估，并提出FactNet，一种证据条件协议，对发现能力、细粒度准确性和效率进行评分。跨模型和设置，我们发现当前系统经常在细粒度细节方面遇到困难，效率差异显著。最后，使用基准诊断，我们将智能体性能与底层模型能力相关联，突出短上下文提取、多语言鲁棒性和可靠工具使用的重要性。
原文摘要
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 substan...
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