PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
This paper introduces PolitNuggets, a multilingual benchmark for evaluating large reasoning models' ability to discover and synthesize long-tail political facts within agentic frameworks. It constructs political biographies for 400 global elites, covering over 10,000 facts. The authors propose FactNet, an evidence-conditional protocol for scoring discovery, fine-grained accuracy, and efficiency. Results show current systems struggle with fine-grained details and vary in efficiency. Diagnostics highlight the importance of short-context extraction, multilingual robustness, and reliable tool use.
[2605.14002] PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
[Submitted on 13 May 2026]
Title:PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
View a PDF of the paper titled PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts, by Yifei Zhu
View PDF HTML (experimental)
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 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
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yifei Zhu Mr. [view email] [v1] Wed, 13 May 2026 18:09:03 UTC (3,596 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts, by Yifei Zhu
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-05
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
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
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)