UQ: Assessing Language Models on Unsolved Questions
Authors: name: Fan Nie, name: Ken Ziyu Liu, name: Zihao Wang, name: Rui Sun, name: Wei Liu, name: Weijia Shi, name: Huaxiu Yao, name: Linjun Zhang, name: Andrew Y. Ng, name: James Zou, name: Sanmi Koyejo, name: Yejin Choi, name: Percy Liang, name: Niklas Muennighoff

Benchmarks shape progress in AI research. A useful benchmark should be both
difficult and realistic: questions should challenge frontier models while also
reflecting real-world usage. Yet, current paradigms face a difficulty-realism
tension: exam-style benchmarks are often made artificially difficult with
limited real-world value, while benchmarks based on real user interaction often
skew toward easy, high-frequency problems. In this work, we explore a radically
different paradigm: assessing models on unsolved questions. Rather than a
static benchmark scored once, we curate unsolved questions and evaluate models
asynchronously over time with validator-assisted screening and community
verification. We introduce UQ, a testbed of 500 challenging, diverse questions
sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi
and history, probing capabilities including reasoning, factuality, and
browsing. UQ is difficult and realistic by construction: unsolved questions are
often hard and naturally arise when humans seek answers, thus solving them
yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset
and its collection pipeline combining rule-based filters, LLM judges, and human
review to ensure question quality (e.g., well-defined and difficult); (2)
UQ-Validators, compound validation strategies that leverage the
generator-validator gap to provide evaluation signals and pre-screen candidate
solutions for human review; and (3) UQ-Platform, an open platform where experts
collectively verify questions and solutions. The top model passes UQ-validation
on only 15% of questions, and preliminary human verification has already
identified correct answers among those that passed. UQ charts a path for
evaluating frontier models on real-world, open-ended challenges, where success
pushes the frontier of human knowledge. We release UQ at
https://uq.stanford.edu.