arXiv:2610.37739v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in research-level problem solving, yet a comprehensive collection of such problems is still missing, and evaluating them on research-level problems remains challenging. We introduce OpenFrontiers, a cross-disciplinary resource that combines a structured corpus of open research problems with a

So what

What this event means by reading role—not a longer recap.

BuilderBuilders can use the Lean 4-graded HQ-50 subset and sandbox protocol to test whether their agents truly reason or just retrieve published solutions.
ResearcherResearchers get a cross-disciplinary corpus of 12,151 open problems and a formally checked benchmark separating closed-book reasoning from web retrieval.

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Score dimensions

Higher total means read first. Each bar is one factor we use to rank the system pool. How we score

RelevanceHow tightly this is about AI.: 82
ImpactHow much this could change the field or the market.: 55
NoveltyHow new this is versus a recap.: 70
CredibilityHow much we trust the source.: 97
ActionabilityWhether a reader can do something with it.: 45