Benchmarks with Benefits: What DeepScholar-Bench Really Measures
TL;DR for operators DeepScholar-Bench is useful because it turns “deep research” from a demo category into a measurable workflow: retrieve the right sources, synthesize the right facts, and attach citations that actually support the claims.1 The headline result is not flattering. No evaluated system exceeds a 31% geometric mean across all metrics. OpenAI DeepResearch leads overall with a 0.309 geometric mean, but its best-looking strengths hide serious gaps: 0.857 on organization, 0.392 on nugget coverage, 0.187 on reference coverage, and 0.124 on document importance. Translation: the report may read well while still missing the intellectual furniture. ...