K-Bench: measuring model performance on real scientific agent requests
Quick summary
arXiv:2608.21601v2 Announce Type: replace Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference solutions, or simulators with a known generative structure. Real scientific requests arrive differently. They are underspecified, they carry attachments, and they lack ground truth. We report K-Bench 01, an evaluation built from first-turn requests sampled from live user traffic on K-Dense Web and run end to end by nine frontier models in identical sandboxes, yielding 1,602 completed agent runs. Thre
Key takeaways
- arXiv:2608.21601v2 Announce Type: replace Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference solutions, or simulators with a known generative structure.
- Real scientific requests arrive differently.
- They are underspecified, they carry attachments, and they lack ground truth.
Why it matters
“K-Bench: measuring model performance on real scientific agent requests” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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