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9 K-Dense articles on Benchmarks — research automation, benchmarks, and case studies from the K-Dense team.
K-Bench 01 is our internal benchmark built from 178 real scientific tasks. Nine frontier models ran them end to end; only one clears the acceptability bar.
A 20-task benchmark found K-Dense Web stronger at producing auditable research bundles, while Claude Science held a narrow scientific-quality lead.
A 35-case K-Dense benchmark of Google's Omni Flash shows stunning scientific video quality, but unreliable text, physics, and factual correctness.
A 240-image benchmark of four scientific image models shows Nano Banana 2 Lite is fastest, while GPT Image 2 still leads on raw figure quality.
The next AI scientist benchmark should be a lab escape room: a fresh hidden world, limited probes, real evidence, and no place for science theater to hide.
AI is flooding science with claims no one can check. The quieter story: agents now reproduce most published findings — and that is the bigger deal.
A reproducible 250-run study of feature detection, adduct grouping, quantification, and identification, run by an AI agent with and without the pyOpenMS skill.
A reproducible study of pKa, logD, tautomers, ADME, and docking run by an AI agent with the Rowan skill, measured against RDKit and experimental ground truth.
K-Dense Web scored 45/50 on BixBench-Verified-50, a cleaned biology-agent benchmark designed to separate real model mistakes from benchmark noise.
