JAI SOGANI

Keystone is a Claude-powered discovery engine for life sciences that verifies evidence before it reasons. Across four real disease programs — immunology (CD4+ T-cell/Th2), glioblastoma, brain hemorrhage, and diabetes — built on real literature, real retraction records, and real Perturb-seq data from Gladstone Institutes, a coordinated cell of Claude agents (planner, evidence-quality checker, contradiction miner, adversarial reviewer, principal investigator) proposes and ranks competing hypotheses. But every claim is gated by a deterministic integrity check first — nothing reaches a conclusion unverified. Scientists can also feed in their own reference list, their own bench data (plate-reader CSVs), or any gene symbol, and get the same live integrity-and-reasoning pipeline run on their own work, not a curated demo. What we found: A gated, reviewer-checked pipeline of 8 agents admits zero unverified claims into its final recommendation; an ungated swarm of 300 agents running the identical task cites unverified sources at real, measured cost — proof that more agents isn't better science, verification is. Our load-bearing citation classifier agrees with human annotators at 0.82, inside the human-to-human agreement band (0.69–0.75) on the same task. And the system caught a real, still-uncorrected problem in the wild: a foundational glioblastoma paper is retracted, and a widely used cell line (U-87MG) is independently flagged as misidentified — Keystone surfaces both automatically before letting any hypothesis build on them. Why it matters: The bottleneck in AI for science was never generating hypotheses — it's knowing what not to trust. Scientists routinely build years of work on literature that later turns out compromised, and most AI research tools make this worse by confidently citing sources they never checked. Keystone inverts that default: every recommendation ships with its own falsification condition, every claim traces to a source you can open, and the AI is built to downgrade its own confidence — live, in front of you — the moment the evidence doesn't hold up.

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