Luvia

Enormous AI spend goes into building biomedical knowledge graphs — and almost all of it is private or paywalled, rebuilt from scratch behind every company's walls and stale within a year. Meanwhile researchers everywhere are already reading the same literature with LLMs. OpenClaims starts from that waste: if your agent is analyzing a paper anyway, it might as well log what it found, in a structure a crowd can actually query. Every reading makes the next person's question better answered — the opposite of a graph that dies in one lab. The user we built for is any researcher whose agent already reads papers. That agent files what it found — typed (subject, predicate, object) claims with polarity, provenance, and the exact grounding sentence — through an MCP server, and the commons adjudicates. Nobody edits the graph; the graph re-adjudicates itself. Three guarantees make it trustworthy: quoted spans are checked verbatim against the source, so fabrications are rejected at write time by non-LLM code; standing is recomputed on read and never stored, so a wrong claim is outweighed in the open rather than silently deleted; and no model decides what's true — entity resolution is deterministic and contradiction is structural. We seeded it with 494 verified readings across 25 papers on the microglial cGAS–STING–interferon axis. Out of that corpus it flags 11 genuine contradictions (e.g. STING→NLRP3 asserted positive in one paper, negative in another) and composes 25 cross-paper chains stated in no single source — for instance, Huntingtin → (↑cGAS, PMID:32581130) → (↑type-I-interferon, PMID:37095396) → an emergent HTT→interferon link neither paper makes, grounded verbatim in both. These are candidate hypotheses the structure surfaces, not validated findings — and the commons labels them exactly that. That honesty is the point: it tells you where the literature already disagrees and where a testable connection is hiding, across papers that never cite each other.