“Claims provides the best evaluation for scientific records at a cost that scales” Over at @ExploitSummit, Claims co-founder @nashgambit broke down the three key verification models for scientific records within #SN111, all live on mainnet. Bronze: “Inexpensive compute to compare miner submissions with reference outputs.” Silver: “Higher-quality compute used only when there is disagreement between the reference outputs and the miners.” Gold: “Human-assisted adjudication, with a live consensus loop in v1 where miners assess the hard cases Silver can’t resolve automatically.” All three are active now. See the dashboard: dashboard.claims111.ai/

“The product is validated data. We’re going to the companies we’re already talking to and enriching their data with ontologies that match their particular research questions, adding greater value.” @DeSciClaims appeared on @SubnetSummerTAO yesterday, breaking down how the subnet’s key product is to enhance data for others, through one-time fees and regular subscriptions where incoming and relevant information is consistently fed, sharpening their research. Watch the full podcast to learn about roadmaps, architecture, and incentives.

23 clinical trials had tested whether switching off a woman's ovaries helps her survive #BreastCancer. But not one could tell whether it adds anything on top of the drugs she is already taking. These cancers are fed by oestrogen. Before menopause the ovaries are the main source, so they can be taken out with surgery, closed down with radiation, or switched off with drugs. The drugs are chemotherapy and tamoxifen. Chemotherapy kills dividing cells. Tamoxifen stops the cancer from responding to oestrogen. Earlier reviews had found no clear benefit from switching off the ovaries. So the Early Breast Cancer Trialists' Collaborative Group went back for the individual records: what happened to each woman, rather than each trial's summary results. 18,851 women. That is 98.9% of every woman randomised in eligible trials of this treatment. The oldest trial began enrolling in 1948, at the Christie Hospital in Manchester (@TheChristieNHS). It was the first cancer treatment ever assessed in a randomised trial. Its long-term survival data came from @NHS records. The women were typically in their early forties when they joined, and were followed for more than a decade. The result, among women who were under 45 when they joined and were already taking tamoxifen: 9.1% of those who also had their ovaries switched off had died of breast cancer within ten years. Among those on tamoxifen alone, 11.7% had. In the older trials, where none of the women received tamoxifen, the gap was wider. Cancer came back within 15 years in 39.1% of the women whose ovaries were switched off, and in 56.5% of the women whose were not. Switching off the ovaries has costs. The women whose ovaries were switched off had consistently more severe hot flushes than the women whose were not, and it is known to cause bone loss, which can be monitored and managed. Only 4 of the 23 trials measured quality of life at all. Across the trials there was no increase in deaths from other causes, and none in second cancers. This paper went through Claims this week: 8 adjudicated claims, each tied to its evidence. This is the shape of question we want a claim-evidence graph to surface: 23 trials, one claim between them, and no single paper that settles it. Getting the answer still needs the individual patient records, the original investigators and a national mortality register. The Claims database and AI do not produce that part. But they can point to where someone should look.

Claims V0 is live on mainnet. Here’s how mining works. Every six hours, Claims puts ~50 scientific papers in front of 10 competing miners. Miners extract the claims and evidence. Validators test the work. The strongest batch earns the most weight. Here’s the full loop. 🧵

Everyone's worried about AI hallucinating sources for its facts. But that's the easy half of the problem. The hard half: when a cited paper is real, knowing whether the finding inside is too. One landmark study is titled, literally, "Why Most Published Research Findings Are False." When a pharma company went back to reproduce dozens of high-profile results, most didn't hold up. P-hacking, underpowered studies, failed replications buried in the file drawer - the errors are baked into the record itself. Retrieval doesn't solve this. It hands you the paper and repeats what the author said. It can't tell you the result was contradicted three times since, or never replicated once. And the pile keeps growing. Since ChatGPT, the rate of new submissions has increased by ~50% - more claims stacked on the same broken foundation. That's what Claims exists to fix. Not just "find the paper," but map every claim, the evidence for and against it, and how much of it actually survives contact with the rest of the field. 👉 Follow our progress on Discord: discord.gg/phE5N3uT