A short, honest note for anyone reading closely. Parts of this site describe an earlier chapter of the work — the never-launder reasoning engine, the drug-interaction database that shows its
Here is the failure mode that should scare anyone who ships clinical decision support: a drug-interaction checker that, when it doesn't know, quietly returns nothing . No alert. No flag.
Ask a commercial interaction checker about a three-drug problem and you'll get, at best, three separate two-drug answers. That's not a limitation of the vendor's diligence — it's a limitation
Most drug-interaction alerts arrive as a verdict with no receipt. "Major interaction." Says who? Based on what? A clinician can't tell, so — under time pressure, for the hundredth alert
0% hallucination on grounded queries. Not reduced. Not minimized. Zero — and the scope in that sentence is the whole claim, not a hedge attached to it. On a query
148,000 to 197,000 knowledge triples in 8 hours. That's a +33% knowledge growth rate during a single autonomous tutoring session — and not one of those triples was written by
9 out of 9 hypotheses validated. Zero hallucination. 98% routing accuracy. P95 latency of 7.7 milliseconds — 13x under our 100ms target. And 100% LLM call elimination for known facts.
Zero safety violations. Zero false refusals. 0.0013ms routing overhead. That is what happens when you stop treating AI safety as a single threshold and start treating it as a context-sensitive
85.7% to 100% error elimination on repeated tasks. 4.8% Expected Calibration Error on self-assessment. 53% reduction in dispatch logic. These are not three separate results from three separate systems. They
5 hypotheses. 5 failures. 0 out of 5 quantitative targets met. We are publishing this paper anyway, because the whole point of Hypothesis-Driven Development is that failure is data. And