Thanks for sharing this experiment. I re-analysed the published material at claim level using DESi and then subjected the DESi analysis itself to a rule-guided Doktores self-audit.
The purpose was not to judge the legal correctness of the Muse Spark response, but to examine the epistemic structure and traceability of the claimed validation.
The main findings were:
23 curated claims were extracted, typed and anchored.
Only 4 of 23 had domain-admissible evidence in the supplied material.
18 of 23 had no concrete evidence passage attached.
One genuine contradiction was found between the stated method and the printed prompt: the method says that no verification, source or stage instructions were given, while the prompt explicitly requests references, direct quotations, citation checking and six named phases.
Two additional issues were classified more cautiously: one as a pipeline inconsistency — VERIFIED without citable evidence — and one as an unsubstantiated claim of validator independence.
The final “Epistemic Boundary” conclusion appears self-sealing as presented: both validator success and validator failure are interpreted as confirmation, while no explicit falsification condition is specified.
The analysis also recognises genuine strengths in MarCognity-AI, including claim decomposition, multi-source retrieval and an explicit skeptical pass. The central problem is not the idea, but source-domain gating, provenance and the absence of concrete claim-to-evidence bindings.
The full report, including all 23 claims, the evidence classifications, the three structural conflicts, limitations and the Doktores self-audit, is available here:
I would be particularly interested in your response to the three conflicts discussed on page 2. It would also help reproducibility if the exact PubMed document — or the complete source bundle supplied to the validator — were made available.