We present Gnosis AI, the first artificial intelligence system that autonomously discovers new structural knowledge by synthesising established results across independent scientific domains. The system implements Convergent Descent — a methodology developed across 15 prior research papers — as executable code: it surveys fields of science and mathematics, extracts structural conclusions from established results, detects convergences where independent fields arrive at the same structural finding, validates each discovery through multi-dimensional Epistemic Assurance, and iteratively meta-converges the convergences themselves until an irreducible fixed point is reached. We validate Gnosis AI through three progressive tests. Test 1 (Guided mode, 3 domains, 0. 57) confirmed the pipeline executes end-to-end and correctly identifies a known formal convergence. Test 2 (Exploration mode, 5 mathematics domains, 1. 81) produced 30 cross-domain convergences and a complete meta-convergence cascade across 5 levels, terminating at the fixed point "Reality is constraint. " Test 3 (Auto mode, all 14 physics fields, 26. 61) ran fully autonomously for 2 hours and 11 minutes, surveying 220 established results, exploring all 91 domain pairs, discovering 235 validated convergences, and producing a 5-level meta-convergence cascade terminating at the fixed point "Reality fundamentally operates through dimensional compression at constraint-context boundaries. " Across all three tests, the system produced 266 validated convergences (113 formal, 153 structural analogies), 26 meta-convergence findings across 5 levels, 18 coined structural terms, and 2 independent fixed-point principles — from independent input domains (mathematics and physics respectively). The total cost was 28. 99 across 696 API calls. To the best of our knowledge, no prior AI system has demonstrated autonomous cross-domain structural knowledge discovery at this scale. All source code, test data, individual convergence files, meta-convergence findings, and discovery reports are published with Bitcoin-timestamped provenance.
Mark E. Mala (Fri,) studied this question.