Intelligence Is Structure, Not Scale: A Whole Central Nervous System Connectome as a Fixed Substrate for Scoring Health Data Trust

We took the wiring diagram of a fruit fly's central nervous system, held every connection and sign fixed, and trained only synaptic gains, an input projection, and a readout. The task: reproduce two SuperTruth trust scores, the Data Trust Index™ (DTI) on health records and the Behavioral Integrity Index (BII) on agent event logs. Controls: a shuffled graph, a random graph, a parameter-matched network, a linear readout, and four frontier models (Claude Opus 5, GPT-5, Grok 4, Gemini 3 Flash) given the published DTI paper and the same records. On two of five seeds, the degree-preserving shuffle matched the fly's wiring against the DTI engine (composite error 1.57 against 1.57 points (95% CI -0.67 to +0.66)); the random graph matched on tier agreement and every dimension. Every fixed graph beat the parameter-matched network by 1.8 to 2.0 points: provisional until five seeds; the substrate carries the computation. On 300 identical records, the four models matched the engine's tier on 20% to 45% at 13 s to 73 s and 4 to 26 cents per record; the fly, 84% at 16 ms and no marginal cost. Given 100 engine-scored examples as well (post hoc), they matched 54% to 76%. To our knowledge, as of 20 September 2026, this is the first reported use of a whole central nervous system connectome to score the trustworthiness of health data. Every record was synthetic; we used no real person's data. Simply put, the future of intelligence is analog. Version 1.1 is provisional: it holds the complete pre-registered pilot (seeds 1 and 2 of five, both tasks, all five controls) and the post-hoc examples arm for all four vendors; later versions add seeds 3 to 5 at the same DOI.