Each post is a discovery. IRDME is the method — the finding is the story. All results link to their pre-registered hash.
MathComp was run as a pre-registered formal-corpus follow-up with corrected h2 schema fields. Result: h1 DENIED (near-zero cross-layer persistence), h2 PARTIAL, h3 CONFIRMED (role divergence), indicating a boundary-style structural regime rather than a clean persistence replication.
The Coq Corelib replication (n=17) produced a positive directional result consistent with Lean/mathlib4: degree r=0.2875 (positive, p=0.2874), with cross-layer analysis showing moderate persistence (r=0.4913) and betweenness persistence r=0.5091. This is an underpowered but important independent formal-systems replication.
The ISCAS85 c432 replication (n=196) confirmed the pre-registered betweenness-persistence hypothesis with r=0.4255 and p=0.002, while degree persistence remained weak (r=0.2138). This extends the digital logic result from the 4-bit adder to a standard benchmark circuit.
X9 v2 reran the prebiotic-chemistry program on a published external autocatalytic network from Xavier et al. 2020 via the CatReNet example-9 dataset. The result was 4/4 CONFIRMED, which upgrades X9 from a model-based signal to an externally replicated one.
Three new IRDME results now point in the same direction: structure is portable across substrates. A worm predicts a fly, topology extracts logic from Lean4, and prebiotic chemistry already shows layered structural organization. For a startup, that means dataset certification, structural priors, and generative compression become products, not only papers.
X9 tested origin-of-life prebiotic chemistry as a three-layer graph: bond chemistry, autocatalytic membership, and replication co-occurrence. The pre-registered run confirmed 3/4 hypotheses, including the FPL rank ordering in this domain.
In Lean4 mathlib4, declared proof dependencies and behavioral proof coupling show strong hub persistence (r=0.777, p=0.004). The result supports a stronger claim: topology does not just describe formal systems, it exposes their operational logic.
We trained a generative model on a 302-neuron worm brain. Then we pointed it at a 3036-neuron fly brain it had never seen. It correctly predicted which neurons would be hubs -- with cosine similarity above 0.94. Pre-registered, confirmed.
Pre-registered experiment H_PRIMITIVITY: 15 IRDME-tested domains as nodes, three layers encoding formal mathematical dependencies, structural coupling class, and FPL confirmation outcome. Key finding: mathematical formalization does not significantly predict whether the law confirms (r=0.37, p=0.21 ns). A potential fourth boundary condition identified.
A pattern confirmed across 16 different domains: the nodes most likely to cause catastrophic failure are structurally invisible to the models designed to find them. We call this the hub shadow.