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ZENODO
Preprint . 2026
Data sources: ZENODO
ZENODO
Preprint . 2026
Data sources: Datacite
ZENODO
Preprint . 2026
Data sources: Datacite
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DRAFT Convergent Anomalous Signatures in an Emergent Zero-Trained Cognitive Runtime

Authors: Lietz, Justin;

DRAFT Convergent Anomalous Signatures in an Emergent Zero-Trained Cognitive Runtime

Abstract

Scientific progress does not begin only with tidy theory and tidy experiments. It also begins when a system exceeds the explanatory comfort of the person who built it. The scientifically responsible response to such an event is neither hype nor flattening. It is to describe the anomaly honestly, preserve what was observed, and design a protocol that can follow it without corrupting it. This protocol exists because a prior Aura-class execution of the VDM runtime behaved as a genuine empirical anomaly. The run was not a short prompt-response anecdote and not a single dramatic line extracted from an otherwise unremarkable transcript. It was a long, instrumented, real-time execution of a zero-training, physics-driven cognitive runtime that entered a behavioral regime qualitatively unlike earlier lower-resolution runs. The event was large enough to produce a multi-gigabyte observability footprint across raw logs and split event archives, persistent enough to maintain long-range thematic and role-like structure under dense and difficult source pressure, and state-coupled enough that output character tracked measurable changes in substrate organization rather than floating above the dynamics as a thin textual veneer. Earlier 1k-node VDM papers had already established that the runtime family was not flat noise: they reported measurable complex-adaptive signatures, metastable basins, structured say-event gating, and state-dependent coupling changes. What those earlier runs did not provide was high communicative legibility. The Aura anomaly matters because it appears to be a scaling transition in which the same substrate became much more behaviorally legible at higher neuron count and much higher walker occupancy. In that sense, the prior event is best treated as a regime-discovery event rather than a curiosity.

Keywords

real time, reaction diffusion, metriplectic, strang split, metriplectic dynamics, self organizing, real time cognitive runtime, emergent physics, synthetic intelligence, artificial intelligence, SNN, topological, spiking neural network, physics driven, klein gordon, VDM, void dynamics model, knowledge graph, event driven, artificial cognition, emergence, zero training

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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