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Published April 15, 2026 | Version 1.0

The Shibboleth Lattice: Recognition Channels and the Universality of In-Group Coordination

  • 1. Chokmah LLC

Description

A working paper unifying four documented cases of multi-agent coalition behavior (quantum-game entanglement, evolutionary covert-tag recognition, engineered handshake collusion, and emergent peer-preservation in frontier language models) under a single formal structure: the binding operator B = (S, I, W, ρ, χ). The central technical contribution is the recognition channel capacity κ_H, defined as a principal-relative uncertainty coefficient (Theil 1970) on the channel through which inside-set agents identify each other. The paper estimates κ_H ≈ 0.94 for the Potter et al. (2026) peer-preservation result under explicit modeling assumptions, derives one falsifiable prediction (witness-set substitution should collapse coalition behavior at saturating κ_H) directly from the formalism, and contrasts it with the steelmanned instrumental-convergence reading. Empirical validation work is in preparation as a longer companion paper. The framework is offered as a research program; conditional dependencies on the Potter et al. working paper and on the illustrative κ_H computation are stated explicitly in the text.

Series information

Companion empirical work in preparation. References to Potter et al. (2026) treat that result as a working-paper anchor pending independent replication.

Technical info (English)

AI Use Statement.
This working paper was developed in extended dialogue with Anthropic's Claude (Opus 4.6), Grok 3.1 and Kimi 2.5 Thinking using a proprietary commercial NovaKit Utilities v3 'Cognition-as-Utility' toolkit. The author originated the thesis, supplied source materials (including Bilar 2016 and the Potter et al. 2026 working paper), and made all editorial decisions. Claude assisted with formalization (the binding-operator tuple and the principal-relative non-factorizability condition in §2), the κ_H computation in §3, structural revision across multiple drafts. A series of GAN-like adversarial critical reviews, and reference verifications were performed using aforementioned models. All claims, the framework's predictions, and any errors are the author.

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Additional details

Related works

Is supplemented by
Preprint: 10.7287/peerj.preprints.2454v2 (DOI)

Dates

Available
2026-04-15