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PreprintarXiv licencearXiv

What a collective can hold in common: a gauge framework for private representations

Abstract

Many models of collective behavior write headings, beliefs, and meanings in one experimenter-defined frame. Organisms do not live there: each represents the world in a private space, and comparison requires translation. Consensus becomes an existence problem before it becomes a dynamical one. Reciprocal pairwise translations need not compose consistently around a loop. This return transformation (the holonomy) can expose a mismatch no isolated reciprocal pair can carry. We develop a gauge-covariant theory in which physical predictions are invariant under private relabeling. For reciprocal unitary translations, the kernel of the connection Laplacian is isomorphic to the joint fixed space of loop holonomies: a collective can hold in common exactly what all its loops leave unchanged. A blind-subgroup criterion identifies invisible defects. Common-frame comparison, such as in traditional models of collective behaviour, occupies the flat sector. For independent uniform translations from a finite group G, a connected graph with E relations among N individuals is flat with probability |G|−(E−N+1). Exact cycle spectra determine persistence under linear relaxation. The consequence is operational as well as structural. For unrecorded routes under a specified model with isotropic Gaussian source and readout noise, we derive the smallest mean-squared error achievable by any decoder. At fixed signal-to-noise ratio, the jointly preserved feature fraction fixes its long-route limit. Networks with identical loop angles can differ in recoverable content when their preserved axes differ. The relational geometry of a collective sets both the states it can share and the content it can recover.

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BibTeX
@misc{salahshour2026what,
  title = {{What a collective can hold in common: a gauge framework for private representations}},
  author = {Mohammad Salahshour},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.23066},
  eprint = {2609.23066},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.23066},
}
RIS
TY  - UNPB
TI  - What a collective can hold in common: a gauge framework for private representations
AU  - Mohammad Salahshour
PY  - 2026
DA  - 2026/09/19
PB  - arXiv
DO  - 10.48550/arxiv.2609.23066
UR  - https://arxiv.org/abs/2609.23066
AB  - Many models of collective behavior write headings, beliefs, and meanings in one experimenter-defined frame. Organisms do not live there: each represents the world in a private space, and comparison requires translation. Consensus becomes an existence problem before it becomes a dynamical one. Reciprocal pairwise translations need not compose consistently around a loop. This return transformation (the holonomy) can expose a mismatch no isolated reciprocal pair can carry. We develop a gauge-covariant theory in which physical predictions are invariant under private relabeling. For reciprocal unitary translations, the kernel of the connection Laplacian is isomorphic to the joint fixed space of loop holonomies: a collective can hold in common exactly what all its loops leave unchanged. A blind-subgroup criterion identifies invisible defects. Common-frame comparison, such as in traditional models of collective behaviour, occupies the flat sector. For independent uniform translations from a finite group G, a connected graph with E relations among N individuals is flat with probability |G|^−(E−N+1). Exact cycle spectra determine persistence under linear relaxation. The consequence is operational as well as structural. For unrecorded routes under a specified model with isotropic Gaussian source and readout noise, we derive the smallest mean-squared error achievable by any decoder. At fixed signal-to-noise ratio, the jointly preserved feature fraction fixes its long-route limit. Networks with identical loop angles can differ in recoverable content when their preserved axes differ. The relational geometry of a collective sets both the states it can share and the content it can recover.
ER  -