Parallel Dialogues: What LLM Inference Can (and Cannot) Teach Us About the Many-Worlds Interpretation — A Companion Note

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Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-15
DOI
https://doi.org/10.5281/zenodo.22759486
Primary Topic
Machine Learning in Materials Science
Type
preprint
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preprint

Parallel Dialogues: What LLM Inference Can (and Cannot) Teach Us About the Many-Worlds Interpretation — A Companion Note

Earth); Vitaly Shcherbak (Montenegro, San Francisco) Claude (Anthropic PBC
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
preprint

Parallel Dialogues: What LLM Inference Can (and Cannot) Teach Us About the Many-Worlds Interpretation — A Companion Note

Earth); Vitaly Shcherbak (Montenegro, San Francisco) Claude (Anthropic PBC
preprint en

Abstract

A companion note to "Evidence for the Many-Worlds Interpretation of Quantum Mechanics via Large Language Model Architecture" (10.5281/zenodo.20533902). Revisits the structural analogy between parallel LLM inference and Everettian branching honestly: identifies what the analogy illustrates well (shared origin, isolated states, irreversible branch selection) and where it breaks down as physics — chiefly, the analogy is externally observable in a way that MWI branches, by construction, cannot be.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Machine Learning in Materials Science
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Parallel Dialogues: What LLM Inference Can (and Cannot) Teach Us About the Many-Worlds Interpretation — A Companion Note — Earth); Vitaly Shcherbak (Montenegro, San Francisco) Claude (Anthropic PBC · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS