From Formulation to Articulation: Considerations for AI-Assisted Research in Information Theory
Abstract
Recent advances in artificial intelligence have contributed to several information-theoretic results through genuinely different modes: a language model may suggest a proof, participate in an extended mathematical dialogue, coordinate computation, or operate as one component of a search harness. Treating all of these as "AI-discovered mathematics" obscures distinctions that matter for scientific reliability, attribution, and reproducibility. This article examines a set of recent cases through a four-stage pipeline: how the problem was formulated; how AI participated in finding a solution; how the resulting analytical and computational claims were verified; and how machine-generated outputs were translated into mathematical exposition for human understanding. Based on these cases, we recommend that research publications (1) disclose the nature and extent of AI assistance at each stage, (2) report verification of analytical reasoning separately from certification of numerical computations, and (3) place more emphasis on articulating the underlying ideas, techniques, and interpretations.
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BibTeX
@misc{tian2026from,
title = {{From Formulation to Articulation: Considerations for AI-Assisted Research in Information Theory}},
author = {Chao Tian and Krishna Narayanan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.25490},
eprint = {2610.25490},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.25490},
}
RIS
TY - UNPB
TI - From Formulation to Articulation: Considerations for AI-Assisted Research in Information Theory
AU - Chao Tian
AU - Krishna Narayanan
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2610.25490
UR - https://arxiv.org/abs/2610.25490
AB - Recent advances in artificial intelligence have contributed to several information-theoretic results through genuinely different modes: a language model may suggest a proof, participate in an extended mathematical dialogue, coordinate computation, or operate as one component of a search harness. Treating all of these as "AI-discovered mathematics" obscures distinctions that matter for scientific reliability, attribution, and reproducibility. This article examines a set of recent cases through a four-stage pipeline: how the problem was formulated; how AI participated in finding a solution; how the resulting analytical and computational claims were verified; and how machine-generated outputs were translated into mathematical exposition for human understanding. Based on these cases, we recommend that research publications (1) disclose the nature and extent of AI assistance at each stage, (2) report verification of analytical reasoning separately from certification of numerical computations, and (3) place more emphasis on articulating the underlying ideas, techniques, and interpretations.
ER -