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AI assists in formalizing Dong-Yang classification of binary codes

By PulseAugur Editorial · [1 sources] ·

Researchers have formalized Dong and Yang's classification of optimal (n,4) binary block codes for binary symmetric channels using the Lean 4 programming language. This machine-checked proof involved feeding the original paper's proofs to an AI tool, followed by verification of the main theorem statements and axioms within Lean. The process led to corrections and simplifications of the AI-generated formalization, and also revealed discrepancies in the original paper. AI

IMPACT Demonstrates AI's utility in formal verification and mathematical proof, potentially accelerating research in theoretical computer science and related fields.

RANK_REASON The cluster describes a machine-checked proof of a classification in coding theory, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI assists in formalizing Dong-Yang classification of binary codes

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The cluster describes a machine-checked proof of a classification in coding theory, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shenghao Yang, Yanyan Dong ·

    A machine-checked proof of the Dong-Yang classification of optimal (n,4) binary codes for BSCs

    arXiv:2609.10579v1 Announce Type: cross Abstract: We present a machine-checked Lean~4 formalization of Dong and Yang's classification of optimal finite-length $(n,4)$ binary block codes for binary symmetric channels. The formalization was developed mainly by feeding the paper's p…

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