Pith. sign in

REVIEW 2 cited by

Using binary string to prove the Collatz conjecture

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.00001 v3 pith:JX2XRG3Z submitted 2023-09-20 math.GM

classification math.GM
keywords collatznumbersbinaryconjecturesequencefunctionnaturalpure
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce a full binary directed tree structure to represent the set of natural numbers, further categorizing them into three distinct subsets: pure odd numbers, pure even numbers, and mixed numbers. We adopt a binary string representation for natural numbers and elaborate on the composite methodology encompassing odd- and even-number functions. Our analysis focuses on examining the iteration sequence (or composition) of the Collatz function and its reduced variant, which serves as an analog to the inverse function, to scrutinize the validity of the Collatz conjecture. To substantiate this conjecture, we incorporate binary strings into an algebraic formula that captures the essence of the Collatz sequence. By this means, we transform discrete powers of 2 into continuous counterparts, ultimately culminating in the smallest natural number, 1. Consequently, the sequence generated through infinite iterations of the Collatz function emerges as an eventually periodic sequence, thereby validating an enduring 87-year-old conjecture.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NM-Hebb: Coupling Local Hebbian Plasticity with Metric Learning for More Accurate and Interpretable CNNs

    cs.LG 2025-08 conditional novelty 5.0 of 10

    NM-Hebb combines a Hebbian activation-weight alignment regulariser, a loss-gated neuromodulator, and a metric fine-tuning phase to improve CNN accuracy and embedding compactness.

  2. RL-Struct: A Lightweight Reinforcement Learning Framework for Reliable Structured Output in LLMs

    cs.AI 2025-11 conditional novelty 4.0 of 10

    RL fine-tuning with a syntax-first weighted reward substantially raises JSON structural validity of a small LLM, though the advantage over PPO is within noise.

Pith tools