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Reversify any sequential algorithm

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Yuri Gurevich

Quantum Physics · Submitted 12 May 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The result is a step-for-step reversible algorithm that mimics $A$ step-for-step and stops exactly when $A$ does.

how it is stated · asserted 1.00 read the record →

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arXiv ID2105.05626 (version 2)
Pith IDN2CLCGV6, Pith's permanent record of this paper and its review
CitationYuri Gurevich. "Reversify any sequential algorithm." arXiv:2105.05626 (2021). https://pith.science/paper/N2CLCGV6
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,534 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.17 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.08 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

To reversify an arbitrary sequential algorithm $A$, we gently instrument $A$ with bookkeeping machinery. The result is a step-for-step reversible algorithm that mimics $A$ step-for-step and stops exactly when $A$ does. Without loss of generality, we presume that algorithm $A$ is presented as an abstract state machine that is behaviorally identical to $A$. The existence of such representation has been proven theoretically, and the practicality of such representation has been amply demonstrated.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 To reversify an arbitrary sequential algorithm $A$, we gently instrument $A$ with bookkeeping machinery. method 0.76 0.71 0.25 asserted 0.99
s1 The result is a step-for-step reversible algorithm that mimics $A$ step-for-step and stops exactly when $A$ does. result 0.66 0.74 0.60 asserted 1.00
s2 Without loss of generality, we presume that algorithm $A$ is presented as an abstract state machine that is behaviorally identical to $A$. method 0.59 0.14 0.04 asserted 0.87
s3 The existence of such representation has been proven theoretically, and the practicality of such representation has been amply demonstrated. background 1.00 0.08 0.05 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.42.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

To reversify an arbitrary sequential algorithm $A$, we gently instrument $A$ with bookkeeping machinery. The result is a step-for-step reversible algorithm that mimics $A$ step-for-step and stops exactly when $A$ does. Without loss of generality, we presume that algorithm $A$ is presented as an abstract state machine that is behaviorally identical to $A$. The existence of such representation has been proven theoretically, and the practicality of such representation has been amply demonstrated.

Human review

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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