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arXiv:2607.25373 (cs)
[Submitted on 28 Jul 2026]

Title:Towards Bottom-Up Enumeration in miniKanren via Pruning and
Memoization

Authors:Nikolai Kudasov
View a PDF of the paper titled Towards Bottom-Up Enumeration in
miniKanren via Pruning and Memoization, by Nikolai Kudasov
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    Abstract:We present two small library combinators on top of plain
    miniKanren, designed to bring bottom-up enumeration with
    observational deduplication, the standard tool in non-relational
    program-by-example (PBE) synthesizers, into the relational
    setting. The first combinator, prune, deduplicates an answer
    stream by a user-supplied key, typically the input/output
    behavior of the candidate. The second, defrel/bank, memoizes a
    relation against canonical fresh variables so that a single
    pruned answer stream is built bottom-up and replayed at every
    call site. We also discuss a weighted variant, defrel/bank-w,
    which attaches admissible upper bounds to immature streams to
    recover best-first enumeration in cases where the natural
    depth-first canonical order misses compact representatives. On a
    preliminary PBE benchmark of arithmetic and string synthesis
    targets, defrel/bank substantially outperforms the depth-bounded
    baseline on most deep targets, while losing on a small family
    where the canonical depth-first enumeration order misses compact
    representatives. We leave a broader empirical evaluation to an
    extended version of this paper.

          18 pages, plus appendices with full source code (60 pages
Comments: total). Camera-ready for the miniKanren and Relational
          Programming Workshop 2026 (miniKanren'26)
Subjects: Programming Languages (cs.PL); Logic in Computer Science
          (cs.LO)
Cite as:  arXiv:2607.25373 [cs.PL]
          (or arXiv:2607.25373v1 [cs.PL] for this version)
          https://doi.org/10.48550/arXiv.2607.25373
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nikolai Kudasov [view email]
[v1] Tue, 28 Jul 2026 07:28:51 UTC (94 KB)
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