Scholarlink

<p><i>Texas Graffiti</i> (<i>TxGraffiti</i>) is an automated conjecturing system that operates on a finite, versioned <i>snapshot table</i> of mathematical objects equipped with precomputed numerical invariants and Boolean predicates. For a chosen target invariant and hypothesis predicate, <i>TxGraffiti</i> searches over predicate–predictor combinations and generates <i>table-true</i> conditional inequalities by fitting coefficients within simple, human-readable templates (e.g., univariate affine upper and lower bounds) via a sequence of small linear programs. The resulting candidates are ranked and compressed using heuristics that measure informativeness (e.g., touch/sharp behavior) and remove redundancy or transitive implication, producing a compact set of interpretable conjectures suitable for further testing and proof. We describe the system architecture, data curation workflow, optimization models, and filtering procedures, and we provide an interactive web interface together with an open-source Python implementation. Although our examples focus on graph invariants, the snapshot-table paradigm and optimization-and-filtering pipeline apply broadly whenever objects admit computable features and predicates.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated conjecturing with TxGraffiti

  • 期刊论文

  • 发表日期：

    2026年4月14日

查看全文
  • Randy Davila

摘要

Texas Graffiti (TxGraffiti) is an automated conjecturing system that operates on a finite, versioned snapshot table of mathematical objects equipped with precomputed numerical invariants and Boolean predicates. For a chosen target invariant and hypothesis predicate, TxGraffiti searches over predicate–predictor combinations and generates table-true conditional inequalities by fitting coefficients within simple, human-readable templates (e.g., univariate affine upper and lower bounds) via a sequence of small linear programs. The resulting candidates are ranked and compressed using heuristics that measure informativeness (e.g., touch/sharp behavior) and remove redundancy or transitive implication, producing a compact set of interpretable conjectures suitable for further testing and proof. We describe the system architecture, data curation workflow, optimization models, and filtering procedures, and we provide an interactive web interface together with an open-source Python implementation. Although our examples focus on graph invariants, the snapshot-table paradigm and optimization-and-filtering pipeline apply broadly whenever objects admit computable features and predicates.