Paper
RL unknotter, hard unknots and unknotting number
Authors
Anne Dranowski, Yura Kabkov, Daniel Tubbenhauer
Abstract
We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for navigating Reidemeister moves. The pipeline applies to arbitrary knots and links; we test it on ``very hard'' unknot diagrams and, using diagram inflation, on $4_1\#9_{10}$ where we recover the recently established and surprising upper bound of three for the unknotting number.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.07955v1</id>\n <title>RL unknotter, hard unknots and unknotting number</title>\n <updated>2026-03-09T04:43:59Z</updated>\n <link href='https://arxiv.org/abs/2603.07955v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.07955v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for navigating Reidemeister moves. The pipeline applies to arbitrary knots and links; we test it on ``very hard'' unknot diagrams and, using diagram inflation, on $4_1\\#9_{10}$ where we recover the recently established and surprising upper bound of three for the unknotting number.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='math.GT'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <category scheme='http://arxiv.org/schemas/atom' term='stat.ML'/>\n <published>2026-03-09T04:43:59Z</published>\n <arxiv:comment>15 pages, many figures, comments welcome</arxiv:comment>\n <arxiv:primary_category term='math.GT'/>\n <author>\n <name>Anne Dranowski</name>\n </author>\n <author>\n <name>Yura Kabkov</name>\n </author>\n <author>\n <name>Daniel Tubbenhauer</name>\n </author>\n </entry>"
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