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This research advances the snake-in-the-box problem by establishing new length records in hypercube graphs through hybrid methods combining combinatorial search with learned construction algorithms. The introduction of "snakepits" and the Beam Anchor algorithm demonstrates a practical application of machine learning to solve complex discrete optimization problems, pushing the boundaries of what is known about induced paths in dimension 9 and beyond.
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New Snake-in-the-Box Records via Snakepit Surgery and Learned Construction

arXiv2 min read

arXiv:2607.15270v3 Announce Type: cross Abstract: The snake-in-the-box problem asks for a longest induced path in the hypercube graph $Q_n$. We find a length-191 snake in dimension $n=9$, the lowest dimension where the maximum is unknown, improving the previous record of 190 that had stood for 14 years. We also establish new lower bounds in dimensions 10-13. To find these records, we introduce snakepits, collections of disjoint snakes, to expand the search space and open new routes between snakes. This motivates our new Snakepit-in-the-Box benchmark, which seeks maximal edge counts when allowing multiple components. Finally, we introduce Beam Anchor, a search-supervised learned constructor algorithm that finds 100 inequivalent length-190 snakes in dimension 9.

Abstract:The snake-in-the-box problem asks for a longest induced path in the hypercube graph $Q_n$. We find a length-191 snake in dimension $n=9$, the lowest dimension where the maximum is unknown, improving the previous record of 190 that had stood for 14 years. We also establish new lower bounds in dimensions 10-13. To find these records, we introduce snakepits, collections of disjoint snakes, to expand the search space and open new routes between snakes. This motivates our new Snakepit-in-the-Box benchmark, which seeks maximal edge counts when allowing multiple components. Finally, we introduce Beam Anchor, a search-supervised learned constructor algorithm that finds 100 inequivalent length-190 snakes in dimension 9.

Updated to include detailed information about methods. 23 pages, 4 figures

Discrete Mathematics (cs.DM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Combinatorics (math.CO)

arXiv:2607.15270 [cs.DM]

(or arXiv:2607.15270v3 [cs.DM] for this version)

https://doi.org/10.48550/arXiv.2607.15270 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 218 words

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