{"paper":{"title":"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DM","cs.SI","math.CO","math.GR"],"primary_cat":"cs.LG","authors_text":"A. Chervov, A. Dolgorukova, A. Eliseev, A. Korolkova, A. Lukyanenko, A. Ogurtsov, A. Romanov, A. Rozanov, A. Soibelman, A. Sychev, D. Gorodkov, D. Mamayeva, F. Petrov, G. Antiufeev, G. Verbii, I. Kiselev, I. Koltsov, K. Khoruzhii, L. Cheldieva, L. Grunvald, L. Shishina, M. Evseev, M. Obozov, N. Narynbaev, N. Rokotyan, R. Turtayev, S. Ermilov, S. Fironov, S. Kovalev, S. Krymskii, S. Lytkin, S. Nikolenko, V. Nelin, V. Zamkovoy","submitted_at":"2025-02-25T21:53:41Z","abstract_excerpt":"This paper is the second in a series of studies on developing efficient artificial intelligence-based approaches to pathfinding on extremely large graphs (e.g. $10^{70}$ nodes) with a focus on Cayley graphs and mathematical applications. The open-source CayleyPy project is a central component of our research. The present paper proposes a novel combination of a reinforcement learning approach with a more direct diffusion distance approach from the first paper. Our analysis includes benchmarking various choices for the key building blocks of the approach: architectures of the neural network, gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.18663","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.18663/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}