{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OFUJ2OFABKXHLBZAPUZQFNSDGL","short_pith_number":"pith:OFUJ2OFA","schema_version":"1.0","canonical_sha256":"71689d38a00aae7587207d3302b64332ff55bddad137ea2695a839dd1315b1b7","source":{"kind":"arxiv","id":"2401.05451","version":1},"attestation_state":"computed","paper":{"title":"OpenSkill: A faster asymmetric multi-team, multiplayer rating system","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Vivek Joshy","submitted_at":"2024-01-09T16:23:04Z","abstract_excerpt":"Assessing and comparing player skill in online multiplayer gaming environments is essential for fair matchmaking and player engagement. Traditional ranking models like Elo and Glicko-2, designed for two-player games, are insufficient for the complexity of multi-player, asymmetric team-based matches. To address this gap, the OpenSkill library offers a suite of sophisticated, fast, and adaptable models tailored for such dynamics. Drawing from Bayesian inference methods, OpenSkill provides a more accurate representation of individual player contributions and speeds up the computation of ranks. Th"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.05451","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-01-09T16:23:04Z","cross_cats_sorted":[],"title_canon_sha256":"a5f77443401603e7e2302a814ae727950492bb85ea095026ea21ebfaded2116d","abstract_canon_sha256":"95da0a017975b270901dfa51def6605405c86290fd04ed285d6f1c84bf46a336"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:32:30.402173Z","signature_b64":"D3GqKlSd79m4UYQbGGjDrO2/29T84U6MgIL7RYS6lpDDpyxxiMdSx2Zb7gYeV/6E0Z9FyZaJnY1C4K4Q1BXsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71689d38a00aae7587207d3302b64332ff55bddad137ea2695a839dd1315b1b7","last_reissued_at":"2026-07-05T07:32:30.401683Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:32:30.401683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenSkill: A faster asymmetric multi-team, multiplayer rating system","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Vivek Joshy","submitted_at":"2024-01-09T16:23:04Z","abstract_excerpt":"Assessing and comparing player skill in online multiplayer gaming environments is essential for fair matchmaking and player engagement. Traditional ranking models like Elo and Glicko-2, designed for two-player games, are insufficient for the complexity of multi-player, asymmetric team-based matches. To address this gap, the OpenSkill library offers a suite of sophisticated, fast, and adaptable models tailored for such dynamics. Drawing from Bayesian inference methods, OpenSkill provides a more accurate representation of individual player contributions and speeds up the computation of ranks. Th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05451","kind":"arxiv","version":1},"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/2401.05451/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.05451","created_at":"2026-07-05T07:32:30.401750+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.05451v1","created_at":"2026-07-05T07:32:30.401750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05451","created_at":"2026-07-05T07:32:30.401750+00:00"},{"alias_kind":"pith_short_12","alias_value":"OFUJ2OFABKXH","created_at":"2026-07-05T07:32:30.401750+00:00"},{"alias_kind":"pith_short_16","alias_value":"OFUJ2OFABKXHLBZA","created_at":"2026-07-05T07:32:30.401750+00:00"},{"alias_kind":"pith_short_8","alias_value":"OFUJ2OFA","created_at":"2026-07-05T07:32:30.401750+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.21684","citing_title":"Incentivizing Permissionless Distributed Learning of LLMs","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL","json":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL.json","graph_json":"https://pith.science/api/pith-number/OFUJ2OFABKXHLBZAPUZQFNSDGL/graph.json","events_json":"https://pith.science/api/pith-number/OFUJ2OFABKXHLBZAPUZQFNSDGL/events.json","paper":"https://pith.science/paper/OFUJ2OFA"},"agent_actions":{"view_html":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL","download_json":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL.json","view_paper":"https://pith.science/paper/OFUJ2OFA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.05451&json=true","fetch_graph":"https://pith.science/api/pith-number/OFUJ2OFABKXHLBZAPUZQFNSDGL/graph.json","fetch_events":"https://pith.science/api/pith-number/OFUJ2OFABKXHLBZAPUZQFNSDGL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL/action/storage_attestation","attest_author":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL/action/author_attestation","sign_citation":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL/action/citation_signature","submit_replication":"https://pith.science/pith/OFUJ2OFABKXHLBZAPUZQFNSDGL/action/replication_record"}},"created_at":"2026-07-05T07:32:30.401750+00:00","updated_at":"2026-07-05T07:32:30.401750+00:00"}