{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5XAOCDPB6FUF3H7KULB7X4NGYL","short_pith_number":"pith:5XAOCDPB","schema_version":"1.0","canonical_sha256":"edc0e10de1f1685d9feaa2c3fbf1a6c2fd02954fad0e4db7162d50520a9979d8","source":{"kind":"arxiv","id":"2209.14110","version":2},"attestation_state":"computed","paper":{"title":"Meta-Learning in Games","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GT","authors_text":"Gabriele Farina, Ioannis Anagnostides, Keegan Harris, Mikhail Khodak, Tuomas Sandholm, Zhiwei Steven Wu","submitted_at":"2022-09-28T13:57:25Z","abstract_excerpt":"In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from routing problems to online advertising auctions -- evolve dynamically, thereby leading to many similar games to be solved. To address this gap, we introduce meta-learning for equilibrium finding and learning to play games. We establish the first meta-learning guarantees for a variety of fundamental and well-studied classes of games, including two-player zero-sum games, general-sum games, and Stackelberg games. In part"},"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":"2209.14110","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2022-09-28T13:57:25Z","cross_cats_sorted":[],"title_canon_sha256":"14d666696f188e92b2a01a748e5aa87f23d0e3d91e6509607ca24c275124bee1","abstract_canon_sha256":"16b39d9cc4f388f3dddc57d00a3297eb39042392e3fa51633b4ec8e73681179a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:04.050391Z","signature_b64":"Cbb0RNpZbGs1VRzErXVhXF+PLK6MvOEbiWIH9eqml6pqUIiX7h1GuRb4jVNmVdPlf+RQ9eYOAuVYxm79ageKAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"edc0e10de1f1685d9feaa2c3fbf1a6c2fd02954fad0e4db7162d50520a9979d8","last_reissued_at":"2026-07-05T05:47:04.049895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:04.049895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta-Learning in Games","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GT","authors_text":"Gabriele Farina, Ioannis Anagnostides, Keegan Harris, Mikhail Khodak, Tuomas Sandholm, Zhiwei Steven Wu","submitted_at":"2022-09-28T13:57:25Z","abstract_excerpt":"In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from routing problems to online advertising auctions -- evolve dynamically, thereby leading to many similar games to be solved. To address this gap, we introduce meta-learning for equilibrium finding and learning to play games. We establish the first meta-learning guarantees for a variety of fundamental and well-studied classes of games, including two-player zero-sum games, general-sum games, and Stackelberg games. In part"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.14110","kind":"arxiv","version":2},"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/2209.14110/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":"2209.14110","created_at":"2026-07-05T05:47:04.049956+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.14110v2","created_at":"2026-07-05T05:47:04.049956+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.14110","created_at":"2026-07-05T05:47:04.049956+00:00"},{"alias_kind":"pith_short_12","alias_value":"5XAOCDPB6FUF","created_at":"2026-07-05T05:47:04.049956+00:00"},{"alias_kind":"pith_short_16","alias_value":"5XAOCDPB6FUF3H7K","created_at":"2026-07-05T05:47:04.049956+00:00"},{"alias_kind":"pith_short_8","alias_value":"5XAOCDPB","created_at":"2026-07-05T05:47:04.049956+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.20818","citing_title":"cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL","json":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL.json","graph_json":"https://pith.science/api/pith-number/5XAOCDPB6FUF3H7KULB7X4NGYL/graph.json","events_json":"https://pith.science/api/pith-number/5XAOCDPB6FUF3H7KULB7X4NGYL/events.json","paper":"https://pith.science/paper/5XAOCDPB"},"agent_actions":{"view_html":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL","download_json":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL.json","view_paper":"https://pith.science/paper/5XAOCDPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.14110&json=true","fetch_graph":"https://pith.science/api/pith-number/5XAOCDPB6FUF3H7KULB7X4NGYL/graph.json","fetch_events":"https://pith.science/api/pith-number/5XAOCDPB6FUF3H7KULB7X4NGYL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL/action/storage_attestation","attest_author":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL/action/author_attestation","sign_citation":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL/action/citation_signature","submit_replication":"https://pith.science/pith/5XAOCDPB6FUF3H7KULB7X4NGYL/action/replication_record"}},"created_at":"2026-07-05T05:47:04.049956+00:00","updated_at":"2026-07-05T05:47:04.049956+00:00"}