{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:V3IASIGUXBINQVLILZNIW5NIAO","short_pith_number":"pith:V3IASIGU","canonical_record":{"source":{"id":"2505.18433","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T00:00:43Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"071ed0d21385dd78b97a4a5a296ffeca799a8d9c7883662bbedb6d53d5683a42","abstract_canon_sha256":"ecd0808b0cd012f33f829eea729d65b1ee73578d6100286c4669681cf9ec2621"},"schema_version":"1.0"},"canonical_sha256":"aed00920d4b850d855685e5a8b75a803aac0865ad2fa7264cdef8cbeb333181a","source":{"kind":"arxiv","id":"2505.18433","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.18433","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"arxiv_version","alias_value":"2505.18433v2","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18433","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"pith_short_12","alias_value":"V3IASIGUXBIN","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"pith_short_16","alias_value":"V3IASIGUXBINQVLI","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"pith_short_8","alias_value":"V3IASIGU","created_at":"2026-07-05T11:53:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:V3IASIGUXBINQVLILZNIW5NIAO","target":"record","payload":{"canonical_record":{"source":{"id":"2505.18433","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T00:00:43Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"071ed0d21385dd78b97a4a5a296ffeca799a8d9c7883662bbedb6d53d5683a42","abstract_canon_sha256":"ecd0808b0cd012f33f829eea729d65b1ee73578d6100286c4669681cf9ec2621"},"schema_version":"1.0"},"canonical_sha256":"aed00920d4b850d855685e5a8b75a803aac0865ad2fa7264cdef8cbeb333181a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:03.515584Z","signature_b64":"yuqzyYSP0GCYecdwTGm2/SkfDhghbr+GH+aVpDdAL5c2tz00DmgIplRqKIAnVgHzxzbkAJA7IV2GUPtmEIDPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aed00920d4b850d855685e5a8b75a803aac0865ad2fa7264cdef8cbeb333181a","last_reissued_at":"2026-07-05T11:53:03.515115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:03.515115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.18433","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:53:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bPjho9+VDaGgroeNaWvokaZLn6lvcGCNYuqlIiA1LHeYFfxrcilv7NbtRkPTdaly/8DDRjYn+Qx/wrixIdzbCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T05:33:37.501766Z"},"content_sha256":"489c1a49b8a7d866aa759026bd7d0f727708ce22be151757c3260f06886fa2a4","schema_version":"1.0","event_id":"sha256:489c1a49b8a7d866aa759026bd7d0f727708ce22be151757c3260f06886fa2a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:V3IASIGUXBINQVLILZNIW5NIAO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.LG","authors_text":"Alvaro Velasquez, Fnu Hairi, Jia Liu, Myeung Suk Oh, Zhiyao Zhang, Ziyue Luo","submitted_at":"2025-05-24T00:00:43Z","abstract_excerpt":"Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. To date, however, most theoretical convergence studies for existing actor-critic decentralized MARL methods are limited to the guarantee of a stationary solution under the linear function approximation. This leaves a significant gap between the highly successful use of deep neural actor-critic for decentralized MARL in practice and the current theoretical understanding. To brid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18433","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/2505.18433/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:53:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8VCO22qSwYxqQAdmtN/XOHvLMFSNChvY8jjsMzteCq/QFWm59eUuyLSlHxlwX1T2SeUaCi0Y5yrzeVkwelFCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T05:33:37.502778Z"},"content_sha256":"510370e2aae030ede7005bbec4727b2feecc8f38f469ccac19b3e95ec0dc49fd","schema_version":"1.0","event_id":"sha256:510370e2aae030ede7005bbec4727b2feecc8f38f469ccac19b3e95ec0dc49fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V3IASIGUXBINQVLILZNIW5NIAO/bundle.json","state_url":"https://pith.science/pith/V3IASIGUXBINQVLILZNIW5NIAO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V3IASIGUXBINQVLILZNIW5NIAO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-01T05:33:37Z","links":{"resolver":"https://pith.science/pith/V3IASIGUXBINQVLILZNIW5NIAO","bundle":"https://pith.science/pith/V3IASIGUXBINQVLILZNIW5NIAO/bundle.json","state":"https://pith.science/pith/V3IASIGUXBINQVLILZNIW5NIAO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V3IASIGUXBINQVLILZNIW5NIAO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V3IASIGUXBINQVLILZNIW5NIAO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ecd0808b0cd012f33f829eea729d65b1ee73578d6100286c4669681cf9ec2621","cross_cats_sorted":["cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T00:00:43Z","title_canon_sha256":"071ed0d21385dd78b97a4a5a296ffeca799a8d9c7883662bbedb6d53d5683a42"},"schema_version":"1.0","source":{"id":"2505.18433","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.18433","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"arxiv_version","alias_value":"2505.18433v2","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18433","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"pith_short_12","alias_value":"V3IASIGUXBIN","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"pith_short_16","alias_value":"V3IASIGUXBINQVLI","created_at":"2026-07-05T11:53:03Z"},{"alias_kind":"pith_short_8","alias_value":"V3IASIGU","created_at":"2026-07-05T11:53:03Z"}],"graph_snapshots":[{"event_id":"sha256:510370e2aae030ede7005bbec4727b2feecc8f38f469ccac19b3e95ec0dc49fd","target":"graph","created_at":"2026-07-05T11:53:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.18433/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. To date, however, most theoretical convergence studies for existing actor-critic decentralized MARL methods are limited to the guarantee of a stationary solution under the linear function approximation. This leaves a significant gap between the highly successful use of deep neural actor-critic for decentralized MARL in practice and the current theoretical understanding. To brid","authors_text":"Alvaro Velasquez, Fnu Hairi, Jia Liu, Myeung Suk Oh, Zhiyao Zhang, Ziyue Luo","cross_cats":["cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T00:00:43Z","title":"Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18433","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:489c1a49b8a7d866aa759026bd7d0f727708ce22be151757c3260f06886fa2a4","target":"record","created_at":"2026-07-05T11:53:03Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ecd0808b0cd012f33f829eea729d65b1ee73578d6100286c4669681cf9ec2621","cross_cats_sorted":["cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-24T00:00:43Z","title_canon_sha256":"071ed0d21385dd78b97a4a5a296ffeca799a8d9c7883662bbedb6d53d5683a42"},"schema_version":"1.0","source":{"id":"2505.18433","kind":"arxiv","version":2}},"canonical_sha256":"aed00920d4b850d855685e5a8b75a803aac0865ad2fa7264cdef8cbeb333181a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aed00920d4b850d855685e5a8b75a803aac0865ad2fa7264cdef8cbeb333181a","first_computed_at":"2026-07-05T11:53:03.515115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:53:03.515115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yuqzyYSP0GCYecdwTGm2/SkfDhghbr+GH+aVpDdAL5c2tz00DmgIplRqKIAnVgHzxzbkAJA7IV2GUPtmEIDPAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:53:03.515584Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.18433","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:489c1a49b8a7d866aa759026bd7d0f727708ce22be151757c3260f06886fa2a4","sha256:510370e2aae030ede7005bbec4727b2feecc8f38f469ccac19b3e95ec0dc49fd"],"state_sha256":"e1b47ab8c89a9174634663ca69e132b5b891dc0d69284a5e190db9241aad9ed3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4WqkpDsbse9ahILf5KSA1GReV2AXOVzifwJGsOlflSNDkjwjZn5e04JQ0JIE3/Fam5ckrBWTrOAiQ+gqaQryCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T05:33:37.508059Z","bundle_sha256":"d9795e52ba7788e877f574f93bc47f5664f32fa267ef4ab657ba319d1657e0fa"}}