{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:X6N55W6OEOXZ37OAOI27Y6IVU6","short_pith_number":"pith:X6N55W6O","canonical_record":{"source":{"id":"2501.15695","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-01-26T22:49:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b43de1a9b73ff5171cf3b68d2f5f2b6837c800c6a7d3ff30ec0581a05b03f4a6","abstract_canon_sha256":"f2ed43a3cc30e85229b62e9a8393c266563dea1ab1c9dd05095cade856c277ec"},"schema_version":"1.0"},"canonical_sha256":"bf9bdedbce23af9dfdc07235fc7915a784f113cd1ecede89be4291e9877d9c0c","source":{"kind":"arxiv","id":"2501.15695","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15695","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15695v1","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15695","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"pith_short_12","alias_value":"X6N55W6OEOXZ","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"pith_short_16","alias_value":"X6N55W6OEOXZ37OA","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"pith_short_8","alias_value":"X6N55W6O","created_at":"2026-07-05T10:05:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:X6N55W6OEOXZ37OAOI27Y6IVU6","target":"record","payload":{"canonical_record":{"source":{"id":"2501.15695","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-01-26T22:49:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b43de1a9b73ff5171cf3b68d2f5f2b6837c800c6a7d3ff30ec0581a05b03f4a6","abstract_canon_sha256":"f2ed43a3cc30e85229b62e9a8393c266563dea1ab1c9dd05095cade856c277ec"},"schema_version":"1.0"},"canonical_sha256":"bf9bdedbce23af9dfdc07235fc7915a784f113cd1ecede89be4291e9877d9c0c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:32.870114Z","signature_b64":"L3ssfybcVmD7xmVywx5noLZ+ryc7hPtFMStu46CDPnowE6/ervGYcy5M+gvYZEOfc8pCa4TvFZg5XmHe3MFADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf9bdedbce23af9dfdc07235fc7915a784f113cd1ecede89be4291e9877d9c0c","last_reissued_at":"2026-07-05T10:05:32.869653Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:32.869653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.15695","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JrJK22X4dgvBElPWoo8rD35EVoloDq7zteRh9LRYWzvgTC9uyF01eYTb8W1EFUaaEZsjyyuZjIV0wQpmcoQ4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T14:58:36.405038Z"},"content_sha256":"6c96e9450bf7e1e6d50452feb1ccfaff1e31460d9f508e4a32334305cbca51b6","schema_version":"1.0","event_id":"sha256:6c96e9450bf7e1e6d50452feb1ccfaff1e31460d9f508e4a32334305cbca51b6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:X6N55W6OEOXZ37OAOI27Y6IVU6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Hung Du, Hy Nguyen, Kon Mouzakis, Rajesh Vasa, Srikanth Thudumu","submitted_at":"2025-01-26T22:49:50Z","abstract_excerpt":"Decentralized Multi-Agent Reinforcement Learning (Dec-MARL) has emerged as a pivotal approach for addressing complex tasks in dynamic environments. Existing Multi-Agent Reinforcement Learning (MARL) methodologies typically assume a shared objective among agents and rely on centralized control. However, many real-world scenarios feature agents with individual goals and limited observability of other agents, complicating coordination and hindering adaptability. Existing Dec-MARL strategies prioritize either communication or coordination, lacking an integrated approach that leverages both. This p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15695","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/2501.15695/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":"2026-07-05T10:05:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hlhMWZUyCqsOcxbjgH+s4GPzPb3ehZXfQSPDpVb2xgefTQDim/2iuFLe8pTrS2SJDaEZtOKVTVsO7XEgUogVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T14:58:36.405375Z"},"content_sha256":"7fb985bcb617305b43937922f86b69c229d09a7fd8e576363ce883322f136794","schema_version":"1.0","event_id":"sha256:7fb985bcb617305b43937922f86b69c229d09a7fd8e576363ce883322f136794"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X6N55W6OEOXZ37OAOI27Y6IVU6/bundle.json","state_url":"https://pith.science/pith/X6N55W6OEOXZ37OAOI27Y6IVU6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X6N55W6OEOXZ37OAOI27Y6IVU6/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":"2026-08-17T14:58:36Z","links":{"resolver":"https://pith.science/pith/X6N55W6OEOXZ37OAOI27Y6IVU6","bundle":"https://pith.science/pith/X6N55W6OEOXZ37OAOI27Y6IVU6/bundle.json","state":"https://pith.science/pith/X6N55W6OEOXZ37OAOI27Y6IVU6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X6N55W6OEOXZ37OAOI27Y6IVU6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:X6N55W6OEOXZ37OAOI27Y6IVU6","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":"f2ed43a3cc30e85229b62e9a8393c266563dea1ab1c9dd05095cade856c277ec","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-01-26T22:49:50Z","title_canon_sha256":"b43de1a9b73ff5171cf3b68d2f5f2b6837c800c6a7d3ff30ec0581a05b03f4a6"},"schema_version":"1.0","source":{"id":"2501.15695","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15695","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15695v1","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15695","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"pith_short_12","alias_value":"X6N55W6OEOXZ","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"pith_short_16","alias_value":"X6N55W6OEOXZ37OA","created_at":"2026-07-05T10:05:32Z"},{"alias_kind":"pith_short_8","alias_value":"X6N55W6O","created_at":"2026-07-05T10:05:32Z"}],"graph_snapshots":[{"event_id":"sha256:7fb985bcb617305b43937922f86b69c229d09a7fd8e576363ce883322f136794","target":"graph","created_at":"2026-07-05T10:05:32Z","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/2501.15695/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Decentralized Multi-Agent Reinforcement Learning (Dec-MARL) has emerged as a pivotal approach for addressing complex tasks in dynamic environments. Existing Multi-Agent Reinforcement Learning (MARL) methodologies typically assume a shared objective among agents and rely on centralized control. However, many real-world scenarios feature agents with individual goals and limited observability of other agents, complicating coordination and hindering adaptability. Existing Dec-MARL strategies prioritize either communication or coordination, lacking an integrated approach that leverages both. This p","authors_text":"Hung Du, Hy Nguyen, Kon Mouzakis, Rajesh Vasa, Srikanth Thudumu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-01-26T22:49:50Z","title":"Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15695","kind":"arxiv","version":1},"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":"sha256:6c96e9450bf7e1e6d50452feb1ccfaff1e31460d9f508e4a32334305cbca51b6","target":"record","created_at":"2026-07-05T10:05:32Z","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":"f2ed43a3cc30e85229b62e9a8393c266563dea1ab1c9dd05095cade856c277ec","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-01-26T22:49:50Z","title_canon_sha256":"b43de1a9b73ff5171cf3b68d2f5f2b6837c800c6a7d3ff30ec0581a05b03f4a6"},"schema_version":"1.0","source":{"id":"2501.15695","kind":"arxiv","version":1}},"canonical_sha256":"bf9bdedbce23af9dfdc07235fc7915a784f113cd1ecede89be4291e9877d9c0c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bf9bdedbce23af9dfdc07235fc7915a784f113cd1ecede89be4291e9877d9c0c","first_computed_at":"2026-07-05T10:05:32.869653Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:32.869653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L3ssfybcVmD7xmVywx5noLZ+ryc7hPtFMStu46CDPnowE6/ervGYcy5M+gvYZEOfc8pCa4TvFZg5XmHe3MFADA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:32.870114Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.15695","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c96e9450bf7e1e6d50452feb1ccfaff1e31460d9f508e4a32334305cbca51b6","sha256:7fb985bcb617305b43937922f86b69c229d09a7fd8e576363ce883322f136794"],"state_sha256":"2dd02663cf2eff4c6c89ca04facd3df2d6f1c285b72bba26c711d9bb4fbba056"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xGzsDiEvwMtm5AmSNUqqkqnWbhbYXf5ggPz6oyCNYfDahjZ1EyvaxcZwxCARUhnpw0VngahgBisErgGJK4U8AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T14:58:36.408071Z","bundle_sha256":"0bd4abe554a568a702880283019e8520eca4fa0068a9903309b0e4f293b4b71b"}}