{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FTXWKSRPJ6YA4HMFVCDE7MSTGF","short_pith_number":"pith:FTXWKSRP","schema_version":"1.0","canonical_sha256":"2cef654a2f4fb00e1d85a8864fb2533163dd0c27db275e492c5d04762bbab72d","source":{"kind":"arxiv","id":"2507.06004","version":1},"attestation_state":"computed","paper":{"title":"From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Chang Yao, Hao Wu, Kai Lv, Shang Han, Shoucheng Song, Youfang Lin, Yuqing Ma","submitted_at":"2025-07-08T14:07:53Z","abstract_excerpt":"Continual Multi-Agent Reinforcement Learning (Co-MARL) requires agents to address catastrophic forgetting issues while learning new coordination policies with the dynamics team. In this paper, we delve into the core of Co-MARL, namely Relation Patterns, which refer to agents' general understanding of interactions. In addition to generality, relation patterns exhibit task-specificity when mapped to different action spaces. To this end, we propose a novel method called General Relation Patterns-Guided Task-Specific Decision-Maker (RPG). In RPG, agents extract relation patterns from dynamic obser"},"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":"2507.06004","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MA","submitted_at":"2025-07-08T14:07:53Z","cross_cats_sorted":[],"title_canon_sha256":"5ec73f33bbfd5369f36869cb027d95b375bfaa6d4d833c75f6f032a47e59317f","abstract_canon_sha256":"785bf49d7a3f1a830b7a4d17b1336e7364ee7d605ece150b9ca18cd06c1ad4e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:39.817867Z","signature_b64":"DNPysrPpnvk1RDxTiTG2A28C23i90lCeqXYltbeWHDx5EGI1Md6paZ1PolqW3Q+NUYZ0Mk2y5erXYLV2kqpgDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cef654a2f4fb00e1d85a8864fb2533163dd0c27db275e492c5d04762bbab72d","last_reissued_at":"2026-07-05T11:33:39.817369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:39.817369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Chang Yao, Hao Wu, Kai Lv, Shang Han, Shoucheng Song, Youfang Lin, Yuqing Ma","submitted_at":"2025-07-08T14:07:53Z","abstract_excerpt":"Continual Multi-Agent Reinforcement Learning (Co-MARL) requires agents to address catastrophic forgetting issues while learning new coordination policies with the dynamics team. In this paper, we delve into the core of Co-MARL, namely Relation Patterns, which refer to agents' general understanding of interactions. In addition to generality, relation patterns exhibit task-specificity when mapped to different action spaces. To this end, we propose a novel method called General Relation Patterns-Guided Task-Specific Decision-Maker (RPG). In RPG, agents extract relation patterns from dynamic obser"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06004","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/2507.06004/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":"2507.06004","created_at":"2026-07-05T11:33:39.817431+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.06004v1","created_at":"2026-07-05T11:33:39.817431+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06004","created_at":"2026-07-05T11:33:39.817431+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTXWKSRPJ6YA","created_at":"2026-07-05T11:33:39.817431+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTXWKSRPJ6YA4HMF","created_at":"2026-07-05T11:33:39.817431+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTXWKSRP","created_at":"2026-07-05T11:33:39.817431+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.19042","citing_title":"STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation","ref_index":93,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF","json":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF.json","graph_json":"https://pith.science/api/pith-number/FTXWKSRPJ6YA4HMFVCDE7MSTGF/graph.json","events_json":"https://pith.science/api/pith-number/FTXWKSRPJ6YA4HMFVCDE7MSTGF/events.json","paper":"https://pith.science/paper/FTXWKSRP"},"agent_actions":{"view_html":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF","download_json":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF.json","view_paper":"https://pith.science/paper/FTXWKSRP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.06004&json=true","fetch_graph":"https://pith.science/api/pith-number/FTXWKSRPJ6YA4HMFVCDE7MSTGF/graph.json","fetch_events":"https://pith.science/api/pith-number/FTXWKSRPJ6YA4HMFVCDE7MSTGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF/action/storage_attestation","attest_author":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF/action/author_attestation","sign_citation":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF/action/citation_signature","submit_replication":"https://pith.science/pith/FTXWKSRPJ6YA4HMFVCDE7MSTGF/action/replication_record"}},"created_at":"2026-07-05T11:33:39.817431+00:00","updated_at":"2026-07-05T11:33:39.817431+00:00"}