{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:L2WJT5P5EUSVGPU5ON7ZV5UTJE","short_pith_number":"pith:L2WJT5P5","canonical_record":{"source":{"id":"1809.11044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-28T14:10:39Z","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"title_canon_sha256":"70eb5248dba83fa6be855a712f3ed7722c7fb60c7ba920206083d91ad013179c","abstract_canon_sha256":"73bcc467573067c392585f4bf17acadfe664c2d5a9b3163ce49fd67f368c661b"},"schema_version":"1.0"},"canonical_sha256":"5eac99f5fd2525533e9d737f9af6934919dc9844f83f281e4ea3420f3145e4ce","source":{"kind":"arxiv","id":"1809.11044","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.11044","created_at":"2026-05-18T00:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"1809.11044v1","created_at":"2026-05-18T00:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.11044","created_at":"2026-05-18T00:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"L2WJT5P5EUSV","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L2WJT5P5EUSVGPU5","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L2WJT5P5","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:L2WJT5P5EUSVGPU5ON7ZV5UTJE","target":"record","payload":{"canonical_record":{"source":{"id":"1809.11044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-28T14:10:39Z","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"title_canon_sha256":"70eb5248dba83fa6be855a712f3ed7722c7fb60c7ba920206083d91ad013179c","abstract_canon_sha256":"73bcc467573067c392585f4bf17acadfe664c2d5a9b3163ce49fd67f368c661b"},"schema_version":"1.0"},"canonical_sha256":"5eac99f5fd2525533e9d737f9af6934919dc9844f83f281e4ea3420f3145e4ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:33.495556Z","signature_b64":"fZpZ3KEgBfrXp1z1tgLGOm/0uEUleISGA/OYzetDISZVm1sIRbnenbHhDkq+5X5f6hHZ1jgV/TOkbh9qBnDcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5eac99f5fd2525533e9d737f9af6934919dc9844f83f281e4ea3420f3145e4ce","last_reissued_at":"2026-05-18T00:04:33.494769Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:33.494769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.11044","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":"2026-05-18T00:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mQ5cLswYg4+oc8vAq+6G7FG5QvAhdfQVsF6arsfLdCsj3eWSqvHIoCBqX3MZkvrI048WJP9elaLz7qgZKS8iCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T10:18:21.266568Z"},"content_sha256":"75c643c06a78b3342c925395026eab13823daffb36e638a3a4764ac406331f3f","schema_version":"1.0","event_id":"sha256:75c643c06a78b3342c925395026eab13823daffb36e638a3a4764ac406331f3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:L2WJT5P5EUSVGPU5ON7ZV5UTJE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Relational Forward Models for Multi-Agent Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MA","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrea Tacchetti, H. Francis Song, Matthew Botvinick, Neil C. Rabinowitz, Pedro A. M. Mediano, Peter W. Battaglia, Thore Graepel, Vinicius Zambaldi","submitted_at":"2018-09-28T14:10:39Z","abstract_excerpt":"The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments. Because these models operate on the discrete entities and relations present in the environment, they produce interpretable intermediate representations which offer insights into what drives agents' behavior, and what e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.11044","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":""},"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":"2026-05-18T00:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yv2cAGL+8z6GY0xIp5s30w5L15CN+PRhmlt/0ZsvZqBPvRPB5hISqNWcqLUFbXsF5jVG3RfrGg4iIQJxgYNfBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T10:18:21.266919Z"},"content_sha256":"2cc612a0ba59bfb25d124af41f74a1d3897284f5e790bcfe5302a2f4e868c1a2","schema_version":"1.0","event_id":"sha256:2cc612a0ba59bfb25d124af41f74a1d3897284f5e790bcfe5302a2f4e868c1a2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE/bundle.json","state_url":"https://pith.science/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE/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":"2026-05-30T10:18:21Z","links":{"resolver":"https://pith.science/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE","bundle":"https://pith.science/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE/bundle.json","state":"https://pith.science/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L2WJT5P5EUSVGPU5ON7ZV5UTJE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:L2WJT5P5EUSVGPU5ON7ZV5UTJE","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":"73bcc467573067c392585f4bf17acadfe664c2d5a9b3163ce49fd67f368c661b","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-28T14:10:39Z","title_canon_sha256":"70eb5248dba83fa6be855a712f3ed7722c7fb60c7ba920206083d91ad013179c"},"schema_version":"1.0","source":{"id":"1809.11044","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.11044","created_at":"2026-05-18T00:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"1809.11044v1","created_at":"2026-05-18T00:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.11044","created_at":"2026-05-18T00:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"L2WJT5P5EUSV","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L2WJT5P5EUSVGPU5","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L2WJT5P5","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:2cc612a0ba59bfb25d124af41f74a1d3897284f5e790bcfe5302a2f4e868c1a2","target":"graph","created_at":"2026-05-18T00:04:33Z","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"},"paper":{"abstract_excerpt":"The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments. Because these models operate on the discrete entities and relations present in the environment, they produce interpretable intermediate representations which offer insights into what drives agents' behavior, and what e","authors_text":"Andrea Tacchetti, H. Francis Song, Matthew Botvinick, Neil C. Rabinowitz, Pedro A. M. Mediano, Peter W. Battaglia, Thore Graepel, Vinicius Zambaldi","cross_cats":["cs.AI","cs.MA","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-28T14:10:39Z","title":"Relational Forward Models for Multi-Agent Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.11044","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":"sha256:75c643c06a78b3342c925395026eab13823daffb36e638a3a4764ac406331f3f","target":"record","created_at":"2026-05-18T00:04:33Z","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":"73bcc467573067c392585f4bf17acadfe664c2d5a9b3163ce49fd67f368c661b","cross_cats_sorted":["cs.AI","cs.MA","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-28T14:10:39Z","title_canon_sha256":"70eb5248dba83fa6be855a712f3ed7722c7fb60c7ba920206083d91ad013179c"},"schema_version":"1.0","source":{"id":"1809.11044","kind":"arxiv","version":1}},"canonical_sha256":"5eac99f5fd2525533e9d737f9af6934919dc9844f83f281e4ea3420f3145e4ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5eac99f5fd2525533e9d737f9af6934919dc9844f83f281e4ea3420f3145e4ce","first_computed_at":"2026-05-18T00:04:33.494769Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:33.494769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fZpZ3KEgBfrXp1z1tgLGOm/0uEUleISGA/OYzetDISZVm1sIRbnenbHhDkq+5X5f6hHZ1jgV/TOkbh9qBnDcBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:33.495556Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.11044","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75c643c06a78b3342c925395026eab13823daffb36e638a3a4764ac406331f3f","sha256:2cc612a0ba59bfb25d124af41f74a1d3897284f5e790bcfe5302a2f4e868c1a2"],"state_sha256":"54c51d798d40b175be0bfb7f68ed69f75e04983451c3fa9b3d94d6bda2a3f6fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IXess6Vvvz2+es3dwZi1rjYwSCR0D9qKOBceXXBSC/JTqO+39NBM4QX/+hJmYCDkEN5HkEPGB6gU3ppmk//KAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T10:18:21.269450Z","bundle_sha256":"bbd905c2a739e34c321a415a72b0f184aeed1985257404d6c16bf3ff5d2e988b"}}