{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HUEZUWJVIOR7V54IBDLN7M54QQ","short_pith_number":"pith:HUEZUWJV","canonical_record":{"source":{"id":"1803.07612","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-20T19:19:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"541cdbee24db6c63331851a0dd9c4666228910ea13d3adc0bb8844f549abcb67","abstract_canon_sha256":"223165e79a81d8c3db80a5d3610498e803875aadde551fc93f1907d4b783d2ae"},"schema_version":"1.0"},"canonical_sha256":"3d099a593543a3faf78808d6dfb3bc841a888d52fcb2b0078981289a05e24b77","source":{"kind":"arxiv","id":"1803.07612","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07612","created_at":"2026-05-17T23:52:59Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07612v6","created_at":"2026-05-17T23:52:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07612","created_at":"2026-05-17T23:52:59Z"},{"alias_kind":"pith_short_12","alias_value":"HUEZUWJVIOR7","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HUEZUWJVIOR7V54I","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HUEZUWJV","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HUEZUWJVIOR7V54IBDLN7M54QQ","target":"record","payload":{"canonical_record":{"source":{"id":"1803.07612","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-20T19:19:13Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"541cdbee24db6c63331851a0dd9c4666228910ea13d3adc0bb8844f549abcb67","abstract_canon_sha256":"223165e79a81d8c3db80a5d3610498e803875aadde551fc93f1907d4b783d2ae"},"schema_version":"1.0"},"canonical_sha256":"3d099a593543a3faf78808d6dfb3bc841a888d52fcb2b0078981289a05e24b77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:59.284649Z","signature_b64":"vhzz49e5cWufofU7Ys1j+8o0rIjSujljxY3qbM5tQ1i4D+OISsMlSLDf44TSi5TRQW6SNCr4Cld/0UoJfj32CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d099a593543a3faf78808d6dfb3bc841a888d52fcb2b0078981289a05e24b77","last_reissued_at":"2026-05-17T23:52:59.283867Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:59.283867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.07612","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s8VnYZ6TkagLHcjivZOGJMLmND8qxmDukCkMSanbSm/sIlcY9Tj93ZcVbbL3TejUtywLsxVKA3czQZyvoxY2Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T19:50:51.092195Z"},"content_sha256":"69a688ca07cf301dfafec34e65f9386ce3b6e2c371b290e238f8669c7cd36a48","schema_version":"1.0","event_id":"sha256:69a688ca07cf301dfafec34e65f9386ce3b6e2c371b290e238f8669c7cd36a48"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HUEZUWJVIOR7V54IBDLN7M54QQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generating Multi-Agent Trajectories using Programmatic Weak Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric Zhan, Long Sha, Patrick Lucey, Stephan Zheng, Yisong Yue","submitted_at":"2018-03-20T19:19:13Z","abstract_excerpt":"We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulatable way. We present a hierarchical framework that can effectively learn such sequential generative models. Our approach is inspired by recent work on lev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07612","kind":"arxiv","version":6},"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":"2026-05-17T23:52:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NA128asuqlxqiOMeC+oiSure22xrGpPk5FbhCBQjcwJaMzORodWrXz/FRxC48mbMS+MgU24c62UrngN8qKhBDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T19:50:51.092897Z"},"content_sha256":"3c536466c730dc585e03274d99277df6be86badba96c1d254ae3c9a2fc985d5b","schema_version":"1.0","event_id":"sha256:3c536466c730dc585e03274d99277df6be86badba96c1d254ae3c9a2fc985d5b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HUEZUWJVIOR7V54IBDLN7M54QQ/bundle.json","state_url":"https://pith.science/pith/HUEZUWJVIOR7V54IBDLN7M54QQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HUEZUWJVIOR7V54IBDLN7M54QQ/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":"2026-05-27T19:50:51Z","links":{"resolver":"https://pith.science/pith/HUEZUWJVIOR7V54IBDLN7M54QQ","bundle":"https://pith.science/pith/HUEZUWJVIOR7V54IBDLN7M54QQ/bundle.json","state":"https://pith.science/pith/HUEZUWJVIOR7V54IBDLN7M54QQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HUEZUWJVIOR7V54IBDLN7M54QQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HUEZUWJVIOR7V54IBDLN7M54QQ","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":"223165e79a81d8c3db80a5d3610498e803875aadde551fc93f1907d4b783d2ae","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-20T19:19:13Z","title_canon_sha256":"541cdbee24db6c63331851a0dd9c4666228910ea13d3adc0bb8844f549abcb67"},"schema_version":"1.0","source":{"id":"1803.07612","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.07612","created_at":"2026-05-17T23:52:59Z"},{"alias_kind":"arxiv_version","alias_value":"1803.07612v6","created_at":"2026-05-17T23:52:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.07612","created_at":"2026-05-17T23:52:59Z"},{"alias_kind":"pith_short_12","alias_value":"HUEZUWJVIOR7","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HUEZUWJVIOR7V54I","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HUEZUWJV","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:3c536466c730dc585e03274d99277df6be86badba96c1d254ae3c9a2fc985d5b","target":"graph","created_at":"2026-05-17T23:52:59Z","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":"We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulatable way. We present a hierarchical framework that can effectively learn such sequential generative models. Our approach is inspired by recent work on lev","authors_text":"Eric Zhan, Long Sha, Patrick Lucey, Stephan Zheng, Yisong Yue","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-20T19:19:13Z","title":"Generating Multi-Agent Trajectories using Programmatic Weak Supervision"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.07612","kind":"arxiv","version":6},"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":"sha256:69a688ca07cf301dfafec34e65f9386ce3b6e2c371b290e238f8669c7cd36a48","target":"record","created_at":"2026-05-17T23:52:59Z","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":"223165e79a81d8c3db80a5d3610498e803875aadde551fc93f1907d4b783d2ae","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-20T19:19:13Z","title_canon_sha256":"541cdbee24db6c63331851a0dd9c4666228910ea13d3adc0bb8844f549abcb67"},"schema_version":"1.0","source":{"id":"1803.07612","kind":"arxiv","version":6}},"canonical_sha256":"3d099a593543a3faf78808d6dfb3bc841a888d52fcb2b0078981289a05e24b77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d099a593543a3faf78808d6dfb3bc841a888d52fcb2b0078981289a05e24b77","first_computed_at":"2026-05-17T23:52:59.283867Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:59.283867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vhzz49e5cWufofU7Ys1j+8o0rIjSujljxY3qbM5tQ1i4D+OISsMlSLDf44TSi5TRQW6SNCr4Cld/0UoJfj32CQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:59.284649Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.07612","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:69a688ca07cf301dfafec34e65f9386ce3b6e2c371b290e238f8669c7cd36a48","sha256:3c536466c730dc585e03274d99277df6be86badba96c1d254ae3c9a2fc985d5b"],"state_sha256":"b95b4454ee3f5273679a524b995360577166e9088c924edc93ae329baa7ff28d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u3ENcLuMmCXghgMcbadYbdOOfkNMSn+HYPSgomlSGN1s0gFjF7esqVmFrxZudYSsxuGVfIXp3NZ2BL0XTmJWAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T19:50:51.096577Z","bundle_sha256":"290283a174d2f814bec36114b38963c497138d23c80e98d5724625979710c11e"}}