{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:WILBYQ6FPAOYD5FIGL5P4TTGIR","short_pith_number":"pith:WILBYQ6F","canonical_record":{"source":{"id":"2507.18196","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T08:54:17Z","cross_cats_sorted":[],"title_canon_sha256":"4da8e89869479f86df5ef6c1280aa99028d2d2839fb6661a2b2cb3e19aeb4653","abstract_canon_sha256":"510a062e3f9735190479b7bbb20819d7e39ee3f35725e0a851feeda4efa8dc52"},"schema_version":"1.0"},"canonical_sha256":"b2161c43c5781d81f4a832fafe4e66447eac2364e669730fb988b7bdd069ab53","source":{"kind":"arxiv","id":"2507.18196","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18196","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18196v1","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18196","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"pith_short_12","alias_value":"WILBYQ6FPAOY","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"pith_short_16","alias_value":"WILBYQ6FPAOYD5FI","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"pith_short_8","alias_value":"WILBYQ6F","created_at":"2026-07-05T11:42:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:WILBYQ6FPAOYD5FIGL5P4TTGIR","target":"record","payload":{"canonical_record":{"source":{"id":"2507.18196","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T08:54:17Z","cross_cats_sorted":[],"title_canon_sha256":"4da8e89869479f86df5ef6c1280aa99028d2d2839fb6661a2b2cb3e19aeb4653","abstract_canon_sha256":"510a062e3f9735190479b7bbb20819d7e39ee3f35725e0a851feeda4efa8dc52"},"schema_version":"1.0"},"canonical_sha256":"b2161c43c5781d81f4a832fafe4e66447eac2364e669730fb988b7bdd069ab53","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:43.481550Z","signature_b64":"eeBFSCVRsUQNXPARmTiVuidB0CIDZGe8YMOLrGydQ/LV7bWLL7uNMesBbmgdw6fjhIAuVrgSvh4I8WIMdhKEBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2161c43c5781d81f4a832fafe4e66447eac2364e669730fb988b7bdd069ab53","last_reissued_at":"2026-07-05T11:42:43.481107Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:43.481107Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.18196","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":"2026-07-05T11:42:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xr5MuE0PtFurnoaAN+6Z8bLgsDRFXvB0IQWkGbQA9eJHaAU1YM1FA6JfXLEMZLwHaD654qsQLCuelwKFJ6b2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T09:28:46.281466Z"},"content_sha256":"5068c6572ae5e796b8e399056dc01985ff11518f3796149a6937f1ae527bd883","schema_version":"1.0","event_id":"sha256:5068c6572ae5e796b8e399056dc01985ff11518f3796149a6937f1ae527bd883"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:WILBYQ6FPAOYD5FIGL5P4TTGIR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Goal-based Trajectory Prediction for improved Cross-Dataset Generalization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ahmed Abouelazm, Daniel Grimm, J. Marius Z\\\"ollner","submitted_at":"2025-07-24T08:54:17Z","abstract_excerpt":"To achieve full autonomous driving, a good understanding of the surrounding environment is necessary. Especially predicting the future states of other traffic participants imposes a non-trivial challenge. Current SotA-models already show promising results when trained on real datasets (e.g. Argoverse2, NuScenes). Problems arise when these models are deployed to new/unseen areas. Typically, performance drops significantly, indicating that the models lack generalization. In this work, we introduce a new Graph Neural Network (GNN) that utilizes a heterogeneous graph consisting of traffic particip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18196","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/2507.18196/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":"2026-07-05T11:42:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yQNYglslWN5Y2qqJR2mTCXfRnj3a9lLROnWTZFOlmSPZoXAA5cltwrcQWp7E4PCebyV33ozLsAda7sfu9kj9BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T09:28:46.281837Z"},"content_sha256":"a152e54c1ffd96664ee8b987c74b1730e4adc02474a75cb1b2f124cc4a68cad2","schema_version":"1.0","event_id":"sha256:a152e54c1ffd96664ee8b987c74b1730e4adc02474a75cb1b2f124cc4a68cad2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR/bundle.json","state_url":"https://pith.science/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR/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":"2026-07-22T09:28:46Z","links":{"resolver":"https://pith.science/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR","bundle":"https://pith.science/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR/bundle.json","state":"https://pith.science/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WILBYQ6FPAOYD5FIGL5P4TTGIR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WILBYQ6FPAOYD5FIGL5P4TTGIR","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":"510a062e3f9735190479b7bbb20819d7e39ee3f35725e0a851feeda4efa8dc52","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T08:54:17Z","title_canon_sha256":"4da8e89869479f86df5ef6c1280aa99028d2d2839fb6661a2b2cb3e19aeb4653"},"schema_version":"1.0","source":{"id":"2507.18196","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18196","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18196v1","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18196","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"pith_short_12","alias_value":"WILBYQ6FPAOY","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"pith_short_16","alias_value":"WILBYQ6FPAOYD5FI","created_at":"2026-07-05T11:42:43Z"},{"alias_kind":"pith_short_8","alias_value":"WILBYQ6F","created_at":"2026-07-05T11:42:43Z"}],"graph_snapshots":[{"event_id":"sha256:a152e54c1ffd96664ee8b987c74b1730e4adc02474a75cb1b2f124cc4a68cad2","target":"graph","created_at":"2026-07-05T11:42:43Z","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/2507.18196/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To achieve full autonomous driving, a good understanding of the surrounding environment is necessary. Especially predicting the future states of other traffic participants imposes a non-trivial challenge. Current SotA-models already show promising results when trained on real datasets (e.g. Argoverse2, NuScenes). Problems arise when these models are deployed to new/unseen areas. Typically, performance drops significantly, indicating that the models lack generalization. In this work, we introduce a new Graph Neural Network (GNN) that utilizes a heterogeneous graph consisting of traffic particip","authors_text":"Ahmed Abouelazm, Daniel Grimm, J. Marius Z\\\"ollner","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T08:54:17Z","title":"Goal-based Trajectory Prediction for improved Cross-Dataset Generalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18196","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":"sha256:5068c6572ae5e796b8e399056dc01985ff11518f3796149a6937f1ae527bd883","target":"record","created_at":"2026-07-05T11:42:43Z","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":"510a062e3f9735190479b7bbb20819d7e39ee3f35725e0a851feeda4efa8dc52","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-24T08:54:17Z","title_canon_sha256":"4da8e89869479f86df5ef6c1280aa99028d2d2839fb6661a2b2cb3e19aeb4653"},"schema_version":"1.0","source":{"id":"2507.18196","kind":"arxiv","version":1}},"canonical_sha256":"b2161c43c5781d81f4a832fafe4e66447eac2364e669730fb988b7bdd069ab53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2161c43c5781d81f4a832fafe4e66447eac2364e669730fb988b7bdd069ab53","first_computed_at":"2026-07-05T11:42:43.481107Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:43.481107Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eeBFSCVRsUQNXPARmTiVuidB0CIDZGe8YMOLrGydQ/LV7bWLL7uNMesBbmgdw6fjhIAuVrgSvh4I8WIMdhKEBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:43.481550Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.18196","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5068c6572ae5e796b8e399056dc01985ff11518f3796149a6937f1ae527bd883","sha256:a152e54c1ffd96664ee8b987c74b1730e4adc02474a75cb1b2f124cc4a68cad2"],"state_sha256":"03ffe1a21ce0994b1b742dfafd73bf360de48919acc0b8ce9b7badbce584698f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q7bIBvLrNyyjOB53wpi1L3mww55xaT0mY13EL+ljvY35wmZlK3Vv7bTQB7T7wnKwH9AzZaerpsHVIkEl9kjxCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-22T09:28:46.284103Z","bundle_sha256":"fb85017eff4dd69049332ea53b4e9eb2895fb1d8f8c0c067d5569ce066f7ea42"}}