{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2YBPTBKFEOFKONYEATA4ZV5VQB","short_pith_number":"pith:2YBPTBKF","canonical_record":{"source":{"id":"2506.18285","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-23T04:41:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d59482593bb97cb6ed7d84f7bcf6f290907c7a1f3257d2c418fe7fb07168dcb","abstract_canon_sha256":"11a5ae8822a944627d2a4695bd61498ca8ea9c13c014e2a8a3cfad1dad63a8a7"},"schema_version":"1.0"},"canonical_sha256":"d602f98545238aa7370404c1ccd7b5804bc32da1249fd3acdbaf6de59b689fc9","source":{"kind":"arxiv","id":"2506.18285","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18285","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18285v1","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18285","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"pith_short_12","alias_value":"2YBPTBKFEOFK","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"pith_short_16","alias_value":"2YBPTBKFEOFKONYE","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"pith_short_8","alias_value":"2YBPTBKF","created_at":"2026-07-05T11:25:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2YBPTBKFEOFKONYEATA4ZV5VQB","target":"record","payload":{"canonical_record":{"source":{"id":"2506.18285","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-23T04:41:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d59482593bb97cb6ed7d84f7bcf6f290907c7a1f3257d2c418fe7fb07168dcb","abstract_canon_sha256":"11a5ae8822a944627d2a4695bd61498ca8ea9c13c014e2a8a3cfad1dad63a8a7"},"schema_version":"1.0"},"canonical_sha256":"d602f98545238aa7370404c1ccd7b5804bc32da1249fd3acdbaf6de59b689fc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:47.957300Z","signature_b64":"JoS1bdTs8VXs/CnyjQE90RET7e3olFHi0AMBjj54J9h/aNmSc2XhLVkfmKxv4NuOTLEluNV+FwiChQShhOTECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d602f98545238aa7370404c1ccd7b5804bc32da1249fd3acdbaf6de59b689fc9","last_reissued_at":"2026-07-05T11:25:47.956896Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:47.956896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.18285","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":"2026-07-05T11:25:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P8L4ZMzJmx94yMcyORHy4pYMHKjCVc8rta4JpyEFaOEYOrBCA0QsS8MqE5gEpzyBUQlZyZA9+qTNrAawzViVCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:25:11.632145Z"},"content_sha256":"dd1b3ad9946bbfa66f22fe0bca73f227fef894ba60f799e40494abbb6747717c","schema_version":"1.0","event_id":"sha256:dd1b3ad9946bbfa66f22fe0bca73f227fef894ba60f799e40494abbb6747717c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2YBPTBKFEOFKONYEATA4ZV5VQB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Causal Graphs at Scale: A Foundation Model Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Naiyu Yin, Tian Gao, Yue Yu","submitted_at":"2025-06-23T04:41:02Z","abstract_excerpt":"Due to its human-interpretability and invariance properties, Directed Acyclic Graph (DAG) has been a foundational tool across various areas of AI research, leading to significant advancements. However, DAG learning remains highly challenging, due to its super-exponential growth in computational cost and identifiability issues, particularly in small-sample regimes. To address these two challenges, in this work we leverage the recent success of linear transformers and develop a foundation model approach for discovering multiple order-consistent DAGs across tasks. In particular, we propose Attent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18285","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/2506.18285/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":"2026-07-05T11:25:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bDYq+POHzKR9dLXwwB9iRB6xQSwKwx89znT6A0EbKRfYdknfGOrdl2Ph/YCbsB14LtuVGccDEN+fObTzxIgnBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:25:11.633060Z"},"content_sha256":"72d1e62053d269c9dd1b480b8987d597326530f6d670ce753fdb97a575cc6bf9","schema_version":"1.0","event_id":"sha256:72d1e62053d269c9dd1b480b8987d597326530f6d670ce753fdb97a575cc6bf9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2YBPTBKFEOFKONYEATA4ZV5VQB/bundle.json","state_url":"https://pith.science/pith/2YBPTBKFEOFKONYEATA4ZV5VQB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2YBPTBKFEOFKONYEATA4ZV5VQB/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":"2026-08-16T14:25:11Z","links":{"resolver":"https://pith.science/pith/2YBPTBKFEOFKONYEATA4ZV5VQB","bundle":"https://pith.science/pith/2YBPTBKFEOFKONYEATA4ZV5VQB/bundle.json","state":"https://pith.science/pith/2YBPTBKFEOFKONYEATA4ZV5VQB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2YBPTBKFEOFKONYEATA4ZV5VQB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2YBPTBKFEOFKONYEATA4ZV5VQB","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":"11a5ae8822a944627d2a4695bd61498ca8ea9c13c014e2a8a3cfad1dad63a8a7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-23T04:41:02Z","title_canon_sha256":"6d59482593bb97cb6ed7d84f7bcf6f290907c7a1f3257d2c418fe7fb07168dcb"},"schema_version":"1.0","source":{"id":"2506.18285","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18285","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18285v1","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18285","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"pith_short_12","alias_value":"2YBPTBKFEOFK","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"pith_short_16","alias_value":"2YBPTBKFEOFKONYE","created_at":"2026-07-05T11:25:47Z"},{"alias_kind":"pith_short_8","alias_value":"2YBPTBKF","created_at":"2026-07-05T11:25:47Z"}],"graph_snapshots":[{"event_id":"sha256:72d1e62053d269c9dd1b480b8987d597326530f6d670ce753fdb97a575cc6bf9","target":"graph","created_at":"2026-07-05T11:25:47Z","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/2506.18285/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to its human-interpretability and invariance properties, Directed Acyclic Graph (DAG) has been a foundational tool across various areas of AI research, leading to significant advancements. However, DAG learning remains highly challenging, due to its super-exponential growth in computational cost and identifiability issues, particularly in small-sample regimes. To address these two challenges, in this work we leverage the recent success of linear transformers and develop a foundation model approach for discovering multiple order-consistent DAGs across tasks. In particular, we propose Attent","authors_text":"Naiyu Yin, Tian Gao, Yue Yu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-23T04:41:02Z","title":"Learning Causal Graphs at Scale: A Foundation Model Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18285","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:dd1b3ad9946bbfa66f22fe0bca73f227fef894ba60f799e40494abbb6747717c","target":"record","created_at":"2026-07-05T11:25:47Z","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":"11a5ae8822a944627d2a4695bd61498ca8ea9c13c014e2a8a3cfad1dad63a8a7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-23T04:41:02Z","title_canon_sha256":"6d59482593bb97cb6ed7d84f7bcf6f290907c7a1f3257d2c418fe7fb07168dcb"},"schema_version":"1.0","source":{"id":"2506.18285","kind":"arxiv","version":1}},"canonical_sha256":"d602f98545238aa7370404c1ccd7b5804bc32da1249fd3acdbaf6de59b689fc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d602f98545238aa7370404c1ccd7b5804bc32da1249fd3acdbaf6de59b689fc9","first_computed_at":"2026-07-05T11:25:47.956896Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:47.956896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JoS1bdTs8VXs/CnyjQE90RET7e3olFHi0AMBjj54J9h/aNmSc2XhLVkfmKxv4NuOTLEluNV+FwiChQShhOTECA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:47.957300Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.18285","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd1b3ad9946bbfa66f22fe0bca73f227fef894ba60f799e40494abbb6747717c","sha256:72d1e62053d269c9dd1b480b8987d597326530f6d670ce753fdb97a575cc6bf9"],"state_sha256":"8e07edc8559fc0019d6d15779586d1614e1a08ad56615f75ba61b773c420d99f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PtdGQ+3IFTs2IJdzYMM6f6gxTWdAL9druZn5a+eOo+MxSJImfUxVK8zGZBC6UnTr/AAtxnm2BB3kDJToufWHBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T14:25:11.639779Z","bundle_sha256":"9a2ad99c0aa88da3252648cb92703abd333139384386656d440a02bab6cbe6f2"}}