{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:GNPCF7U422JMPXOSMTFGS7AW4I","short_pith_number":"pith:GNPCF7U4","canonical_record":{"source":{"id":"2605.30015","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T14:39:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"62c2192ad12bf36d31cf7da4d682dc69784b9bb3cfbdd1dab0bd6de2743af0a0","abstract_canon_sha256":"44fe499a6e90df699522e9ddf9842860fc2726ff120feba6f55424c2ec605683"},"schema_version":"1.0"},"canonical_sha256":"335e22fe9cd692c7ddd264ca697c16e212bc370b654c670a8901e4c9ace999f1","source":{"kind":"arxiv","id":"2605.30015","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30015","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30015v1","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30015","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"pith_short_12","alias_value":"GNPCF7U422JM","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"pith_short_16","alias_value":"GNPCF7U422JMPXOS","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"pith_short_8","alias_value":"GNPCF7U4","created_at":"2026-05-29T02:06:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:GNPCF7U422JMPXOSMTFGS7AW4I","target":"record","payload":{"canonical_record":{"source":{"id":"2605.30015","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T14:39:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"62c2192ad12bf36d31cf7da4d682dc69784b9bb3cfbdd1dab0bd6de2743af0a0","abstract_canon_sha256":"44fe499a6e90df699522e9ddf9842860fc2726ff120feba6f55424c2ec605683"},"schema_version":"1.0"},"canonical_sha256":"335e22fe9cd692c7ddd264ca697c16e212bc370b654c670a8901e4c9ace999f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:07.076863Z","signature_b64":"O1w4K59j/ykK3FD2uRkW2byliZGYylkXInvAUWrf3sJ7TUfBs2CqcWEwkWoC/MX1zQEd65iLuSlZJSGpvIM+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"335e22fe9cd692c7ddd264ca697c16e212bc370b654c670a8901e4c9ace999f1","last_reissued_at":"2026-05-29T02:06:07.076055Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:07.076055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.30015","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":"2026-05-29T02:06:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ShOAwxNECs4akyloU0fCi4oJgkRspI8eOY1+ID0sU2GkEvxWI23Tri7zvGBrD8PoDGx+nUvRnwYTw+f1FdfvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:00:11.720780Z"},"content_sha256":"c87cd779103b4bb03049436baee7f9d5ceea0fcf29c25622599699b4082d8d6d","schema_version":"1.0","event_id":"sha256:c87cd779103b4bb03049436baee7f9d5ceea0fcf29c25622599699b4082d8d6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:GNPCF7U422JMPXOSMTFGS7AW4I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Test Time Training for Supervised Causal Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dongmei Zhang, Huang Bojun, Jiaru Zhang, Jinzhuo Wang, Qiang Fu, Rui Ding, Shi Han, Zizhen Deng","submitted_at":"2026-05-28T14:39:49Z","abstract_excerpt":"Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution generalization challenges. We reveal three limitations of previous SCL practices: a significant performance gap between synthetic benchmarks and real-world data, fragility to distribution shifts, and failure in compositional generalization, collectively questioning its real-world applicability. To address this, we propose Test-Time Training for Supervised Causal Learning (TTT-SCL), a novel framework that dynamically gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30015","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/2605.30015/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":"2026-05-29T02:06:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZmlcFdGbMHO5587uxTf3EdAEpjgcnnUQc1rBFzdzRKwylktpQbKDPRLV4iwlQM3b0Jjx/ZUhVZg+MBOtuClkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:00:11.721601Z"},"content_sha256":"f1af385d5fea0308699577b6dc2e0ac65f34141ba6da46b54be43875e7f6d75f","schema_version":"1.0","event_id":"sha256:f1af385d5fea0308699577b6dc2e0ac65f34141ba6da46b54be43875e7f6d75f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GNPCF7U422JMPXOSMTFGS7AW4I/bundle.json","state_url":"https://pith.science/pith/GNPCF7U422JMPXOSMTFGS7AW4I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GNPCF7U422JMPXOSMTFGS7AW4I/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":"2026-06-08T19:00:11Z","links":{"resolver":"https://pith.science/pith/GNPCF7U422JMPXOSMTFGS7AW4I","bundle":"https://pith.science/pith/GNPCF7U422JMPXOSMTFGS7AW4I/bundle.json","state":"https://pith.science/pith/GNPCF7U422JMPXOSMTFGS7AW4I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GNPCF7U422JMPXOSMTFGS7AW4I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GNPCF7U422JMPXOSMTFGS7AW4I","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":"44fe499a6e90df699522e9ddf9842860fc2726ff120feba6f55424c2ec605683","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T14:39:49Z","title_canon_sha256":"62c2192ad12bf36d31cf7da4d682dc69784b9bb3cfbdd1dab0bd6de2743af0a0"},"schema_version":"1.0","source":{"id":"2605.30015","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30015","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30015v1","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30015","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"pith_short_12","alias_value":"GNPCF7U422JM","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"pith_short_16","alias_value":"GNPCF7U422JMPXOS","created_at":"2026-05-29T02:06:07Z"},{"alias_kind":"pith_short_8","alias_value":"GNPCF7U4","created_at":"2026-05-29T02:06:07Z"}],"graph_snapshots":[{"event_id":"sha256:f1af385d5fea0308699577b6dc2e0ac65f34141ba6da46b54be43875e7f6d75f","target":"graph","created_at":"2026-05-29T02:06:07Z","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/2605.30015/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from significant out-of-distribution generalization challenges. We reveal three limitations of previous SCL practices: a significant performance gap between synthetic benchmarks and real-world data, fragility to distribution shifts, and failure in compositional generalization, collectively questioning its real-world applicability. To address this, we propose Test-Time Training for Supervised Causal Learning (TTT-SCL), a novel framework that dynamically gene","authors_text":"Dongmei Zhang, Huang Bojun, Jiaru Zhang, Jinzhuo Wang, Qiang Fu, Rui Ding, Shi Han, Zizhen Deng","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T14:39:49Z","title":"Test Time Training for Supervised Causal Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30015","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:c87cd779103b4bb03049436baee7f9d5ceea0fcf29c25622599699b4082d8d6d","target":"record","created_at":"2026-05-29T02:06:07Z","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":"44fe499a6e90df699522e9ddf9842860fc2726ff120feba6f55424c2ec605683","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T14:39:49Z","title_canon_sha256":"62c2192ad12bf36d31cf7da4d682dc69784b9bb3cfbdd1dab0bd6de2743af0a0"},"schema_version":"1.0","source":{"id":"2605.30015","kind":"arxiv","version":1}},"canonical_sha256":"335e22fe9cd692c7ddd264ca697c16e212bc370b654c670a8901e4c9ace999f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"335e22fe9cd692c7ddd264ca697c16e212bc370b654c670a8901e4c9ace999f1","first_computed_at":"2026-05-29T02:06:07.076055Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T02:06:07.076055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O1w4K59j/ykK3FD2uRkW2byliZGYylkXInvAUWrf3sJ7TUfBs2CqcWEwkWoC/MX1zQEd65iLuSlZJSGpvIM+CQ==","signature_status":"signed_v1","signed_at":"2026-05-29T02:06:07.076863Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.30015","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c87cd779103b4bb03049436baee7f9d5ceea0fcf29c25622599699b4082d8d6d","sha256:f1af385d5fea0308699577b6dc2e0ac65f34141ba6da46b54be43875e7f6d75f"],"state_sha256":"a8f463871b6e4859c6b2f014db6646f39fac32ac906e91d088f24e8a24e1247b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lrg+J68csYpUO/Zg+B8jwZMNTpbTrJitSFwSqDVUEGrKaiGv/EQ+wTCdkBoF4VoCKWCC4QkQ3VG2HBo3IWuKAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T19:00:11.725783Z","bundle_sha256":"d3c6fd2e17c7b5178e23ed3e0b53ef8ebb17cb1cc5296fd19439cff430dcc777"}}