{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GCQWHFLKQOLQTU6EGI4G3TC4ZO","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":"e90beb7e675377f68adf18cbbe6b6d120585da68c6548f397b92f4d2f39356e9","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-27T08:20:19Z","title_canon_sha256":"5ec5622f92b4b3a2d07f4ad2dc89925d2d87a9329b9068e658c375c670937f57"},"schema_version":"1.0","source":{"id":"2212.13402","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.13402","created_at":"2026-07-05T05:29:41Z"},{"alias_kind":"arxiv_version","alias_value":"2212.13402v2","created_at":"2026-07-05T05:29:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.13402","created_at":"2026-07-05T05:29:41Z"},{"alias_kind":"pith_short_12","alias_value":"GCQWHFLKQOLQ","created_at":"2026-07-05T05:29:41Z"},{"alias_kind":"pith_short_16","alias_value":"GCQWHFLKQOLQTU6E","created_at":"2026-07-05T05:29:41Z"},{"alias_kind":"pith_short_8","alias_value":"GCQWHFLK","created_at":"2026-07-05T05:29:41Z"}],"graph_snapshots":[{"event_id":"sha256:5f518e37fc8ac820a2e68a2c8998e433a5d96e348e4a1cabcb9e22c93d136de3","target":"graph","created_at":"2026-07-05T05:29:41Z","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/2212.13402/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to repre","authors_text":"Dongjie Wang, Kunpeng Liu, Meng Xiao, Min Wu, Pengfei Wang, Yanjie Fu, Yuanchun Zhou, Ziyue Qiao","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-27T08:20:19Z","title":"Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.13402","kind":"arxiv","version":2},"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":"sha256:97b6cf707a7b2ef393f791a98c3ce8c7f1a3c3f34d0fbecbd4da50dacb2c3eec","target":"record","created_at":"2026-07-05T05:29:41Z","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":"e90beb7e675377f68adf18cbbe6b6d120585da68c6548f397b92f4d2f39356e9","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-27T08:20:19Z","title_canon_sha256":"5ec5622f92b4b3a2d07f4ad2dc89925d2d87a9329b9068e658c375c670937f57"},"schema_version":"1.0","source":{"id":"2212.13402","kind":"arxiv","version":2}},"canonical_sha256":"30a163956a839709d3c432386dcc5ccb96d84ba0b039d62becb8c2133db6fdc6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"30a163956a839709d3c432386dcc5ccb96d84ba0b039d62becb8c2133db6fdc6","first_computed_at":"2026-07-05T05:29:41.326898Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:29:41.326898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ldz7RghawC42BsoCiUTmf/pr2yd3Z5lYP15yB4mPPXd3BIeTlCKNdjWDNfC0Cr14iSY3CfYVn8IkhwKkrY/nAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:29:41.327296Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.13402","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97b6cf707a7b2ef393f791a98c3ce8c7f1a3c3f34d0fbecbd4da50dacb2c3eec","sha256:5f518e37fc8ac820a2e68a2c8998e433a5d96e348e4a1cabcb9e22c93d136de3"],"state_sha256":"d739b1f65f737f906de3d54dad894801b5e28803cde7b7a36f8957fbed22e851"}