{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QWTMIIFTAC7JSBXD746TNJEJAN","short_pith_number":"pith:QWTMIIFT","schema_version":"1.0","canonical_sha256":"85a6c420b300be9906e3ff3d36a4890361b95897894bab7554eeeaf9b27ca53b","source":{"kind":"arxiv","id":"2410.03210","version":2},"attestation_state":"computed","paper":{"title":"Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aleksandr Drozd, Emil Vatai, Ivan R. Ivanov, Joao E. Batista, Mohamed Wahib, Yinghao Ren","submitted_at":"2024-10-04T07:56:05Z","abstract_excerpt":"Frameworks and domain-specific languages for auto-generating code have traditionally depended on human experts to implement rigorous methods ensuring the legality of code transformations. Recently, machine learning (ML) has gained traction for generating code optimized for specific hardware targets. However, ML approaches-particularly black-box neural networks-offer no guarantees on the correctness or legality of the transformations they produce. To address this gap, we introduce Tadashi, an end-to-end system that leverages the polyhedral model to support researchers in curating datasets criti"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.03210","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-04T07:56:05Z","cross_cats_sorted":[],"title_canon_sha256":"7c70a0c0b8ce9fe51523ebd96354f0da18d6c1187cde9a4a5f794a6df995e014","abstract_canon_sha256":"e342437613063e8f34522e06264fb0ab482568a9fe18e2fff43559ad80c59967"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:58.453692Z","signature_b64":"uXDTTnGOkfCHQEHHaf86ZxGIF7A1jVs4iWS6ahSAM4bbVYYNyLu5LsgF6p/L/Ye2Y415m0cDg4jNaBNNpP9zAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85a6c420b300be9906e3ff3d36a4890361b95897894bab7554eeeaf9b27ca53b","last_reissued_at":"2026-07-05T11:13:58.453229Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:58.453229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aleksandr Drozd, Emil Vatai, Ivan R. Ivanov, Joao E. Batista, Mohamed Wahib, Yinghao Ren","submitted_at":"2024-10-04T07:56:05Z","abstract_excerpt":"Frameworks and domain-specific languages for auto-generating code have traditionally depended on human experts to implement rigorous methods ensuring the legality of code transformations. Recently, machine learning (ML) has gained traction for generating code optimized for specific hardware targets. However, ML approaches-particularly black-box neural networks-offer no guarantees on the correctness or legality of the transformations they produce. To address this gap, we introduce Tadashi, an end-to-end system that leverages the polyhedral model to support researchers in curating datasets criti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03210","kind":"arxiv","version":2},"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/2410.03210/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.03210","created_at":"2026-07-05T11:13:58.453285+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.03210v2","created_at":"2026-07-05T11:13:58.453285+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03210","created_at":"2026-07-05T11:13:58.453285+00:00"},{"alias_kind":"pith_short_12","alias_value":"QWTMIIFTAC7J","created_at":"2026-07-05T11:13:58.453285+00:00"},{"alias_kind":"pith_short_16","alias_value":"QWTMIIFTAC7JSBXD","created_at":"2026-07-05T11:13:58.453285+00:00"},{"alias_kind":"pith_short_8","alias_value":"QWTMIIFT","created_at":"2026-07-05T11:13:58.453285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN","json":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN.json","graph_json":"https://pith.science/api/pith-number/QWTMIIFTAC7JSBXD746TNJEJAN/graph.json","events_json":"https://pith.science/api/pith-number/QWTMIIFTAC7JSBXD746TNJEJAN/events.json","paper":"https://pith.science/paper/QWTMIIFT"},"agent_actions":{"view_html":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN","download_json":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN.json","view_paper":"https://pith.science/paper/QWTMIIFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.03210&json=true","fetch_graph":"https://pith.science/api/pith-number/QWTMIIFTAC7JSBXD746TNJEJAN/graph.json","fetch_events":"https://pith.science/api/pith-number/QWTMIIFTAC7JSBXD746TNJEJAN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN/action/storage_attestation","attest_author":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN/action/author_attestation","sign_citation":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN/action/citation_signature","submit_replication":"https://pith.science/pith/QWTMIIFTAC7JSBXD746TNJEJAN/action/replication_record"}},"created_at":"2026-07-05T11:13:58.453285+00:00","updated_at":"2026-07-05T11:13:58.453285+00:00"}