{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:7EXXJDKTSFZFO7M6HGJV3UMUEZ","short_pith_number":"pith:7EXXJDKT","canonical_record":{"source":{"id":"2211.12641","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-23T00:04:57Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"26ffd916aa5dda32c1df4f4a78ee5cae7658b84efba1c7e58c3b431bc9040e87","abstract_canon_sha256":"71303cc47de2e68b823e5b2e3ab5ee3aae5a883f781aedaa1f5aef3f296fae8f"},"schema_version":"1.0"},"canonical_sha256":"f92f748d539172577d9e39935dd194264dded49a3e5ec2dcb9924cef1c7f4d0b","source":{"kind":"arxiv","id":"2211.12641","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.12641","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"arxiv_version","alias_value":"2211.12641v1","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.12641","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"pith_short_12","alias_value":"7EXXJDKTSFZF","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"pith_short_16","alias_value":"7EXXJDKTSFZFO7M6","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"pith_short_8","alias_value":"7EXXJDKT","created_at":"2026-07-05T05:18:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:7EXXJDKTSFZFO7M6HGJV3UMUEZ","target":"record","payload":{"canonical_record":{"source":{"id":"2211.12641","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-23T00:04:57Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"26ffd916aa5dda32c1df4f4a78ee5cae7658b84efba1c7e58c3b431bc9040e87","abstract_canon_sha256":"71303cc47de2e68b823e5b2e3ab5ee3aae5a883f781aedaa1f5aef3f296fae8f"},"schema_version":"1.0"},"canonical_sha256":"f92f748d539172577d9e39935dd194264dded49a3e5ec2dcb9924cef1c7f4d0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:18:53.210894Z","signature_b64":"CnszZoXy7/Sx5/uaSV9PYGehpTJZpkoFkTY1acTb/e2qQl+X4uC0XIDGV7VFq/0IYYq8RaCHNFdjyXuCIAg9DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f92f748d539172577d9e39935dd194264dded49a3e5ec2dcb9924cef1c7f4d0b","last_reissued_at":"2026-07-05T05:18:53.210523Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:18:53.210523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.12641","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":"2026-07-05T05:18:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QoMBN+dm6JxqMyei/k0Jua713GZLyZnJGxrvv0C8yqDQXArwShorhWM4YCYdFMtR+C1LXFUNh4f2TykuR2uNBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T05:30:59.010989Z"},"content_sha256":"86a8985758725ef6045a85cc3f4657ae438ad280b663448b784332bc80079357","schema_version":"1.0","event_id":"sha256:86a8985758725ef6045a85cc3f4657ae438ad280b663448b784332bc80079357"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:7EXXJDKTSFZFO7M6HGJV3UMUEZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraging Data Recasting to Enhance Tabular Reasoning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aashna Jena, Julian Martin Eisenschlos, Manish Shrivastava, Vivek Gupta","submitted_at":"2022-11-23T00:04:57Z","abstract_excerpt":"Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness. In this research, we present a framework for semi-automatically recasting existing tabular data to make use of the benefits of both approaches. We utilize our framework to build tabular NLI instances from five datasets that were"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.12641","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/2211.12641/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":"2026-07-05T05:18:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K4jZGOiji6HHfNe2gjJTFQud69gMTxM90WTNKsEBKE178+GkXs+yWstJh7hBV9TEyy14HTh/JF+cFWAiSkG9AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T05:30:59.011381Z"},"content_sha256":"a229a4f11e0fcb45f3728662b88966dab7ee1e8a916148d81ace253a33172783","schema_version":"1.0","event_id":"sha256:a229a4f11e0fcb45f3728662b88966dab7ee1e8a916148d81ace253a33172783"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ/bundle.json","state_url":"https://pith.science/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ/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":"2026-07-20T05:30:59Z","links":{"resolver":"https://pith.science/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ","bundle":"https://pith.science/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ/bundle.json","state":"https://pith.science/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7EXXJDKTSFZFO7M6HGJV3UMUEZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7EXXJDKTSFZFO7M6HGJV3UMUEZ","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":"71303cc47de2e68b823e5b2e3ab5ee3aae5a883f781aedaa1f5aef3f296fae8f","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-23T00:04:57Z","title_canon_sha256":"26ffd916aa5dda32c1df4f4a78ee5cae7658b84efba1c7e58c3b431bc9040e87"},"schema_version":"1.0","source":{"id":"2211.12641","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.12641","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"arxiv_version","alias_value":"2211.12641v1","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.12641","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"pith_short_12","alias_value":"7EXXJDKTSFZF","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"pith_short_16","alias_value":"7EXXJDKTSFZFO7M6","created_at":"2026-07-05T05:18:53Z"},{"alias_kind":"pith_short_8","alias_value":"7EXXJDKT","created_at":"2026-07-05T05:18:53Z"}],"graph_snapshots":[{"event_id":"sha256:a229a4f11e0fcb45f3728662b88966dab7ee1e8a916148d81ace253a33172783","target":"graph","created_at":"2026-07-05T05:18:53Z","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/2211.12641/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness. In this research, we present a framework for semi-automatically recasting existing tabular data to make use of the benefits of both approaches. We utilize our framework to build tabular NLI instances from five datasets that were","authors_text":"Aashna Jena, Julian Martin Eisenschlos, Manish Shrivastava, Vivek Gupta","cross_cats":["cs.AI","cs.IR","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-23T00:04:57Z","title":"Leveraging Data Recasting to Enhance Tabular Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.12641","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":"sha256:86a8985758725ef6045a85cc3f4657ae438ad280b663448b784332bc80079357","target":"record","created_at":"2026-07-05T05:18:53Z","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":"71303cc47de2e68b823e5b2e3ab5ee3aae5a883f781aedaa1f5aef3f296fae8f","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-11-23T00:04:57Z","title_canon_sha256":"26ffd916aa5dda32c1df4f4a78ee5cae7658b84efba1c7e58c3b431bc9040e87"},"schema_version":"1.0","source":{"id":"2211.12641","kind":"arxiv","version":1}},"canonical_sha256":"f92f748d539172577d9e39935dd194264dded49a3e5ec2dcb9924cef1c7f4d0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f92f748d539172577d9e39935dd194264dded49a3e5ec2dcb9924cef1c7f4d0b","first_computed_at":"2026-07-05T05:18:53.210523Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:18:53.210523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CnszZoXy7/Sx5/uaSV9PYGehpTJZpkoFkTY1acTb/e2qQl+X4uC0XIDGV7VFq/0IYYq8RaCHNFdjyXuCIAg9DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:18:53.210894Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.12641","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:86a8985758725ef6045a85cc3f4657ae438ad280b663448b784332bc80079357","sha256:a229a4f11e0fcb45f3728662b88966dab7ee1e8a916148d81ace253a33172783"],"state_sha256":"d65467d02475099c0275ab1ad0e5e616e174132cf063a005757c09fb6424f2a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s8WgTGt5RjegDaZfzEvCxvZTdSFiQZ98oFQLKXTqBnkurKQBwvcC/zsr6+UrkJSxGvt9EXs8zjo8+Lt5ZRtYAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T05:30:59.013949Z","bundle_sha256":"55572f1c97cd34689b5193f159c8776408441a034adf543de2c83e144dd3ad67"}}