{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NVR7ZIVXOIM4TZTUG7JEHB3CZ6","short_pith_number":"pith:NVR7ZIVX","canonical_record":{"source":{"id":"2608.09162","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-10T06:16:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d4fe094e427bca796f4cc5b9c925f3045fecf44d1287a0c04ba782d38266740","abstract_canon_sha256":"81ee11ed7cfa6ea3d930de223e9aff025e0334c9d26d3e5b71ceafb922d23b46"},"schema_version":"1.0"},"canonical_sha256":"6d63fca2b77219c9e67437d2438762cf8d91d9ac3a8adbfb00200be81a69eef7","source":{"kind":"arxiv","id":"2608.09162","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.09162","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"arxiv_version","alias_value":"2608.09162v1","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.09162","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"pith_short_12","alias_value":"NVR7ZIVXOIM4","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"pith_short_16","alias_value":"NVR7ZIVXOIM4TZTU","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"pith_short_8","alias_value":"NVR7ZIVX","created_at":"2026-08-11T02:21:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NVR7ZIVXOIM4TZTUG7JEHB3CZ6","target":"record","payload":{"canonical_record":{"source":{"id":"2608.09162","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-10T06:16:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6d4fe094e427bca796f4cc5b9c925f3045fecf44d1287a0c04ba782d38266740","abstract_canon_sha256":"81ee11ed7cfa6ea3d930de223e9aff025e0334c9d26d3e5b71ceafb922d23b46"},"schema_version":"1.0"},"canonical_sha256":"6d63fca2b77219c9e67437d2438762cf8d91d9ac3a8adbfb00200be81a69eef7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T02:21:56.975840Z","signature_b64":"gSl5AQrWdc9jn3wna8/nT/cAi5T0oD6MPp3QolK4dT1icIyelA1oPxI/OFexz2TwBM62bEHUZm1Ic8HNBu0vBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d63fca2b77219c9e67437d2438762cf8d91d9ac3a8adbfb00200be81a69eef7","last_reissued_at":"2026-08-11T02:21:56.974279Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T02:21:56.974279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.09162","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":"2026-08-11T02:21:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bnnjmMqDqWCJfUQ/jReIyS8I/PbPZ2mpmbECDVRwksugMgvgJjuemiJxDyoxMnK+R+4rPsZGBDP3DwmTwIOlAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T21:42:37.400138Z"},"content_sha256":"1d02f0e4fb6f13a7a712f9acb950309cd0c35debfb3d752bc033e52361dcd82c","schema_version":"1.0","event_id":"sha256:1d02f0e4fb6f13a7a712f9acb950309cd0c35debfb3d752bc033e52361dcd82c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NVR7ZIVXOIM4TZTUG7JEHB3CZ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tabular Numeric Stretch Transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Burak Varici, Johnna Sundberg, Juyong Kim, Pradeep Ravikumar, Zihao Ye","submitted_at":"2026-08-10T06:16:41Z","abstract_excerpt":"Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties. Although recent advances have improved how models learn from tabular data, how numeric data are transformed into model-friendly representations remains comparatively underexplored. We introduce the stretch transformation framework, which formulates numeric feature preprocessing as an optimization problem to make the target function smoother and thus more learnable. Our framework has two variants: (1) unsupervised st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.09162","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/2608.09162/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":"2026-08-11T02:21:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UoKIdXSEAjw6GkjbQXoXaKpuk7OA8pKwn9yByq0OlE4/l/ANUG7xvaB/zJbE3wIOHLf5mh9culot+hWv8csACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T21:42:37.400514Z"},"content_sha256":"f996ea8faba0e28f966b2d6e65d604a0e395ad575818c51534f7f34296b8e1d0","schema_version":"1.0","event_id":"sha256:f996ea8faba0e28f966b2d6e65d604a0e395ad575818c51534f7f34296b8e1d0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6/bundle.json","state_url":"https://pith.science/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6/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":"2026-08-12T21:42:37Z","links":{"resolver":"https://pith.science/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6","bundle":"https://pith.science/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6/bundle.json","state":"https://pith.science/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NVR7ZIVXOIM4TZTUG7JEHB3CZ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NVR7ZIVXOIM4TZTUG7JEHB3CZ6","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":"81ee11ed7cfa6ea3d930de223e9aff025e0334c9d26d3e5b71ceafb922d23b46","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-10T06:16:41Z","title_canon_sha256":"6d4fe094e427bca796f4cc5b9c925f3045fecf44d1287a0c04ba782d38266740"},"schema_version":"1.0","source":{"id":"2608.09162","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.09162","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"arxiv_version","alias_value":"2608.09162v1","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.09162","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"pith_short_12","alias_value":"NVR7ZIVXOIM4","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"pith_short_16","alias_value":"NVR7ZIVXOIM4TZTU","created_at":"2026-08-11T02:21:56Z"},{"alias_kind":"pith_short_8","alias_value":"NVR7ZIVX","created_at":"2026-08-11T02:21:56Z"}],"graph_snapshots":[{"event_id":"sha256:f996ea8faba0e28f966b2d6e65d604a0e395ad575818c51534f7f34296b8e1d0","target":"graph","created_at":"2026-08-11T02:21:56Z","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/2608.09162/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties. Although recent advances have improved how models learn from tabular data, how numeric data are transformed into model-friendly representations remains comparatively underexplored. We introduce the stretch transformation framework, which formulates numeric feature preprocessing as an optimization problem to make the target function smoother and thus more learnable. Our framework has two variants: (1) unsupervised st","authors_text":"Burak Varici, Johnna Sundberg, Juyong Kim, Pradeep Ravikumar, Zihao Ye","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-10T06:16:41Z","title":"Tabular Numeric Stretch Transformation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.09162","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":"sha256:1d02f0e4fb6f13a7a712f9acb950309cd0c35debfb3d752bc033e52361dcd82c","target":"record","created_at":"2026-08-11T02:21:56Z","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":"81ee11ed7cfa6ea3d930de223e9aff025e0334c9d26d3e5b71ceafb922d23b46","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-10T06:16:41Z","title_canon_sha256":"6d4fe094e427bca796f4cc5b9c925f3045fecf44d1287a0c04ba782d38266740"},"schema_version":"1.0","source":{"id":"2608.09162","kind":"arxiv","version":1}},"canonical_sha256":"6d63fca2b77219c9e67437d2438762cf8d91d9ac3a8adbfb00200be81a69eef7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d63fca2b77219c9e67437d2438762cf8d91d9ac3a8adbfb00200be81a69eef7","first_computed_at":"2026-08-11T02:21:56.974279Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-11T02:21:56.974279Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gSl5AQrWdc9jn3wna8/nT/cAi5T0oD6MPp3QolK4dT1icIyelA1oPxI/OFexz2TwBM62bEHUZm1Ic8HNBu0vBA==","signature_status":"signed_v1","signed_at":"2026-08-11T02:21:56.975840Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.09162","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d02f0e4fb6f13a7a712f9acb950309cd0c35debfb3d752bc033e52361dcd82c","sha256:f996ea8faba0e28f966b2d6e65d604a0e395ad575818c51534f7f34296b8e1d0"],"state_sha256":"ecbff3e892bcf34864b2867561c2cdb2d547ba071fc4219fbfbc46d8c2883d49"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TtRn7LCm74PnCCrots7cHDsYNo4eXn+nSkXWsIaooaBn5gx2zFmw0b+D1wefPCEeCx8Vjs55XndZQVNuKI/JBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T21:42:37.402882Z","bundle_sha256":"70925e774ca9353d3959e2dd91415f08dbad4703d966c5abde36ed5c4483d1d3"}}