{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:BZWQ4MOSUYWHG2RCJCC3Z5YDBH","short_pith_number":"pith:BZWQ4MOS","canonical_record":{"source":{"id":"2605.24340","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T01:52:50Z","cross_cats_sorted":[],"title_canon_sha256":"c1378eeb84bffe69ac7c8342cf1c20269b81eed9b169813e488ac0f0f44c55b5","abstract_canon_sha256":"089a23e6c89b95df17c1bf5ca0cb54ea9d07210daff50d686c16a60098c37a1a"},"schema_version":"1.0"},"canonical_sha256":"0e6d0e31d2a62c736a224885bcf70309e4bd627a26847d089778193911d0500a","source":{"kind":"arxiv","id":"2605.24340","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24340","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24340v1","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24340","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"pith_short_12","alias_value":"BZWQ4MOSUYWH","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"pith_short_16","alias_value":"BZWQ4MOSUYWHG2RC","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"pith_short_8","alias_value":"BZWQ4MOS","created_at":"2026-05-26T01:03:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:BZWQ4MOSUYWHG2RCJCC3Z5YDBH","target":"record","payload":{"canonical_record":{"source":{"id":"2605.24340","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T01:52:50Z","cross_cats_sorted":[],"title_canon_sha256":"c1378eeb84bffe69ac7c8342cf1c20269b81eed9b169813e488ac0f0f44c55b5","abstract_canon_sha256":"089a23e6c89b95df17c1bf5ca0cb54ea9d07210daff50d686c16a60098c37a1a"},"schema_version":"1.0"},"canonical_sha256":"0e6d0e31d2a62c736a224885bcf70309e4bd627a26847d089778193911d0500a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:00.759893Z","signature_b64":"z08pxwvTaVBgLPlQdtZKFaywVJdacTMZiiXE5SOLwCIwv8E7MiXsk00d6lmdj2LGzoIn8a5UOpb3K0v5jBqoCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e6d0e31d2a62c736a224885bcf70309e4bd627a26847d089778193911d0500a","last_reissued_at":"2026-05-26T01:03:00.759352Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:00.759352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.24340","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":"2026-05-26T01:03:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Th89jMT7PGRc4QnPziUBBuf8S9/+lB8itigE8kuplXHpW7dpxIShmstLEBCGX/9H/dyNdvTedK2hgYnv1/DpAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T00:56:00.665500Z"},"content_sha256":"9e1c7777e7d27375152b9c0e728932a3079bbd0e43f335cbb9ef2bcdd3fc9db7","schema_version":"1.0","event_id":"sha256:9e1c7777e7d27375152b9c0e728932a3079bbd0e43f335cbb9ef2bcdd3fc9db7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:BZWQ4MOSUYWHG2RCJCC3Z5YDBH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Rowan Martnishn","submitted_at":"2026-05-23T01:52:50Z","abstract_excerpt":"Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure: labeled data is expensive, inference budgets are tight, and models that cannot explain their behavior are difficult to trust and maintain. We present ChainzRule (CR), a neural architecture replacing typical activations with learnable polynomial layers governed by Differential Regularization (DREG), a layer-wise Jacobian penalty computed analytically during the forward pass at standard inference cost. The core claim is that bounding intermediate derivatives forces the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24340","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/2605.24340/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":"2026-05-26T01:03:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I0wJpUciibsXbNogLveIyfUMncO3yDVWD4Kf0+VaC7wDayBfE5YZhfpxC7YgxMlPLcYZ4D7F4GauxU0gzMykBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T00:56:00.665875Z"},"content_sha256":"09bea6ce3ae79936a33e3d4037170841fa6796bf858fe95ffb421469d08e1338","schema_version":"1.0","event_id":"sha256:09bea6ce3ae79936a33e3d4037170841fa6796bf858fe95ffb421469d08e1338"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH/bundle.json","state_url":"https://pith.science/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH/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":"2026-07-03T00:56:00Z","links":{"resolver":"https://pith.science/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH","bundle":"https://pith.science/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH/bundle.json","state":"https://pith.science/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BZWQ4MOSUYWHG2RCJCC3Z5YDBH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BZWQ4MOSUYWHG2RCJCC3Z5YDBH","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":"089a23e6c89b95df17c1bf5ca0cb54ea9d07210daff50d686c16a60098c37a1a","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T01:52:50Z","title_canon_sha256":"c1378eeb84bffe69ac7c8342cf1c20269b81eed9b169813e488ac0f0f44c55b5"},"schema_version":"1.0","source":{"id":"2605.24340","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24340","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24340v1","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24340","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"pith_short_12","alias_value":"BZWQ4MOSUYWH","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"pith_short_16","alias_value":"BZWQ4MOSUYWHG2RC","created_at":"2026-05-26T01:03:00Z"},{"alias_kind":"pith_short_8","alias_value":"BZWQ4MOS","created_at":"2026-05-26T01:03:00Z"}],"graph_snapshots":[{"event_id":"sha256:09bea6ce3ae79936a33e3d4037170841fa6796bf858fe95ffb421469d08e1338","target":"graph","created_at":"2026-05-26T01:03:00Z","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/2605.24340/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure: labeled data is expensive, inference budgets are tight, and models that cannot explain their behavior are difficult to trust and maintain. We present ChainzRule (CR), a neural architecture replacing typical activations with learnable polynomial layers governed by Differential Regularization (DREG), a layer-wise Jacobian penalty computed analytically during the forward pass at standard inference cost. The core claim is that bounding intermediate derivatives forces the","authors_text":"Rowan Martnishn","cross_cats":[],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T01:52:50Z","title":"ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24340","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":"sha256:9e1c7777e7d27375152b9c0e728932a3079bbd0e43f335cbb9ef2bcdd3fc9db7","target":"record","created_at":"2026-05-26T01:03:00Z","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":"089a23e6c89b95df17c1bf5ca0cb54ea9d07210daff50d686c16a60098c37a1a","cross_cats_sorted":[],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-23T01:52:50Z","title_canon_sha256":"c1378eeb84bffe69ac7c8342cf1c20269b81eed9b169813e488ac0f0f44c55b5"},"schema_version":"1.0","source":{"id":"2605.24340","kind":"arxiv","version":1}},"canonical_sha256":"0e6d0e31d2a62c736a224885bcf70309e4bd627a26847d089778193911d0500a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e6d0e31d2a62c736a224885bcf70309e4bd627a26847d089778193911d0500a","first_computed_at":"2026-05-26T01:03:00.759352Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:03:00.759352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"z08pxwvTaVBgLPlQdtZKFaywVJdacTMZiiXE5SOLwCIwv8E7MiXsk00d6lmdj2LGzoIn8a5UOpb3K0v5jBqoCw==","signature_status":"signed_v1","signed_at":"2026-05-26T01:03:00.759893Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.24340","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9e1c7777e7d27375152b9c0e728932a3079bbd0e43f335cbb9ef2bcdd3fc9db7","sha256:09bea6ce3ae79936a33e3d4037170841fa6796bf858fe95ffb421469d08e1338"],"state_sha256":"bf905ebc4b48a5b32a85ba77f3c1c8f7c0f94c23579e2c12428f58de699972bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y4jWOG5pa1npkIeDqEF3AHAzsPH7GUu8P2tRpOeW8QI5NIB1Y2MZSZIR+Fje2v6Wzkwxkq4UbDmD7R+vFKuhCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T00:56:00.667974Z","bundle_sha256":"522bd510a8f6f8c3e0e3f6413a6801f9c4c56d368315de4945ed154f0c7ba8e1"}}