{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4GJLB7MB7RRNRK2SDWEFBQKOCX","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":"99ff4ab10881071e8412484be29d33932ea7a2c931d9535daa8455aef5a5bb4e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-12T07:55:43Z","title_canon_sha256":"427d1b1523f5691fba754f0919e211f3c74d2dffd6efdd7d1fd904bc4d02d05b"},"schema_version":"1.0","source":{"id":"2405.07200","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.07200","created_at":"2026-07-05T08:31:50Z"},{"alias_kind":"arxiv_version","alias_value":"2405.07200v3","created_at":"2026-07-05T08:31:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07200","created_at":"2026-07-05T08:31:50Z"},{"alias_kind":"pith_short_12","alias_value":"4GJLB7MB7RRN","created_at":"2026-07-05T08:31:50Z"},{"alias_kind":"pith_short_16","alias_value":"4GJLB7MB7RRNRK2S","created_at":"2026-07-05T08:31:50Z"},{"alias_kind":"pith_short_8","alias_value":"4GJLB7MB","created_at":"2026-07-05T08:31:50Z"}],"graph_snapshots":[{"event_id":"sha256:a458fad9ba18c98a89a454bb4144538458f04e3f9a98036719b345c65c7b07cc","target":"graph","created_at":"2026-07-05T08:31:50Z","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/2405.07200/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate approximation of complex nonlinear functions is a fundamental challenge across many scientific and engineering domains. Traditional neural network architectures, such as Multi-Layer Perceptrons (MLPs), often struggle to efficiently capture intricate patterns and irregularities present in high-dimensional functions. This paper presents the Chebyshev Kolmogorov-Arnold Network (Chebyshev KAN), a new neural network architecture inspired by the Kolmogorov-Arnold representation theorem, incorporating the powerful approximation capabilities of Chebyshev polynomials. By utilizing learnable fu","authors_text":"Anas KP, Gokul R, Keerthana AR, Sidharth SS","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-12T07:55:43Z","title":"Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07200","kind":"arxiv","version":3},"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":"sha256:1efba23910b627116e36332a0f41cf28436bd4d89997a6b1d004066867175909","target":"record","created_at":"2026-07-05T08:31:50Z","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":"99ff4ab10881071e8412484be29d33932ea7a2c931d9535daa8455aef5a5bb4e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-12T07:55:43Z","title_canon_sha256":"427d1b1523f5691fba754f0919e211f3c74d2dffd6efdd7d1fd904bc4d02d05b"},"schema_version":"1.0","source":{"id":"2405.07200","kind":"arxiv","version":3}},"canonical_sha256":"e192b0fd81fc62d8ab521d8850c14e15fe89a32b2ba74240d07d1d842e67be28","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e192b0fd81fc62d8ab521d8850c14e15fe89a32b2ba74240d07d1d842e67be28","first_computed_at":"2026-07-05T08:31:50.994936Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:31:50.994936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jECv+ZJsWlVa0wW4UhTGdPI/Tma4cY6Q5E0EHIqS1mMeKW8d2zb4ZTaBzaf4uOrAGx1Si/dzZtM0EZkx2l7fDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:31:50.995559Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.07200","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1efba23910b627116e36332a0f41cf28436bd4d89997a6b1d004066867175909","sha256:a458fad9ba18c98a89a454bb4144538458f04e3f9a98036719b345c65c7b07cc"],"state_sha256":"f549261f96ac14722e3202247970bda7b03fa5786112ef4f050bb6e05996ae06"}