{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LXE3QQRNWJYGKRLLVXU7Y5BVRQ","short_pith_number":"pith:LXE3QQRN","schema_version":"1.0","canonical_sha256":"5dc9b8422db27065456bade9fc74358c0d9b1aa36b7c4909e49766cf3b5ec8e6","source":{"kind":"arxiv","id":"2608.02062","version":1},"attestation_state":"computed","paper":{"title":"A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Enver Cavus, Osman Tokluoglu, Sude Ertan","submitted_at":"2026-08-03T11:07:20Z","abstract_excerpt":"Faster-than-Nyquist signaling improves spectral ef- ficiency by deliberately introducing inter-symbol interference. Classical sequence detectors such as BCJR can approach optimal performance, but their computational cost grows rapidly with channel memory. This paper investigates data-driven FTN BPSK detection under AWGN through a direct comparison between multilayer perceptrons and Kolmogorov Arnold Networks. A large-scale Monte Carlo dataset containing nearly four million labeled windows is generated for a time-packing factor of zero point eight and signal-to-noise ratio values from seven to "},"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":"2608.02062","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-08-03T11:07:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"233923f426155bf62652968e66c379f347b3d2e4419e9460db375695442fad1e","abstract_canon_sha256":"e58ffd6fb9272b019f0a0a095dcb995bcc8b742ba182592e96d34e8af20f54e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T02:10:46.162586Z","signature_b64":"LRU9WZ888mcPpiZSq45546rX8SKZgjIcfUu3nnVi0laCy8CafVWK749vUf2CS36E2yWH6vvg3tCoKUeosRk0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5dc9b8422db27065456bade9fc74358c0d9b1aa36b7c4909e49766cf3b5ec8e6","last_reissued_at":"2026-08-04T02:10:46.160984Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T02:10:46.160984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Enver Cavus, Osman Tokluoglu, Sude Ertan","submitted_at":"2026-08-03T11:07:20Z","abstract_excerpt":"Faster-than-Nyquist signaling improves spectral ef- ficiency by deliberately introducing inter-symbol interference. Classical sequence detectors such as BCJR can approach optimal performance, but their computational cost grows rapidly with channel memory. This paper investigates data-driven FTN BPSK detection under AWGN through a direct comparison between multilayer perceptrons and Kolmogorov Arnold Networks. A large-scale Monte Carlo dataset containing nearly four million labeled windows is generated for a time-packing factor of zero point eight and signal-to-noise ratio values from seven to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.02062","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/2608.02062/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":"2608.02062","created_at":"2026-08-04T02:10:46.162505+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.02062v1","created_at":"2026-08-04T02:10:46.162505+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.02062","created_at":"2026-08-04T02:10:46.162505+00:00"},{"alias_kind":"pith_short_12","alias_value":"LXE3QQRNWJYG","created_at":"2026-08-04T02:10:46.162505+00:00"},{"alias_kind":"pith_short_16","alias_value":"LXE3QQRNWJYGKRLL","created_at":"2026-08-04T02:10:46.162505+00:00"},{"alias_kind":"pith_short_8","alias_value":"LXE3QQRN","created_at":"2026-08-04T02:10:46.162505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.04155","citing_title":"Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ","json":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ.json","graph_json":"https://pith.science/api/pith-number/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/graph.json","events_json":"https://pith.science/api/pith-number/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/events.json","paper":"https://pith.science/paper/LXE3QQRN"},"agent_actions":{"view_html":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ","download_json":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ.json","view_paper":"https://pith.science/paper/LXE3QQRN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.02062&json=true","fetch_graph":"https://pith.science/api/pith-number/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/graph.json","fetch_events":"https://pith.science/api/pith-number/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/action/storage_attestation","attest_author":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/action/author_attestation","sign_citation":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/action/citation_signature","submit_replication":"https://pith.science/pith/LXE3QQRNWJYGKRLLVXU7Y5BVRQ/action/replication_record"}},"created_at":"2026-08-04T02:10:46.162505+00:00","updated_at":"2026-08-04T02:10:46.162505+00:00"}