{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XUFL4QCSO3X7A46KN2YSY6VVJL","short_pith_number":"pith:XUFL4QCS","schema_version":"1.0","canonical_sha256":"bd0abe405276eff073ca6eb12c7ab54ad85f719b060846625fe8ec23a3a8c1b0","source":{"kind":"arxiv","id":"2606.27862","version":1},"attestation_state":"computed","paper":{"title":"ScaLe-INR: Scale and Learn Implicit Neural Representations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Athulya Ratnayake, Avishka Ranasinghe, Buwaneka Epakanda, Mario De Silva, Pandula Thennakoon, Parakrama Ekanayake, Roshan Godaliyadda","submitted_at":"2026-06-26T09:02:48Z","abstract_excerpt":"Implicit Neural Representations (INRs) parameterized by multilayer perceptrons excel at modeling continuous signals. However, a key challenge persists as INRs fundamentally suffer from spectral bias and information cross-talk. When a single network attempts to capture multi-scale phenomena, high-frequency weight updates destructively interfere with the underlying low-frequency structural approximation. We introduce Scale and Learn INR (ScaLe-INR), a novel multi-branch architecture that resolves these limitations by explicitly matching the signal's frequency spectrum with the optimal operating "},"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":"2606.27862","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-26T09:02:48Z","cross_cats_sorted":[],"title_canon_sha256":"b52f1167c4da50972d2580b7cc06f4592e7af0e3e02b95d2eb49074f9a737f27","abstract_canon_sha256":"cea73b28ba751c118de6d2ec9dbf02781e714fffde8591bec1fdeb42ff1283b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:50.994031Z","signature_b64":"Mn1ObMDkYhvzrogxjtbxfOfZ1kVzjZU0Hb2R2BfEFXk5azZO2osQPaQqdefAS0n6PfKiVS2ZfTjwi/Us/f2xCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd0abe405276eff073ca6eb12c7ab54ad85f719b060846625fe8ec23a3a8c1b0","last_reissued_at":"2026-06-29T01:14:50.993624Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:50.993624Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ScaLe-INR: Scale and Learn Implicit Neural Representations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Athulya Ratnayake, Avishka Ranasinghe, Buwaneka Epakanda, Mario De Silva, Pandula Thennakoon, Parakrama Ekanayake, Roshan Godaliyadda","submitted_at":"2026-06-26T09:02:48Z","abstract_excerpt":"Implicit Neural Representations (INRs) parameterized by multilayer perceptrons excel at modeling continuous signals. However, a key challenge persists as INRs fundamentally suffer from spectral bias and information cross-talk. When a single network attempts to capture multi-scale phenomena, high-frequency weight updates destructively interfere with the underlying low-frequency structural approximation. We introduce Scale and Learn INR (ScaLe-INR), a novel multi-branch architecture that resolves these limitations by explicitly matching the signal's frequency spectrum with the optimal operating "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27862","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/2606.27862/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":"2606.27862","created_at":"2026-06-29T01:14:50.993690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27862v1","created_at":"2026-06-29T01:14:50.993690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27862","created_at":"2026-06-29T01:14:50.993690+00:00"},{"alias_kind":"pith_short_12","alias_value":"XUFL4QCSO3X7","created_at":"2026-06-29T01:14:50.993690+00:00"},{"alias_kind":"pith_short_16","alias_value":"XUFL4QCSO3X7A46K","created_at":"2026-06-29T01:14:50.993690+00:00"},{"alias_kind":"pith_short_8","alias_value":"XUFL4QCS","created_at":"2026-06-29T01:14:50.993690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL","json":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL.json","graph_json":"https://pith.science/api/pith-number/XUFL4QCSO3X7A46KN2YSY6VVJL/graph.json","events_json":"https://pith.science/api/pith-number/XUFL4QCSO3X7A46KN2YSY6VVJL/events.json","paper":"https://pith.science/paper/XUFL4QCS"},"agent_actions":{"view_html":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL","download_json":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL.json","view_paper":"https://pith.science/paper/XUFL4QCS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27862&json=true","fetch_graph":"https://pith.science/api/pith-number/XUFL4QCSO3X7A46KN2YSY6VVJL/graph.json","fetch_events":"https://pith.science/api/pith-number/XUFL4QCSO3X7A46KN2YSY6VVJL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL/action/storage_attestation","attest_author":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL/action/author_attestation","sign_citation":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL/action/citation_signature","submit_replication":"https://pith.science/pith/XUFL4QCSO3X7A46KN2YSY6VVJL/action/replication_record"}},"created_at":"2026-06-29T01:14:50.993690+00:00","updated_at":"2026-06-29T01:14:50.993690+00:00"}