{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6YZYMCKZHM3ZKIHP4YKENYL7VJ","short_pith_number":"pith:6YZYMCKZ","schema_version":"1.0","canonical_sha256":"f6338609593b379520efe61446e17faa4a6c0e8ae87c15eb4d3e2c6da715f3fc","source":{"kind":"arxiv","id":"2505.21319","version":1},"attestation_state":"computed","paper":{"title":"efunc: An Efficient Function Representation without Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Biao Zhang, Peter Wonka","submitted_at":"2025-05-27T15:16:56Z","abstract_excerpt":"Function fitting/approximation plays a fundamental role in computer graphics and other engineering applications. While recent advances have explored neural networks to address this task, these methods often rely on architectures with many parameters, limiting their practical applicability. In contrast, we pursue high-quality function approximation using parameter-efficient representations that eliminate the dependency on neural networks entirely. We first propose a novel framework for continuous function modeling. Most existing works can be formulated using this framework. We then introduce a "},"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":"2505.21319","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-05-27T15:16:56Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2b3a1eeb931dfa34517ac272be8219a0e702f89d7e5513c0c06dfbf069d0d44e","abstract_canon_sha256":"a1b746a6d1412d6c185077ac3201a9e62a0e3191ae35696ab9858ded8d358328"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:40.107041Z","signature_b64":"Ue8/5mFxwTbBMtOyLSl3TxSYN6M6CS7u8JMgsWJANEppR8ZZOzG/sPEUXay2H/1wSCyuloUKBVlxrUhbjUneBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6338609593b379520efe61446e17faa4a6c0e8ae87c15eb4d3e2c6da715f3fc","last_reissued_at":"2026-07-05T11:10:40.106540Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:40.106540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"efunc: An Efficient Function Representation without Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Biao Zhang, Peter Wonka","submitted_at":"2025-05-27T15:16:56Z","abstract_excerpt":"Function fitting/approximation plays a fundamental role in computer graphics and other engineering applications. While recent advances have explored neural networks to address this task, these methods often rely on architectures with many parameters, limiting their practical applicability. In contrast, we pursue high-quality function approximation using parameter-efficient representations that eliminate the dependency on neural networks entirely. We first propose a novel framework for continuous function modeling. Most existing works can be formulated using this framework. We then introduce a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21319","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/2505.21319/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":"2505.21319","created_at":"2026-07-05T11:10:40.106602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.21319v1","created_at":"2026-07-05T11:10:40.106602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21319","created_at":"2026-07-05T11:10:40.106602+00:00"},{"alias_kind":"pith_short_12","alias_value":"6YZYMCKZHM3Z","created_at":"2026-07-05T11:10:40.106602+00:00"},{"alias_kind":"pith_short_16","alias_value":"6YZYMCKZHM3ZKIHP","created_at":"2026-07-05T11:10:40.106602+00:00"},{"alias_kind":"pith_short_8","alias_value":"6YZYMCKZ","created_at":"2026-07-05T11:10:40.106602+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16266","citing_title":"Patchwork: A compact representation for 3D polygonal shapes","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ","json":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ.json","graph_json":"https://pith.science/api/pith-number/6YZYMCKZHM3ZKIHP4YKENYL7VJ/graph.json","events_json":"https://pith.science/api/pith-number/6YZYMCKZHM3ZKIHP4YKENYL7VJ/events.json","paper":"https://pith.science/paper/6YZYMCKZ"},"agent_actions":{"view_html":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ","download_json":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ.json","view_paper":"https://pith.science/paper/6YZYMCKZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.21319&json=true","fetch_graph":"https://pith.science/api/pith-number/6YZYMCKZHM3ZKIHP4YKENYL7VJ/graph.json","fetch_events":"https://pith.science/api/pith-number/6YZYMCKZHM3ZKIHP4YKENYL7VJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ/action/storage_attestation","attest_author":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ/action/author_attestation","sign_citation":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ/action/citation_signature","submit_replication":"https://pith.science/pith/6YZYMCKZHM3ZKIHP4YKENYL7VJ/action/replication_record"}},"created_at":"2026-07-05T11:10:40.106602+00:00","updated_at":"2026-07-05T11:10:40.106602+00:00"}