{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FLXUEI7OGUUMA6Q4GI7MK4FMXG","short_pith_number":"pith:FLXUEI7O","schema_version":"1.0","canonical_sha256":"2aef4223ee3528c07a1c323ec570acb9a95748d49bf2b2f79d4ab95600b4264f","source":{"kind":"arxiv","id":"2508.16050","version":1},"attestation_state":"computed","paper":{"title":"Expandable Residual Approximation for Knowledge Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binghui Chen, Qixiang Ye, Yunfan Liu, Zhaoyi Yan","submitted_at":"2025-08-22T02:57:13Z","abstract_excerpt":"Knowledge distillation (KD) aims to transfer knowledge from a large-scale teacher model to a lightweight one, significantly reducing computational and storage requirements. However, the inherent learning capacity gap between the teacher and student often hinders the sufficient transfer of knowledge, motivating numerous studies to address this challenge. Inspired by the progressive approximation principle in the Stone-Weierstrass theorem, we propose Expandable Residual Approximation (ERA), a novel KD method that decomposes the approximation of residual knowledge into multiple steps, reducing th"},"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":"2508.16050","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-22T02:57:13Z","cross_cats_sorted":[],"title_canon_sha256":"07b18c0d6f96416aba8e850e1f5bd3c35ced3cbae66d37fb837d0f3399ac99a4","abstract_canon_sha256":"b291c904cbfb302389973c4807469986921d29cb363c97143c2a2aaa45d03975"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:40.775614Z","signature_b64":"ti55O9B0hs0bJpiaZz+6PlstQTilFKXf3xKMpF7u4iZW6IsO05H7Sb37PMsFpO42jeZp0Sf6sPR44IMj/q7RAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2aef4223ee3528c07a1c323ec570acb9a95748d49bf2b2f79d4ab95600b4264f","last_reissued_at":"2026-07-05T11:57:40.774991Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:40.774991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Expandable Residual Approximation for Knowledge Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Binghui Chen, Qixiang Ye, Yunfan Liu, Zhaoyi Yan","submitted_at":"2025-08-22T02:57:13Z","abstract_excerpt":"Knowledge distillation (KD) aims to transfer knowledge from a large-scale teacher model to a lightweight one, significantly reducing computational and storage requirements. However, the inherent learning capacity gap between the teacher and student often hinders the sufficient transfer of knowledge, motivating numerous studies to address this challenge. Inspired by the progressive approximation principle in the Stone-Weierstrass theorem, we propose Expandable Residual Approximation (ERA), a novel KD method that decomposes the approximation of residual knowledge into multiple steps, reducing th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.16050","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/2508.16050/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":"2508.16050","created_at":"2026-07-05T11:57:40.775052+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.16050v1","created_at":"2026-07-05T11:57:40.775052+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.16050","created_at":"2026-07-05T11:57:40.775052+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLXUEI7OGUUM","created_at":"2026-07-05T11:57:40.775052+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLXUEI7OGUUMA6Q4","created_at":"2026-07-05T11:57:40.775052+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLXUEI7O","created_at":"2026-07-05T11:57:40.775052+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/FLXUEI7OGUUMA6Q4GI7MK4FMXG","json":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG.json","graph_json":"https://pith.science/api/pith-number/FLXUEI7OGUUMA6Q4GI7MK4FMXG/graph.json","events_json":"https://pith.science/api/pith-number/FLXUEI7OGUUMA6Q4GI7MK4FMXG/events.json","paper":"https://pith.science/paper/FLXUEI7O"},"agent_actions":{"view_html":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG","download_json":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG.json","view_paper":"https://pith.science/paper/FLXUEI7O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.16050&json=true","fetch_graph":"https://pith.science/api/pith-number/FLXUEI7OGUUMA6Q4GI7MK4FMXG/graph.json","fetch_events":"https://pith.science/api/pith-number/FLXUEI7OGUUMA6Q4GI7MK4FMXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG/action/storage_attestation","attest_author":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG/action/author_attestation","sign_citation":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG/action/citation_signature","submit_replication":"https://pith.science/pith/FLXUEI7OGUUMA6Q4GI7MK4FMXG/action/replication_record"}},"created_at":"2026-07-05T11:57:40.775052+00:00","updated_at":"2026-07-05T11:57:40.775052+00:00"}