{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EK6NRGXWQQCVMLWA2ILUIMNKB2","short_pith_number":"pith:EK6NRGXW","schema_version":"1.0","canonical_sha256":"22bcd89af68405562ec0d2174431aa0e811a0e68e017693eec51170d27aa7d1c","source":{"kind":"arxiv","id":"2605.17759","version":1},"attestation_state":"computed","paper":{"title":"FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingling Fu, Junshi Huang, Lichen Ma, Luohang Liu, Xiaolong Fu, Yan Li, Yu He, Zipeng Guo","submitted_at":"2026-05-18T02:25:07Z","abstract_excerpt":"To circumvent the inherent fidelity bottlenecks and optimization misalignment of VAE-based latent diffusion, pixel-space diffusion models have emerged as a compelling end-to-end paradigm. However, existing pixel diffusion models often struggle to balance computational efficiency with the preservation of high-frequency details. They frequently resort to patch-based compression or restricted local decoding, leading to a \"spectral compromise\" where high-frequency and fine-grained pixel information are suppressed. To address these challenges, we propose \\textbf{FrequencyBooster}, a novel framework"},"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":"2605.17759","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T02:25:07Z","cross_cats_sorted":[],"title_canon_sha256":"1d8ae6eb2625a0cd534ab92e89052a5f22e2f40c5db77ad5458a1327209b9b0c","abstract_canon_sha256":"055f28749443ca8c9420d6096e1130bb248f49cc8960980fbf68d5223d850fde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:56.812882Z","signature_b64":"fkshVTa80481MYfcZyQb0WQhP1Wf8zk82m40dHnZh0mcyDGKsB/7bQmlMGOsy1no3AmionUzBXjrkW87694bAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22bcd89af68405562ec0d2174431aa0e811a0e68e017693eec51170d27aa7d1c","last_reissued_at":"2026-05-20T00:04:56.812136Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:56.812136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingling Fu, Junshi Huang, Lichen Ma, Luohang Liu, Xiaolong Fu, Yan Li, Yu He, Zipeng Guo","submitted_at":"2026-05-18T02:25:07Z","abstract_excerpt":"To circumvent the inherent fidelity bottlenecks and optimization misalignment of VAE-based latent diffusion, pixel-space diffusion models have emerged as a compelling end-to-end paradigm. However, existing pixel diffusion models often struggle to balance computational efficiency with the preservation of high-frequency details. They frequently resort to patch-based compression or restricted local decoding, leading to a \"spectral compromise\" where high-frequency and fine-grained pixel information are suppressed. To address these challenges, we propose \\textbf{FrequencyBooster}, a novel framework"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17759","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/2605.17759/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":"2605.17759","created_at":"2026-05-20T00:04:56.812290+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17759v1","created_at":"2026-05-20T00:04:56.812290+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17759","created_at":"2026-05-20T00:04:56.812290+00:00"},{"alias_kind":"pith_short_12","alias_value":"EK6NRGXWQQCV","created_at":"2026-05-20T00:04:56.812290+00:00"},{"alias_kind":"pith_short_16","alias_value":"EK6NRGXWQQCVMLWA","created_at":"2026-05-20T00:04:56.812290+00:00"},{"alias_kind":"pith_short_8","alias_value":"EK6NRGXW","created_at":"2026-05-20T00:04:56.812290+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/EK6NRGXWQQCVMLWA2ILUIMNKB2","json":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2.json","graph_json":"https://pith.science/api/pith-number/EK6NRGXWQQCVMLWA2ILUIMNKB2/graph.json","events_json":"https://pith.science/api/pith-number/EK6NRGXWQQCVMLWA2ILUIMNKB2/events.json","paper":"https://pith.science/paper/EK6NRGXW"},"agent_actions":{"view_html":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2","download_json":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2.json","view_paper":"https://pith.science/paper/EK6NRGXW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17759&json=true","fetch_graph":"https://pith.science/api/pith-number/EK6NRGXWQQCVMLWA2ILUIMNKB2/graph.json","fetch_events":"https://pith.science/api/pith-number/EK6NRGXWQQCVMLWA2ILUIMNKB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2/action/storage_attestation","attest_author":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2/action/author_attestation","sign_citation":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2/action/citation_signature","submit_replication":"https://pith.science/pith/EK6NRGXWQQCVMLWA2ILUIMNKB2/action/replication_record"}},"created_at":"2026-05-20T00:04:56.812290+00:00","updated_at":"2026-05-20T00:04:56.812290+00:00"}