{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UXFNKFK4N44ZQPMNXUGHPNPP37","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7f7536f882e554141971f9caad724d1918a83bf12ed1a16e32d9b1f80b2a9acc","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T11:38:36Z","title_canon_sha256":"d9d8ed23f21443386c3f47f4271789b193c2395a2f6f0a2411141813e77b0257"},"schema_version":"1.0","source":{"id":"2605.12573","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12573","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12573v1","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12573","created_at":"2026-05-18T03:10:01Z"},{"alias_kind":"pith_short_12","alias_value":"UXFNKFK4N44Z","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"UXFNKFK4N44ZQPMN","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"UXFNKFK4","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:830bb36ed86dc8cc40a6cc0cb9e222455bdffa3ddf0d967cf24c19f2d187eba1","target":"graph","created_at":"2026-05-18T03:10:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"LAMP preserves the structure of a posterior sampler and improves the reverse transition via a bias-variance trade-off, as shown by one-step risk analysis and consistent gains over DiffPIR and DDRM without increasing denoising evaluations."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the second-order discretization term remains stable and beneficial across the full reverse trajectory when combined with the residual correction, rather than only in the one-step analysis."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"LAMP improves diffusion posterior sampling by adding a lagged temporal correction from second-order discretization while preserving the posterior structure."}],"snapshot_sha256":"6b7c48f310e5d1038cfe08d862bebf127cd78b2253dfc8c58c65148a37fcf0dd"},"formal_canon":{"evidence_count":3,"snapshot_sha256":"bba6777d8612f7f1826c2113d97da25304779aa2bff6fdfc22ffade269a76bec"},"paper":{"abstract_excerpt":"Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Yet, its standard formulations rely on instantaneous data-consistent estimates, which induce temporal variability in the reverse dynamics. We reinterpret PS from a dynamical perspective, showing that the standard PS update corresponds to a first-order discretization of the diffusion dynamics plus a residual correction capturing the mismatch between the denoised prediction and the data-consistent estimate. A second-order discretization, however, nat","authors_text":"Davide Evangelista, Elena Morotti, Francesco Pivi, Maurizio Gabbrielli","cross_cats":["cs.AI","cs.LG"],"headline":"LAMP improves diffusion posterior sampling by adding a lagged temporal correction from second-order discretization while preserving the posterior structure.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T11:38:36Z","title":"Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration"},"references":{"count":17,"internal_anchors":0,"resolved_work":17,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. InInternational Conference on Learning Representatio","work_id":"4e07eff4-123a-4a0d-83bf-2b0bc67ae774","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Improving diffusion models for inverse problems using manifold constraints.Advances in Neural Information Processing Systems, 35:25683–25696, 2022","work_id":"f867adf9-9c8b-4e3e-af5e-9ea543846030","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Diffusion models beat gans on image synthesis","work_id":"11a4fa70-50e4-4103-b58a-48259ce280d9","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Denoising diffusion probabilistic models","work_id":"007f689e-17d2-4add-8618-5321c85e056b","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Denoising diffusion restoration models","work_id":"30098793-59a0-459f-8bf7-e8529608aca4","year":2022}],"snapshot_sha256":"999bd6045af544ab4b449bd0738a175538d0bb72a21df240f823301effc5e9a7"},"source":{"id":"2605.12573","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:43:19.910008Z","id":"58d4ff02-a5b9-42e7-90f1-1d34546636dc","model_set":{"reader":"grok-4.3"},"one_line_summary":"LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"LAMP improves diffusion posterior sampling by adding a lagged temporal correction from second-order discretization while preserving the posterior structure.","strongest_claim":"LAMP preserves the structure of a posterior sampler and improves the reverse transition via a bias-variance trade-off, as shown by one-step risk analysis and consistent gains over DiffPIR and DDRM without increasing denoising evaluations.","weakest_assumption":"That the second-order discretization term remains stable and beneficial across the full reverse trajectory when combined with the residual correction, rather than only in the one-step analysis."}},"verdict_id":"58d4ff02-a5b9-42e7-90f1-1d34546636dc"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3c002e8e28077d5b5d9f35de498d8c7ba253a496a4abc0094c11e3748ec3c211","target":"record","created_at":"2026-05-18T03:10:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7f7536f882e554141971f9caad724d1918a83bf12ed1a16e32d9b1f80b2a9acc","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-12T11:38:36Z","title_canon_sha256":"d9d8ed23f21443386c3f47f4271789b193c2395a2f6f0a2411141813e77b0257"},"schema_version":"1.0","source":{"id":"2605.12573","kind":"arxiv","version":1}},"canonical_sha256":"a5cad5155c6f39983d8dbd0c77b5efdfde254571adc2b154c775b384d6738f89","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5cad5155c6f39983d8dbd0c77b5efdfde254571adc2b154c775b384d6738f89","first_computed_at":"2026-05-18T03:10:01.686835Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:10:01.686835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YsdFagbowYjD+c3STC8oBjmpHx2AblWN17ADrkZ8P96m9DSYlD4RL5exhvmJff2L6+BuqihUKQvQQ2nZDuRoAA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:10:01.687913Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12573","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c002e8e28077d5b5d9f35de498d8c7ba253a496a4abc0094c11e3748ec3c211","sha256:830bb36ed86dc8cc40a6cc0cb9e222455bdffa3ddf0d967cf24c19f2d187eba1"],"state_sha256":"224db08aacfd1c3b0d94039c7547bbd0488f3e661879416d6f7a077ffadb4208"}