{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:62BIAXUTEXEFQ7X23FHBAIZAW5","short_pith_number":"pith:62BIAXUT","schema_version":"1.0","canonical_sha256":"f682805e9325c8587efad94e102320b762cf9f37aff351947556a3aa950b1b45","source":{"kind":"arxiv","id":"2608.09133","version":1},"attestation_state":"computed","paper":{"title":"When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Yu Shi, Yu-Wing Tai, Yuyao Zhang","submitted_at":"2026-08-10T05:24:39Z","abstract_excerpt":"Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging. In particular, we observe that diffusion transformers (DiTs) built on latent representations suffer from a critical limitation: the compression bottleneck of the VAE weakens fine-grained spatial information, leading to hallucinated details that are weakly grounded in the input image. In this work, we revisit generative SR from a representation perspective and propose a pixel-grounded super-resolution (PGSR) framework t"},"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":"2608.09133","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-10T05:24:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a85f38f783c19a905623df80b896e81ed044a3f545185eee859f901c046c617d","abstract_canon_sha256":"e96b2905c85465969e00bdf3e591a125e736ff7d71418375d0ebf095109ae514"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T02:21:52.707979Z","signature_b64":"MbkJDmCud6WkRQ4WC0MQSJRwp2XbuN6JeVd4OCm7utMHviLEpuN7ingj2C7+xo/I+4ABbzYYUhmK6PY9aIFlCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f682805e9325c8587efad94e102320b762cf9f37aff351947556a3aa950b1b45","last_reissued_at":"2026-08-11T02:21:52.706491Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T02:21:52.706491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Yu Shi, Yu-Wing Tai, Yuyao Zhang","submitted_at":"2026-08-10T05:24:39Z","abstract_excerpt":"Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging. In particular, we observe that diffusion transformers (DiTs) built on latent representations suffer from a critical limitation: the compression bottleneck of the VAE weakens fine-grained spatial information, leading to hallucinated details that are weakly grounded in the input image. In this work, we revisit generative SR from a representation perspective and propose a pixel-grounded super-resolution (PGSR) framework t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.09133","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/2608.09133/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":"2608.09133","created_at":"2026-08-11T02:21:52.707056+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.09133v1","created_at":"2026-08-11T02:21:52.707056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.09133","created_at":"2026-08-11T02:21:52.707056+00:00"},{"alias_kind":"pith_short_12","alias_value":"62BIAXUTEXEF","created_at":"2026-08-11T02:21:52.707056+00:00"},{"alias_kind":"pith_short_16","alias_value":"62BIAXUTEXEFQ7X2","created_at":"2026-08-11T02:21:52.707056+00:00"},{"alias_kind":"pith_short_8","alias_value":"62BIAXUT","created_at":"2026-08-11T02:21:52.707056+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/62BIAXUTEXEFQ7X23FHBAIZAW5","json":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5.json","graph_json":"https://pith.science/api/pith-number/62BIAXUTEXEFQ7X23FHBAIZAW5/graph.json","events_json":"https://pith.science/api/pith-number/62BIAXUTEXEFQ7X23FHBAIZAW5/events.json","paper":"https://pith.science/paper/62BIAXUT"},"agent_actions":{"view_html":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5","download_json":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5.json","view_paper":"https://pith.science/paper/62BIAXUT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.09133&json=true","fetch_graph":"https://pith.science/api/pith-number/62BIAXUTEXEFQ7X23FHBAIZAW5/graph.json","fetch_events":"https://pith.science/api/pith-number/62BIAXUTEXEFQ7X23FHBAIZAW5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5/action/storage_attestation","attest_author":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5/action/author_attestation","sign_citation":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5/action/citation_signature","submit_replication":"https://pith.science/pith/62BIAXUTEXEFQ7X23FHBAIZAW5/action/replication_record"}},"created_at":"2026-08-11T02:21:52.707056+00:00","updated_at":"2026-08-11T02:21:52.707056+00:00"}