{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XXGIRKYPCL7HQUA4GRPMEZAOXQ","short_pith_number":"pith:XXGIRKYP","canonical_record":{"source":{"id":"2503.16726","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-20T21:58:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0e156dc554240e93871e2c42af59bdd23c16a1a8ab98a64e98217689aa501936","abstract_canon_sha256":"7b3b1b566578f70751a594118163dd925b0d8104e3af4a1646b182ad970f0412"},"schema_version":"1.0"},"canonical_sha256":"bdcc88ab0f12fe78501c345ec2640ebc3326e1e400ad4a7c65f2d0ec97d9dc6f","source":{"kind":"arxiv","id":"2503.16726","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.16726","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"2503.16726v2","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16726","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"XXGIRKYPCL7H","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"pith_short_16","alias_value":"XXGIRKYPCL7HQUA4","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"pith_short_8","alias_value":"XXGIRKYP","created_at":"2026-07-05T11:51:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XXGIRKYPCL7HQUA4GRPMEZAOXQ","target":"record","payload":{"canonical_record":{"source":{"id":"2503.16726","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-20T21:58:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0e156dc554240e93871e2c42af59bdd23c16a1a8ab98a64e98217689aa501936","abstract_canon_sha256":"7b3b1b566578f70751a594118163dd925b0d8104e3af4a1646b182ad970f0412"},"schema_version":"1.0"},"canonical_sha256":"bdcc88ab0f12fe78501c345ec2640ebc3326e1e400ad4a7c65f2d0ec97d9dc6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:24.228562Z","signature_b64":"98Qnar9EU08N26JPqGql4Rkv7vR7QKlF2Rm32xWLJQHV3WyFvTq6g0Y95wSFCGi8ro8676Zp39sdxvSg6nb9DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdcc88ab0f12fe78501c345ec2640ebc3326e1e400ad4a7c65f2d0ec97d9dc6f","last_reissued_at":"2026-07-05T11:51:24.228077Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:24.228077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.16726","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yIxmXO1Hy090QhS4xAXd36JlWRNDOslYu8Z01BuJJtVSi1NxvK4yO4dM2kLU3ID2UJm1yRwbZFrOFDOW5ho2BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T19:29:47.098255Z"},"content_sha256":"2573aad9d327de723fdf13387179008876d719973586f299436d714bd512254e","schema_version":"1.0","event_id":"sha256:2573aad9d327de723fdf13387179008876d719973586f299436d714bd512254e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XXGIRKYPCL7HQUA4GRPMEZAOXQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EDiT: Efficient Diffusion Transformers with Linear Compressed Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Abhinav Mehrotra, Alberto Gil Ramos, Luca Morreale, Malcolm Chadwick, Mehdi Noroozi, Philipp Becker, Ruchika Chavhan, Sourav Bhattacharya","submitted_at":"2025-03-20T21:58:45Z","abstract_excerpt":"Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling properties of the attention in DiTs hinder image generation with higher resolution or on devices with limited resources. This work introduces an efficient diffusion transformer (EDiT) to alleviate these efficiency bottlenecks in conventional DiTs and Multimodal DiTs (MM-DiTs). First, we present a novel linear compressed attention method that uses a multi-layer convolutional network to modulate queries with local infor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16726","kind":"arxiv","version":2},"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/2503.16726/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:51:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8FgssGAsZhA7oZqak+mxUBKAR1S1ZvRku2LuDTUaJPgtDsu40X4ijZrwIztaGzKkJCl5uvGVilL2vLrjY/2WDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T19:29:47.098838Z"},"content_sha256":"d2ca8ce93fc79bb2456e74ab24b1bb43f3b053cde7d768921edf314d4c37b492","schema_version":"1.0","event_id":"sha256:d2ca8ce93fc79bb2456e74ab24b1bb43f3b053cde7d768921edf314d4c37b492"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ/bundle.json","state_url":"https://pith.science/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-20T19:29:47Z","links":{"resolver":"https://pith.science/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ","bundle":"https://pith.science/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ/bundle.json","state":"https://pith.science/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXGIRKYPCL7HQUA4GRPMEZAOXQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XXGIRKYPCL7HQUA4GRPMEZAOXQ","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":"7b3b1b566578f70751a594118163dd925b0d8104e3af4a1646b182ad970f0412","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-20T21:58:45Z","title_canon_sha256":"0e156dc554240e93871e2c42af59bdd23c16a1a8ab98a64e98217689aa501936"},"schema_version":"1.0","source":{"id":"2503.16726","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.16726","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"arxiv_version","alias_value":"2503.16726v2","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16726","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"pith_short_12","alias_value":"XXGIRKYPCL7H","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"pith_short_16","alias_value":"XXGIRKYPCL7HQUA4","created_at":"2026-07-05T11:51:24Z"},{"alias_kind":"pith_short_8","alias_value":"XXGIRKYP","created_at":"2026-07-05T11:51:24Z"}],"graph_snapshots":[{"event_id":"sha256:d2ca8ce93fc79bb2456e74ab24b1bb43f3b053cde7d768921edf314d4c37b492","target":"graph","created_at":"2026-07-05T11:51:24Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.16726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling properties of the attention in DiTs hinder image generation with higher resolution or on devices with limited resources. This work introduces an efficient diffusion transformer (EDiT) to alleviate these efficiency bottlenecks in conventional DiTs and Multimodal DiTs (MM-DiTs). First, we present a novel linear compressed attention method that uses a multi-layer convolutional network to modulate queries with local infor","authors_text":"Abhinav Mehrotra, Alberto Gil Ramos, Luca Morreale, Malcolm Chadwick, Mehdi Noroozi, Philipp Becker, Ruchika Chavhan, Sourav Bhattacharya","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-20T21:58:45Z","title":"EDiT: Efficient Diffusion Transformers with Linear Compressed Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16726","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2573aad9d327de723fdf13387179008876d719973586f299436d714bd512254e","target":"record","created_at":"2026-07-05T11:51:24Z","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":"7b3b1b566578f70751a594118163dd925b0d8104e3af4a1646b182ad970f0412","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-20T21:58:45Z","title_canon_sha256":"0e156dc554240e93871e2c42af59bdd23c16a1a8ab98a64e98217689aa501936"},"schema_version":"1.0","source":{"id":"2503.16726","kind":"arxiv","version":2}},"canonical_sha256":"bdcc88ab0f12fe78501c345ec2640ebc3326e1e400ad4a7c65f2d0ec97d9dc6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdcc88ab0f12fe78501c345ec2640ebc3326e1e400ad4a7c65f2d0ec97d9dc6f","first_computed_at":"2026-07-05T11:51:24.228077Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:51:24.228077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"98Qnar9EU08N26JPqGql4Rkv7vR7QKlF2Rm32xWLJQHV3WyFvTq6g0Y95wSFCGi8ro8676Zp39sdxvSg6nb9DA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:51:24.228562Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.16726","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2573aad9d327de723fdf13387179008876d719973586f299436d714bd512254e","sha256:d2ca8ce93fc79bb2456e74ab24b1bb43f3b053cde7d768921edf314d4c37b492"],"state_sha256":"a75b06af7992b3b9cebdc3cb15537df8d7d7a60390e8af742c6e4591195c8655"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WqVVH1aBbwBhbEoe9WGnRhO5JXSIzuDRB8lxwV9i8+8GLZ5pgwJci2yKr6hN3gsyKv1vEHHU5T3Mn+Z+Gpt8Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T19:29:47.104072Z","bundle_sha256":"3b29c6f874bd56081d48dcd8165a19218c78962ee69bad0b8b97c910ef77ee40"}}