{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6YZX535JX4V3336WCCPHVSCPED","short_pith_number":"pith:6YZX535J","canonical_record":{"source":{"id":"2601.08303","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-13T07:46:46Z","cross_cats_sorted":[],"title_canon_sha256":"c0cc1c16b6916d4607696a5893c95f3bc5b84464ea4c90b2b24ae27190ce146c","abstract_canon_sha256":"10780dcafd6b30d181b9b8874d3b96ad299054fcc0c11c231800ad2d0b27040f"},"schema_version":"1.0"},"canonical_sha256":"f6337eefa9bf2bbdefd6109e7ac84f20dd07b8f5abbb4a406f3f89c4ad0275e5","source":{"kind":"arxiv","id":"2601.08303","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.08303","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"arxiv_version","alias_value":"2601.08303v3","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.08303","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"pith_short_12","alias_value":"6YZX535JX4V3","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"pith_short_16","alias_value":"6YZX535JX4V3336W","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"pith_short_8","alias_value":"6YZX535J","created_at":"2026-07-07T02:19:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6YZX535JX4V3336WCCPHVSCPED","target":"record","payload":{"canonical_record":{"source":{"id":"2601.08303","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-13T07:46:46Z","cross_cats_sorted":[],"title_canon_sha256":"c0cc1c16b6916d4607696a5893c95f3bc5b84464ea4c90b2b24ae27190ce146c","abstract_canon_sha256":"10780dcafd6b30d181b9b8874d3b96ad299054fcc0c11c231800ad2d0b27040f"},"schema_version":"1.0"},"canonical_sha256":"f6337eefa9bf2bbdefd6109e7ac84f20dd07b8f5abbb4a406f3f89c4ad0275e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:19:45.006442Z","signature_b64":"3Gmf16oZj0Hw3ZzbOjem9l3DO3kYA/QOmxAn+6JiKDh4tPS7byWQKFez2GEfxMG9BwZqYWxWhDG/wbIMXYASDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6337eefa9bf2bbdefd6109e7ac84f20dd07b8f5abbb4a406f3f89c4ad0275e5","last_reissued_at":"2026-07-07T02:19:45.005496Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:19:45.005496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.08303","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-07T02:19:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IZOFNO4IQcUPHtZJ0wnSwzVcjIS/q5+ZpO+HVQfW+uxNF9QDknGMbujaT++Qdi7xQs0waaKGU1T4uIeVGnwkDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T17:56:58.678705Z"},"content_sha256":"274b747b05f6541a8b3da8cf5d022f43ba60b5182df46500bb4e37996e99563f","schema_version":"1.0","event_id":"sha256:274b747b05f6541a8b3da8cf5d022f43ba60b5182df46500bb4e37996e99563f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6YZX535JX4V3336WCCPHVSCPED","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aarush Gupta, Ahsan Mahmood, Aleksei Lebedev, Anil Kag, Anujraaj Goyal, Arpit Sahni, Dishani Lahiri, Dongting Hu, Huseyin Coskun, Ju Hu, Magzhan Gabidolla, Mingming Gong, Sergey Tulyakov, Yanyu Li, Yerlan Idelbayev","submitted_at":"2026-01-13T07:46:46Z","abstract_excerpt":"Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and memory costs. In this work, we present an efficient DiT framework tailored for mobile and edge devices that achieves transformer-level generation quality under strict resource constraints. Our design combines three key components. First, we propose a compact DiT architecture with an adaptive global-local sparse attention mechanism that balances global context modeling and local detail preservation. Second, we propose an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.08303","kind":"arxiv","version":3},"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/2601.08303/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":"2026-07-07T02:19:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vC9Zdjpm7cy5Hf4eCbd8/9Pw8CLV3ZgzbRTf+jB8CsfzA3+JuBQL+3buN00NnQjXXSuFpr1f8NFzEkFPQr1QCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T17:56:58.679194Z"},"content_sha256":"4918ba9e143db7974e32854baf8c20ef054a8bc43bd26c3019e9aaabc8d80b35","schema_version":"1.0","event_id":"sha256:4918ba9e143db7974e32854baf8c20ef054a8bc43bd26c3019e9aaabc8d80b35"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6YZX535JX4V3336WCCPHVSCPED/bundle.json","state_url":"https://pith.science/pith/6YZX535JX4V3336WCCPHVSCPED/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6YZX535JX4V3336WCCPHVSCPED/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":"2026-08-21T17:56:58Z","links":{"resolver":"https://pith.science/pith/6YZX535JX4V3336WCCPHVSCPED","bundle":"https://pith.science/pith/6YZX535JX4V3336WCCPHVSCPED/bundle.json","state":"https://pith.science/pith/6YZX535JX4V3336WCCPHVSCPED/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6YZX535JX4V3336WCCPHVSCPED/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6YZX535JX4V3336WCCPHVSCPED","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":"10780dcafd6b30d181b9b8874d3b96ad299054fcc0c11c231800ad2d0b27040f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-13T07:46:46Z","title_canon_sha256":"c0cc1c16b6916d4607696a5893c95f3bc5b84464ea4c90b2b24ae27190ce146c"},"schema_version":"1.0","source":{"id":"2601.08303","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.08303","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"arxiv_version","alias_value":"2601.08303v3","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.08303","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"pith_short_12","alias_value":"6YZX535JX4V3","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"pith_short_16","alias_value":"6YZX535JX4V3336W","created_at":"2026-07-07T02:19:45Z"},{"alias_kind":"pith_short_8","alias_value":"6YZX535J","created_at":"2026-07-07T02:19:45Z"}],"graph_snapshots":[{"event_id":"sha256:4918ba9e143db7974e32854baf8c20ef054a8bc43bd26c3019e9aaabc8d80b35","target":"graph","created_at":"2026-07-07T02:19:45Z","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/2601.08303/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and memory costs. In this work, we present an efficient DiT framework tailored for mobile and edge devices that achieves transformer-level generation quality under strict resource constraints. Our design combines three key components. First, we propose a compact DiT architecture with an adaptive global-local sparse attention mechanism that balances global context modeling and local detail preservation. Second, we propose an","authors_text":"Aarush Gupta, Ahsan Mahmood, Aleksei Lebedev, Anil Kag, Anujraaj Goyal, Arpit Sahni, Dishani Lahiri, Dongting Hu, Huseyin Coskun, Ju Hu, Magzhan Gabidolla, Mingming Gong, Sergey Tulyakov, Yanyu Li, Yerlan Idelbayev","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-13T07:46:46Z","title":"SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.08303","kind":"arxiv","version":3},"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":"sha256:274b747b05f6541a8b3da8cf5d022f43ba60b5182df46500bb4e37996e99563f","target":"record","created_at":"2026-07-07T02:19:45Z","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":"10780dcafd6b30d181b9b8874d3b96ad299054fcc0c11c231800ad2d0b27040f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-13T07:46:46Z","title_canon_sha256":"c0cc1c16b6916d4607696a5893c95f3bc5b84464ea4c90b2b24ae27190ce146c"},"schema_version":"1.0","source":{"id":"2601.08303","kind":"arxiv","version":3}},"canonical_sha256":"f6337eefa9bf2bbdefd6109e7ac84f20dd07b8f5abbb4a406f3f89c4ad0275e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f6337eefa9bf2bbdefd6109e7ac84f20dd07b8f5abbb4a406f3f89c4ad0275e5","first_computed_at":"2026-07-07T02:19:45.005496Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T02:19:45.005496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3Gmf16oZj0Hw3ZzbOjem9l3DO3kYA/QOmxAn+6JiKDh4tPS7byWQKFez2GEfxMG9BwZqYWxWhDG/wbIMXYASDA==","signature_status":"signed_v1","signed_at":"2026-07-07T02:19:45.006442Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.08303","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:274b747b05f6541a8b3da8cf5d022f43ba60b5182df46500bb4e37996e99563f","sha256:4918ba9e143db7974e32854baf8c20ef054a8bc43bd26c3019e9aaabc8d80b35"],"state_sha256":"d9e22a44ae3ef7e640bfe107c06bfbdf61613539de9737361c72083272874cd5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GUohpDsc3672MWh5lW68Mdbk1BJU1VTmcfvFnzzIRt0+cCXRDV3wzmAMRrVPINGK1bMz9amUN9zunRd+lGZzAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T17:56:58.682635Z","bundle_sha256":"8308e04e418f2ca623b03335bd8b53e8ec919af8bd2350d5826f72caaac222d9"}}