{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TOUQ2E4YSZBV5IEBJ2CNTVIPWA","short_pith_number":"pith:TOUQ2E4Y","schema_version":"1.0","canonical_sha256":"9ba90d139896435ea0814e84d9d50fb0228361b65eeafeaa3abe5791d8d0825a","source":{"kind":"arxiv","id":"2606.24888","version":1},"attestation_state":"computed","paper":{"title":"DiffusionBench: On Holistic Evaluation of Diffusion Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aninda Saha, Ethan Smith, Jaskirat Singh, Liang Zheng, Martin Bell, Xingjian Leng, Yuhui Yuan, Zhanhao Liang","submitted_at":"2026-06-23T17:59:55Z","abstract_excerpt":"Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods improve the FID and related metrics, it is increasingly unclear whether they reflect real progress in generative modeling. The natural alternative, i.e., text-to-image (T2I) generation, is perceived as too costly or inconvenient to train and evaluate and is often skipped. We argue that this perception no longer holds. We introduce NanoGen, a unified DiT training and evaluation framework. NanoGen matches state-of-the-art DiT baselines on Im"},"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":"2606.24888","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T17:59:55Z","cross_cats_sorted":[],"title_canon_sha256":"54baa3a9fb6738317dc527ad22251d17371ad90041fbbcb6b34da3d7beeec7d4","abstract_canon_sha256":"8acfd16c11d30b84696304e2c7ca155b47fdd650ec19a246dfce7fbe96064e07"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:45.530221Z","signature_b64":"z/EHTERz+dg4wVDi6HPEn8wDAIheiRv790h2BkARj8LxHOtdi9x/fMnV0xqgMHVvgy5RKlZfEuUwzkJpPZKrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ba90d139896435ea0814e84d9d50fb0228361b65eeafeaa3abe5791d8d0825a","last_reissued_at":"2026-06-24T01:15:45.529825Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:45.529825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffusionBench: On Holistic Evaluation of Diffusion Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aninda Saha, Ethan Smith, Jaskirat Singh, Liang Zheng, Martin Bell, Xingjian Leng, Yuhui Yuan, Zhanhao Liang","submitted_at":"2026-06-23T17:59:55Z","abstract_excerpt":"Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods improve the FID and related metrics, it is increasingly unclear whether they reflect real progress in generative modeling. The natural alternative, i.e., text-to-image (T2I) generation, is perceived as too costly or inconvenient to train and evaluate and is often skipped. We argue that this perception no longer holds. We introduce NanoGen, a unified DiT training and evaluation framework. NanoGen matches state-of-the-art DiT baselines on Im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24888","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/2606.24888/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":"2606.24888","created_at":"2026-06-24T01:15:45.529882+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.24888v1","created_at":"2026-06-24T01:15:45.529882+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24888","created_at":"2026-06-24T01:15:45.529882+00:00"},{"alias_kind":"pith_short_12","alias_value":"TOUQ2E4YSZBV","created_at":"2026-06-24T01:15:45.529882+00:00"},{"alias_kind":"pith_short_16","alias_value":"TOUQ2E4YSZBV5IEB","created_at":"2026-06-24T01:15:45.529882+00:00"},{"alias_kind":"pith_short_8","alias_value":"TOUQ2E4Y","created_at":"2026-06-24T01:15:45.529882+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/TOUQ2E4YSZBV5IEBJ2CNTVIPWA","json":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA.json","graph_json":"https://pith.science/api/pith-number/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/graph.json","events_json":"https://pith.science/api/pith-number/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/events.json","paper":"https://pith.science/paper/TOUQ2E4Y"},"agent_actions":{"view_html":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA","download_json":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA.json","view_paper":"https://pith.science/paper/TOUQ2E4Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.24888&json=true","fetch_graph":"https://pith.science/api/pith-number/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/graph.json","fetch_events":"https://pith.science/api/pith-number/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/action/storage_attestation","attest_author":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/action/author_attestation","sign_citation":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/action/citation_signature","submit_replication":"https://pith.science/pith/TOUQ2E4YSZBV5IEBJ2CNTVIPWA/action/replication_record"}},"created_at":"2026-06-24T01:15:45.529882+00:00","updated_at":"2026-06-24T01:15:45.529882+00:00"}