{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WUPPP64FTKJGX744NF5NEMXVHR","short_pith_number":"pith:WUPPP64F","schema_version":"1.0","canonical_sha256":"b51ef7fb859a926bff9c697ad232f53c5d5de53391fda6227010f969ea57df43","source":{"kind":"arxiv","id":"2204.08734","version":2},"attestation_state":"computed","paper":{"title":"Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Jiazhen Gu, Xin Wang, Xuchuan Luo, Yangfan Zhou","submitted_at":"2022-04-19T08:21:23Z","abstract_excerpt":"Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can cause severe consequences. Unfortunately, existing testing approaches still cannot comprehensively exercise DL libraries. They utilize existing trained models and only detect bugs in model inference phase. In this work we propose Muffin to address these issues. To this end, Muffin applies a specifically-designed model fuzzing approach, which allows it to genera"},"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":"2204.08734","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2022-04-19T08:21:23Z","cross_cats_sorted":[],"title_canon_sha256":"db70339ec4c38cbbcadf118e8ab04b9837b5258504e625f1d5048d3f4223d00b","abstract_canon_sha256":"84e9f585c01aa6da8e0856c5518e8e4987933f4dce3cd52cc4e86efedf130e80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:21:12.608626Z","signature_b64":"P/Thha7sjQfg9ko4O1IZ307fxIkS6DnpBN2yqDKRHCyQW3huo+aeWGI8MiqamY8nAa2EADMxEWHPo2HsZK4MDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b51ef7fb859a926bff9c697ad232f53c5d5de53391fda6227010f969ea57df43","last_reissued_at":"2026-07-05T04:21:12.608185Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:21:12.608185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Jiazhen Gu, Xin Wang, Xuchuan Luo, Yangfan Zhou","submitted_at":"2022-04-19T08:21:23Z","abstract_excerpt":"Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can cause severe consequences. Unfortunately, existing testing approaches still cannot comprehensively exercise DL libraries. They utilize existing trained models and only detect bugs in model inference phase. In this work we propose Muffin to address these issues. To this end, Muffin applies a specifically-designed model fuzzing approach, which allows it to genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.08734","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/2204.08734/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":"2204.08734","created_at":"2026-07-05T04:21:12.608246+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.08734v2","created_at":"2026-07-05T04:21:12.608246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.08734","created_at":"2026-07-05T04:21:12.608246+00:00"},{"alias_kind":"pith_short_12","alias_value":"WUPPP64FTKJG","created_at":"2026-07-05T04:21:12.608246+00:00"},{"alias_kind":"pith_short_16","alias_value":"WUPPP64FTKJGX744","created_at":"2026-07-05T04:21:12.608246+00:00"},{"alias_kind":"pith_short_8","alias_value":"WUPPP64F","created_at":"2026-07-05T04:21:12.608246+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/WUPPP64FTKJGX744NF5NEMXVHR","json":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR.json","graph_json":"https://pith.science/api/pith-number/WUPPP64FTKJGX744NF5NEMXVHR/graph.json","events_json":"https://pith.science/api/pith-number/WUPPP64FTKJGX744NF5NEMXVHR/events.json","paper":"https://pith.science/paper/WUPPP64F"},"agent_actions":{"view_html":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR","download_json":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR.json","view_paper":"https://pith.science/paper/WUPPP64F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.08734&json=true","fetch_graph":"https://pith.science/api/pith-number/WUPPP64FTKJGX744NF5NEMXVHR/graph.json","fetch_events":"https://pith.science/api/pith-number/WUPPP64FTKJGX744NF5NEMXVHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR/action/storage_attestation","attest_author":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR/action/author_attestation","sign_citation":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR/action/citation_signature","submit_replication":"https://pith.science/pith/WUPPP64FTKJGX744NF5NEMXVHR/action/replication_record"}},"created_at":"2026-07-05T04:21:12.608246+00:00","updated_at":"2026-07-05T04:21:12.608246+00:00"}