{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:HEAILIFNTWMUYRL3DYBHIIC7U3","short_pith_number":"pith:HEAILIFN","canonical_record":{"source":{"id":"2010.07075","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-14T13:24:43Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"4ced36b755b62aeadceff44dad335b032aec595a26fc518740ad7bf9d8324dd8","abstract_canon_sha256":"06790ec512e1d1c2092bcb7e3e5946c824d3e194dcd364e6095c42bed38fe520"},"schema_version":"1.0"},"canonical_sha256":"390085a0ad9d994c457b1e0274205fa6f28786925d2c67a6f64d545bf6a12ed6","source":{"kind":"arxiv","id":"2010.07075","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.07075","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"arxiv_version","alias_value":"2010.07075v1","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.07075","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"pith_short_12","alias_value":"HEAILIFNTWMU","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"pith_short_16","alias_value":"HEAILIFNTWMUYRL3","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"pith_short_8","alias_value":"HEAILIFN","created_at":"2026-07-05T01:43:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:HEAILIFNTWMUYRL3DYBHIIC7U3","target":"record","payload":{"canonical_record":{"source":{"id":"2010.07075","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-14T13:24:43Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"4ced36b755b62aeadceff44dad335b032aec595a26fc518740ad7bf9d8324dd8","abstract_canon_sha256":"06790ec512e1d1c2092bcb7e3e5946c824d3e194dcd364e6095c42bed38fe520"},"schema_version":"1.0"},"canonical_sha256":"390085a0ad9d994c457b1e0274205fa6f28786925d2c67a6f64d545bf6a12ed6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:43:02.542696Z","signature_b64":"US2MTNne9F152b02ZRsiArq6ss9uLF4CtkCm4vbw3bzag1cVr7+C+x0x0PSd8/NCr1G9xpfJnRTcB8gYV5HPDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"390085a0ad9d994c457b1e0274205fa6f28786925d2c67a6f64d545bf6a12ed6","last_reissued_at":"2026-07-05T01:43:02.542097Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:43:02.542097Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.07075","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:43:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SndFfhxjtiLiT22lQwJS/X/CAgAIgYWDGwZqJm8gILQ8D3GIyQL0yZ1URUVsfjSgWGc77AqA/x4eFgyWmaPSAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T13:30:33.176297Z"},"content_sha256":"cee35d31f7c86fa282f0d82fef65fb40c5059e06a8f584dbab456b5668039f74","schema_version":"1.0","event_id":"sha256:cee35d31f7c86fa282f0d82fef65fb40c5059e06a8f584dbab456b5668039f74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:HEAILIFNTWMUYRL3DYBHIIC7U3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AutoADR: Automatic Model Design for Ad Relevance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Hong Sun, Jing Bai, Rong Zhou, Ruofei Zhang, Wei Shen, Yaming Yang, Yiren Chen, Yujing Wang, Yunhai Tong, Yu Xu","submitted_at":"2020-10-14T13:24:43Z","abstract_excerpt":"Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, these pre-trained models are memory and computation intensive, hindering their deployment into industrial online systems like Ad Relevance. Meanwhile, how to design an effective yet efficient model architecture is another challenging problem in online Ad Relevance. Recently, AutoML shed new lights on architecture design, but how to integrate it with pre-trained language models remains unsettled. In this paper, we propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.07075","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/2010.07075/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":"2026-07-05T01:43:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9T4xgausWh+RSQtiW02uE6JWxtklaMNuk5DidhvvnQI8SLIepVgLFFfRfmUS0rshv1YYaLEFaeXTGKonBuIGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T13:30:33.176799Z"},"content_sha256":"70f41b35a7c6c420ca616b5b01d3e0b2154ecbcbf311bae1a528e02329cfd675","schema_version":"1.0","event_id":"sha256:70f41b35a7c6c420ca616b5b01d3e0b2154ecbcbf311bae1a528e02329cfd675"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HEAILIFNTWMUYRL3DYBHIIC7U3/bundle.json","state_url":"https://pith.science/pith/HEAILIFNTWMUYRL3DYBHIIC7U3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HEAILIFNTWMUYRL3DYBHIIC7U3/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":"2026-08-13T13:30:33Z","links":{"resolver":"https://pith.science/pith/HEAILIFNTWMUYRL3DYBHIIC7U3","bundle":"https://pith.science/pith/HEAILIFNTWMUYRL3DYBHIIC7U3/bundle.json","state":"https://pith.science/pith/HEAILIFNTWMUYRL3DYBHIIC7U3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HEAILIFNTWMUYRL3DYBHIIC7U3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:HEAILIFNTWMUYRL3DYBHIIC7U3","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":"06790ec512e1d1c2092bcb7e3e5946c824d3e194dcd364e6095c42bed38fe520","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-14T13:24:43Z","title_canon_sha256":"4ced36b755b62aeadceff44dad335b032aec595a26fc518740ad7bf9d8324dd8"},"schema_version":"1.0","source":{"id":"2010.07075","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.07075","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"arxiv_version","alias_value":"2010.07075v1","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.07075","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"pith_short_12","alias_value":"HEAILIFNTWMU","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"pith_short_16","alias_value":"HEAILIFNTWMUYRL3","created_at":"2026-07-05T01:43:02Z"},{"alias_kind":"pith_short_8","alias_value":"HEAILIFN","created_at":"2026-07-05T01:43:02Z"}],"graph_snapshots":[{"event_id":"sha256:70f41b35a7c6c420ca616b5b01d3e0b2154ecbcbf311bae1a528e02329cfd675","target":"graph","created_at":"2026-07-05T01:43:02Z","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/2010.07075/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, these pre-trained models are memory and computation intensive, hindering their deployment into industrial online systems like Ad Relevance. Meanwhile, how to design an effective yet efficient model architecture is another challenging problem in online Ad Relevance. Recently, AutoML shed new lights on architecture design, but how to integrate it with pre-trained language models remains unsettled. In this paper, we propo","authors_text":"Hong Sun, Jing Bai, Rong Zhou, Ruofei Zhang, Wei Shen, Yaming Yang, Yiren Chen, Yujing Wang, Yunhai Tong, Yu Xu","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-14T13:24:43Z","title":"AutoADR: Automatic Model Design for Ad Relevance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.07075","kind":"arxiv","version":1},"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:cee35d31f7c86fa282f0d82fef65fb40c5059e06a8f584dbab456b5668039f74","target":"record","created_at":"2026-07-05T01:43:02Z","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":"06790ec512e1d1c2092bcb7e3e5946c824d3e194dcd364e6095c42bed38fe520","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-14T13:24:43Z","title_canon_sha256":"4ced36b755b62aeadceff44dad335b032aec595a26fc518740ad7bf9d8324dd8"},"schema_version":"1.0","source":{"id":"2010.07075","kind":"arxiv","version":1}},"canonical_sha256":"390085a0ad9d994c457b1e0274205fa6f28786925d2c67a6f64d545bf6a12ed6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"390085a0ad9d994c457b1e0274205fa6f28786925d2c67a6f64d545bf6a12ed6","first_computed_at":"2026-07-05T01:43:02.542097Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:43:02.542097Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"US2MTNne9F152b02ZRsiArq6ss9uLF4CtkCm4vbw3bzag1cVr7+C+x0x0PSd8/NCr1G9xpfJnRTcB8gYV5HPDA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:43:02.542696Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.07075","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cee35d31f7c86fa282f0d82fef65fb40c5059e06a8f584dbab456b5668039f74","sha256:70f41b35a7c6c420ca616b5b01d3e0b2154ecbcbf311bae1a528e02329cfd675"],"state_sha256":"4fad6a644c98df369b540aba5cdf428892aee1359a598a64b36958fa09dae316"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YqkV2kvHCWbcT5h6Fs8eoVHj0YWWNRqBFrcJ0Wb5hv1jjq5h+g7kC7sggOTsaXSFl9pJbwnAkmDeofZOH2SxAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T13:30:33.180562Z","bundle_sha256":"8db53c96916477700ce7d133e60a57fc8561d76755a667fa7ef07e7263041075"}}