{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NYIZUEZQ3YENUG46UYBMJC3LQL","short_pith_number":"pith:NYIZUEZQ","canonical_record":{"source":{"id":"2203.05765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-03-11T05:47:45Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"320c5ff3cd9b4884ec981b2457f4fbc98aaedce57b27eff3e4b025b3ddef9691","abstract_canon_sha256":"ebbf47eb7d161f6005e22a2f4845d45e2ccc730d933a2b663e1e82fff2c16011"},"schema_version":"1.0"},"canonical_sha256":"6e119a1330de08da1b9ea602c48b6b82d68407304638257e9efe61a454388434","source":{"kind":"arxiv","id":"2203.05765","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.05765","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"arxiv_version","alias_value":"2203.05765v1","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.05765","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"pith_short_12","alias_value":"NYIZUEZQ3YEN","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"pith_short_16","alias_value":"NYIZUEZQ3YENUG46","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"pith_short_8","alias_value":"NYIZUEZQ","created_at":"2026-07-05T04:04:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NYIZUEZQ3YENUG46UYBMJC3LQL","target":"record","payload":{"canonical_record":{"source":{"id":"2203.05765","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-03-11T05:47:45Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"320c5ff3cd9b4884ec981b2457f4fbc98aaedce57b27eff3e4b025b3ddef9691","abstract_canon_sha256":"ebbf47eb7d161f6005e22a2f4845d45e2ccc730d933a2b663e1e82fff2c16011"},"schema_version":"1.0"},"canonical_sha256":"6e119a1330de08da1b9ea602c48b6b82d68407304638257e9efe61a454388434","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:04:09.075943Z","signature_b64":"k34FRtw/Lbr3HQs5ClL0s4+mCEcirGLBrWn9i+UT5NOVuvwxY/inTtJWfZmahnOLN1tL7KGPxBv+1kiGSsH7CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e119a1330de08da1b9ea602c48b6b82d68407304638257e9efe61a454388434","last_reissued_at":"2026-07-05T04:04:09.075388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:04:09.075388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.05765","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":"2026-07-05T04:04:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bpznK1qUyut0Qr4f2n6xbJjfcsyjK6HsduSeRUgJb2DAJV5yp+RzbaqCuFypbmLzFZF9S9xkwx5GAPyqVSOTDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:30:41.394855Z"},"content_sha256":"66eb6ddfd67beb3dae9c6c0d89ed1510c5412650cf52560a2898350217ca519a","schema_version":"1.0","event_id":"sha256:66eb6ddfd67beb3dae9c6c0d89ed1510c5412650cf52560a2898350217ca519a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NYIZUEZQ3YENUG46UYBMJC3LQL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Jamie Callan, Jimmy Lin, Luyu Gao, Xueguang Ma","submitted_at":"2022-03-11T05:47:45Z","abstract_excerpt":"Recent rapid advancements in deep pre-trained language models and the introductions of large datasets have powered research in embedding-based dense retrieval. While several good research papers have emerged, many of them come with their own software stacks. These stacks are typically optimized for some particular research goals instead of efficiency or code structure. In this paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency, flexibility, and code simplicity. Tevatron provides a standardized pipeline for dense retrieval including text processing, model training, c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.05765","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/2203.05765/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":"2026-07-05T04:04:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"grNsq+8lrEyqUf57UHgJP7K8zxkDPcFBo5r1JD5Znav4ptspwE2OSTOC7viC+RLBhbLMb26OU+NzpT7I8oQ6Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:30:41.395428Z"},"content_sha256":"4b63e06a3d6283ab3978d8f56063055d789de7ab999200e3e5b8f4fc4ec6ee37","schema_version":"1.0","event_id":"sha256:4b63e06a3d6283ab3978d8f56063055d789de7ab999200e3e5b8f4fc4ec6ee37"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NYIZUEZQ3YENUG46UYBMJC3LQL/bundle.json","state_url":"https://pith.science/pith/NYIZUEZQ3YENUG46UYBMJC3LQL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NYIZUEZQ3YENUG46UYBMJC3LQL/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":"2026-08-09T05:30:41Z","links":{"resolver":"https://pith.science/pith/NYIZUEZQ3YENUG46UYBMJC3LQL","bundle":"https://pith.science/pith/NYIZUEZQ3YENUG46UYBMJC3LQL/bundle.json","state":"https://pith.science/pith/NYIZUEZQ3YENUG46UYBMJC3LQL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NYIZUEZQ3YENUG46UYBMJC3LQL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NYIZUEZQ3YENUG46UYBMJC3LQL","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":"ebbf47eb7d161f6005e22a2f4845d45e2ccc730d933a2b663e1e82fff2c16011","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-03-11T05:47:45Z","title_canon_sha256":"320c5ff3cd9b4884ec981b2457f4fbc98aaedce57b27eff3e4b025b3ddef9691"},"schema_version":"1.0","source":{"id":"2203.05765","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.05765","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"arxiv_version","alias_value":"2203.05765v1","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.05765","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"pith_short_12","alias_value":"NYIZUEZQ3YEN","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"pith_short_16","alias_value":"NYIZUEZQ3YENUG46","created_at":"2026-07-05T04:04:09Z"},{"alias_kind":"pith_short_8","alias_value":"NYIZUEZQ","created_at":"2026-07-05T04:04:09Z"}],"graph_snapshots":[{"event_id":"sha256:4b63e06a3d6283ab3978d8f56063055d789de7ab999200e3e5b8f4fc4ec6ee37","target":"graph","created_at":"2026-07-05T04:04:09Z","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/2203.05765/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent rapid advancements in deep pre-trained language models and the introductions of large datasets have powered research in embedding-based dense retrieval. While several good research papers have emerged, many of them come with their own software stacks. These stacks are typically optimized for some particular research goals instead of efficiency or code structure. In this paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency, flexibility, and code simplicity. Tevatron provides a standardized pipeline for dense retrieval including text processing, model training, c","authors_text":"Jamie Callan, Jimmy Lin, Luyu Gao, Xueguang Ma","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-03-11T05:47:45Z","title":"Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.05765","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":"sha256:66eb6ddfd67beb3dae9c6c0d89ed1510c5412650cf52560a2898350217ca519a","target":"record","created_at":"2026-07-05T04:04:09Z","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":"ebbf47eb7d161f6005e22a2f4845d45e2ccc730d933a2b663e1e82fff2c16011","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2022-03-11T05:47:45Z","title_canon_sha256":"320c5ff3cd9b4884ec981b2457f4fbc98aaedce57b27eff3e4b025b3ddef9691"},"schema_version":"1.0","source":{"id":"2203.05765","kind":"arxiv","version":1}},"canonical_sha256":"6e119a1330de08da1b9ea602c48b6b82d68407304638257e9efe61a454388434","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e119a1330de08da1b9ea602c48b6b82d68407304638257e9efe61a454388434","first_computed_at":"2026-07-05T04:04:09.075388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:04:09.075388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k34FRtw/Lbr3HQs5ClL0s4+mCEcirGLBrWn9i+UT5NOVuvwxY/inTtJWfZmahnOLN1tL7KGPxBv+1kiGSsH7CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:04:09.075943Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.05765","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:66eb6ddfd67beb3dae9c6c0d89ed1510c5412650cf52560a2898350217ca519a","sha256:4b63e06a3d6283ab3978d8f56063055d789de7ab999200e3e5b8f4fc4ec6ee37"],"state_sha256":"4c4eed84b05db3f8a0bd8696b1d4d8b9c294918797536eced90613c581fda506"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xzExsweh59hu+km+bPbB5atPEIN4xtWZ07sBRzCF7dEC0V/ysBxjkiTCjOFAY99rIiVO7yOcKFiFtpsCG2P0BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:30:41.399491Z","bundle_sha256":"d0cc6855ac788edc9ec55641d68c6d7db0e4cc09c9f2f5664dfa0d85ba71bd55"}}