{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:UEDEDY5U4ABN74JCMMAUF646JN","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":"9c5fa84d8b0dbc2edd6b4da71e3d6c52a2cc401678a106be9d3ca9de6569632f","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-05-21T11:38:33Z","title_canon_sha256":"812de0ed12c8dc76f9d57d506516331122dcd2bb430225c38ba9d356756a50c3"},"schema_version":"1.0","source":{"id":"2205.10569","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.10569","created_at":"2026-07-05T04:25:20Z"},{"alias_kind":"arxiv_version","alias_value":"2205.10569v1","created_at":"2026-07-05T04:25:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.10569","created_at":"2026-07-05T04:25:20Z"},{"alias_kind":"pith_short_12","alias_value":"UEDEDY5U4ABN","created_at":"2026-07-05T04:25:20Z"},{"alias_kind":"pith_short_16","alias_value":"UEDEDY5U4ABN74JC","created_at":"2026-07-05T04:25:20Z"},{"alias_kind":"pith_short_8","alias_value":"UEDEDY5U","created_at":"2026-07-05T04:25:20Z"}],"graph_snapshots":[{"event_id":"sha256:dc99ad76fe57a911f17e809f3f83b724043dfdd3008b92115eb69799baf66e2e","target":"graph","created_at":"2026-07-05T04:25:20Z","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/2205.10569/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep pre-trained language models (e,g. BERT) are effective at large-scale text retrieval task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size. Under such a multi-stage architecture, previous studies mainly focused on optimizing single stage of the framework thus improving the overall retrieval performance. However, how to directly couple multi-stage features for optimization has not been well studied. In this paper, we design Hybrid","authors_text":"Dingkun Long, Guangwei Xu, Pengjun Xie, Yanzhao Zhang","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-05-21T11:38:33Z","title":"HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.10569","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":"sha256:97310eb8ba82a337b67235ed226f5f02ecda00c2d75212db47f1acd0fd5d3598","target":"record","created_at":"2026-07-05T04:25:20Z","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":"9c5fa84d8b0dbc2edd6b4da71e3d6c52a2cc401678a106be9d3ca9de6569632f","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-05-21T11:38:33Z","title_canon_sha256":"812de0ed12c8dc76f9d57d506516331122dcd2bb430225c38ba9d356756a50c3"},"schema_version":"1.0","source":{"id":"2205.10569","kind":"arxiv","version":1}},"canonical_sha256":"a10641e3b4e002dff122630142fb9e4b5f922295fb03f89df1eec72c8be37124","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a10641e3b4e002dff122630142fb9e4b5f922295fb03f89df1eec72c8be37124","first_computed_at":"2026-07-05T04:25:20.973995Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:25:20.973995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tQTfzgtG2UgO0iEMPfo3INJ7Y/Z08RTAE4YW/FzqV3kmILGR7OeaSN6W5XeTq0LhhtX/DAmlfb+yXURPSdGDDg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:25:20.974411Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.10569","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97310eb8ba82a337b67235ed226f5f02ecda00c2d75212db47f1acd0fd5d3598","sha256:dc99ad76fe57a911f17e809f3f83b724043dfdd3008b92115eb69799baf66e2e"],"state_sha256":"76557cd5becf601a19e058192924bcf94a04a63b3a4f86dad955d9bcbe500d5a"}