{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GWREPMUBYIKRS5GU6EBASFB77T","short_pith_number":"pith:GWREPMUB","schema_version":"1.0","canonical_sha256":"35a247b281c2151974d4f10209143ffcc6fde2ab1e6001985a5a5cf998d91ba0","source":{"kind":"arxiv","id":"2508.03555","version":1},"attestation_state":"computed","paper":{"title":"PyLate: Flexible Training and Retrieval for Late Interaction Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Antoine Chaffin, Rapha\\\"el Sourty","submitted_at":"2025-08-05T15:23:40Z","abstract_excerpt":"Neural ranking has become a cornerstone of modern information retrieval. While single vector search remains the dominant paradigm, it suffers from the shortcoming of compressing all the information into a single vector. This compression leads to notable performance degradation in out-of-domain, long-context, and reasoning-intensive retrieval tasks. Multi-vector approaches pioneered by ColBERT aim to address these limitations by preserving individual token embeddings and computing similarity via the MaxSim operator. This architecture has demonstrated superior empirical advantages, including enh"},"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":"2508.03555","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-08-05T15:23:40Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"34dac7b7bbe57ebaaa427c98b5f261a591ea1f75224fe260fa6490519981e606","abstract_canon_sha256":"12dea93a6187119a5ff5675cb1d05cafb07eb2b4abda978680c79ba615d36134"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:59.052228Z","signature_b64":"+CdfFCadtz+UkVgBodybVukZLpKCI2HrnGTidUN/a4d4U48jSOD/+z6oB+8KkxcCIMTR2m8ztLdKlmgOlAMcDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35a247b281c2151974d4f10209143ffcc6fde2ab1e6001985a5a5cf998d91ba0","last_reissued_at":"2026-07-05T11:48:59.051736Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:59.051736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PyLate: Flexible Training and Retrieval for Late Interaction Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Antoine Chaffin, Rapha\\\"el Sourty","submitted_at":"2025-08-05T15:23:40Z","abstract_excerpt":"Neural ranking has become a cornerstone of modern information retrieval. While single vector search remains the dominant paradigm, it suffers from the shortcoming of compressing all the information into a single vector. This compression leads to notable performance degradation in out-of-domain, long-context, and reasoning-intensive retrieval tasks. Multi-vector approaches pioneered by ColBERT aim to address these limitations by preserving individual token embeddings and computing similarity via the MaxSim operator. This architecture has demonstrated superior empirical advantages, including enh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03555","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/2508.03555/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":"2508.03555","created_at":"2026-07-05T11:48:59.051802+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.03555v1","created_at":"2026-07-05T11:48:59.051802+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03555","created_at":"2026-07-05T11:48:59.051802+00:00"},{"alias_kind":"pith_short_12","alias_value":"GWREPMUBYIKR","created_at":"2026-07-05T11:48:59.051802+00:00"},{"alias_kind":"pith_short_16","alias_value":"GWREPMUBYIKRS5GU","created_at":"2026-07-05T11:48:59.051802+00:00"},{"alias_kind":"pith_short_8","alias_value":"GWREPMUB","created_at":"2026-07-05T11:48:59.051802+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/GWREPMUBYIKRS5GU6EBASFB77T","json":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T.json","graph_json":"https://pith.science/api/pith-number/GWREPMUBYIKRS5GU6EBASFB77T/graph.json","events_json":"https://pith.science/api/pith-number/GWREPMUBYIKRS5GU6EBASFB77T/events.json","paper":"https://pith.science/paper/GWREPMUB"},"agent_actions":{"view_html":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T","download_json":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T.json","view_paper":"https://pith.science/paper/GWREPMUB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.03555&json=true","fetch_graph":"https://pith.science/api/pith-number/GWREPMUBYIKRS5GU6EBASFB77T/graph.json","fetch_events":"https://pith.science/api/pith-number/GWREPMUBYIKRS5GU6EBASFB77T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T/action/storage_attestation","attest_author":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T/action/author_attestation","sign_citation":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T/action/citation_signature","submit_replication":"https://pith.science/pith/GWREPMUBYIKRS5GU6EBASFB77T/action/replication_record"}},"created_at":"2026-07-05T11:48:59.051802+00:00","updated_at":"2026-07-05T11:48:59.051802+00:00"}