{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VY5ATNVME7IU7SHFV52NYJLK3E","short_pith_number":"pith:VY5ATNVM","schema_version":"1.0","canonical_sha256":"ae3a09b6ac27d14fc8e5af74dc256ad9341169593cf7def1a1b99258092a9938","source":{"kind":"arxiv","id":"2505.17736","version":1},"attestation_state":"computed","paper":{"title":"Modeling Ranking Properties with In-Context Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Andrew Parry, Debasis Ganguly, Nilanjan Sinhababu, Pabitra Mitra","submitted_at":"2025-05-23T10:58:22Z","abstract_excerpt":"While standard IR models are mainly designed to optimize relevance, real-world search often needs to balance additional objectives such as diversity and fairness. These objectives depend on inter-document interactions and are commonly addressed using post-hoc heuristics or supervised learning methods, which require task-specific training for each ranking scenario and dataset. In this work, we propose an in-context learning (ICL) approach that eliminates the need for such training. Instead, our method relies on a small number of example rankings that demonstrate the desired trade-offs between o"},"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":"2505.17736","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-05-23T10:58:22Z","cross_cats_sorted":[],"title_canon_sha256":"074b6188a9014f75487377466dc144ce023b0814909c8ee0b5ce4923ae03a424","abstract_canon_sha256":"e2c66bbcf878aa12ad2e9fff33876e5a61e3ef303e177bebda66c4c48507e001"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:31.028137Z","signature_b64":"2w+MGI5IgDP9XgDhklu6mSbv/VvoMeC94uPWwBkl/3uaqRszkoqX72M6+scwJJWQaxOura4Bvpk+H479IwdoDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae3a09b6ac27d14fc8e5af74dc256ad9341169593cf7def1a1b99258092a9938","last_reissued_at":"2026-07-05T11:08:31.027702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:31.027702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Ranking Properties with In-Context Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Andrew Parry, Debasis Ganguly, Nilanjan Sinhababu, Pabitra Mitra","submitted_at":"2025-05-23T10:58:22Z","abstract_excerpt":"While standard IR models are mainly designed to optimize relevance, real-world search often needs to balance additional objectives such as diversity and fairness. These objectives depend on inter-document interactions and are commonly addressed using post-hoc heuristics or supervised learning methods, which require task-specific training for each ranking scenario and dataset. In this work, we propose an in-context learning (ICL) approach that eliminates the need for such training. Instead, our method relies on a small number of example rankings that demonstrate the desired trade-offs between o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17736","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/2505.17736/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":"2505.17736","created_at":"2026-07-05T11:08:31.027756+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17736v1","created_at":"2026-07-05T11:08:31.027756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17736","created_at":"2026-07-05T11:08:31.027756+00:00"},{"alias_kind":"pith_short_12","alias_value":"VY5ATNVME7IU","created_at":"2026-07-05T11:08:31.027756+00:00"},{"alias_kind":"pith_short_16","alias_value":"VY5ATNVME7IU7SHF","created_at":"2026-07-05T11:08:31.027756+00:00"},{"alias_kind":"pith_short_8","alias_value":"VY5ATNVM","created_at":"2026-07-05T11:08:31.027756+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.09492","citing_title":"Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E","json":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E.json","graph_json":"https://pith.science/api/pith-number/VY5ATNVME7IU7SHFV52NYJLK3E/graph.json","events_json":"https://pith.science/api/pith-number/VY5ATNVME7IU7SHFV52NYJLK3E/events.json","paper":"https://pith.science/paper/VY5ATNVM"},"agent_actions":{"view_html":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E","download_json":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E.json","view_paper":"https://pith.science/paper/VY5ATNVM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17736&json=true","fetch_graph":"https://pith.science/api/pith-number/VY5ATNVME7IU7SHFV52NYJLK3E/graph.json","fetch_events":"https://pith.science/api/pith-number/VY5ATNVME7IU7SHFV52NYJLK3E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E/action/storage_attestation","attest_author":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E/action/author_attestation","sign_citation":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E/action/citation_signature","submit_replication":"https://pith.science/pith/VY5ATNVME7IU7SHFV52NYJLK3E/action/replication_record"}},"created_at":"2026-07-05T11:08:31.027756+00:00","updated_at":"2026-07-05T11:08:31.027756+00:00"}