{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BNP7OCDGGAVQS3472EBLGUEIEU","short_pith_number":"pith:BNP7OCDG","schema_version":"1.0","canonical_sha256":"0b5ff70866302b096f9fd102b350882508bc3201f08d53f68a6fa9f648c17bf8","source":{"kind":"arxiv","id":"2508.13285","version":1},"attestation_state":"computed","paper":{"title":"Towards Human-AI Complementarity in Matching Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Adrian Arnaiz-Rodriguez, Manuel Gomez-Rodriguez, Nina Corvelo Benz, Nuria Oliver, Suhas Thejaswi","submitted_at":"2025-08-18T18:02:45Z","abstract_excerpt":"Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthcare and social service provision. However, existing systems are not designed to achieve human-AI complementarity: decisions made by a human using an algorithmic matching system are not necessarily better than those made by the human or by the algorithm alone. Our work aims to address this gap. To this end, we propose collaborative matching (comatch), a data-driven algorithmic matching system that takes a collaborative"},"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":"2508.13285","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-18T18:02:45Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"312f1d92fbcb594a361212f5af1e0844825fca3b0d4a95d3df94cdb866a5d579","abstract_canon_sha256":"40d8ef2976451d821f33a3df11ae4408c1b722bf10baf315c25fc298094d9767"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:45.774081Z","signature_b64":"OjpHnfMrsk0w7oHCrx3qPEA+1V7Pv/Pvagku4X4ORO/4mkzFDyE3sZVaGZX9ZsEdWEC6yKFxBSxSopSw3neNCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b5ff70866302b096f9fd102b350882508bc3201f08d53f68a6fa9f648c17bf8","last_reissued_at":"2026-07-05T11:55:45.773626Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:45.773626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Human-AI Complementarity in Matching Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.LG","authors_text":"Adrian Arnaiz-Rodriguez, Manuel Gomez-Rodriguez, Nina Corvelo Benz, Nuria Oliver, Suhas Thejaswi","submitted_at":"2025-08-18T18:02:45Z","abstract_excerpt":"Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthcare and social service provision. However, existing systems are not designed to achieve human-AI complementarity: decisions made by a human using an algorithmic matching system are not necessarily better than those made by the human or by the algorithm alone. Our work aims to address this gap. To this end, we propose collaborative matching (comatch), a data-driven algorithmic matching system that takes a collaborative"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.13285","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/2508.13285/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":"2508.13285","created_at":"2026-07-05T11:55:45.773683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.13285v1","created_at":"2026-07-05T11:55:45.773683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.13285","created_at":"2026-07-05T11:55:45.773683+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNP7OCDGGAVQ","created_at":"2026-07-05T11:55:45.773683+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNP7OCDGGAVQS347","created_at":"2026-07-05T11:55:45.773683+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNP7OCDG","created_at":"2026-07-05T11:55:45.773683+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12587","citing_title":"Strategic Decision Support for AI Agents","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18202","citing_title":"Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU","json":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU.json","graph_json":"https://pith.science/api/pith-number/BNP7OCDGGAVQS3472EBLGUEIEU/graph.json","events_json":"https://pith.science/api/pith-number/BNP7OCDGGAVQS3472EBLGUEIEU/events.json","paper":"https://pith.science/paper/BNP7OCDG"},"agent_actions":{"view_html":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU","download_json":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU.json","view_paper":"https://pith.science/paper/BNP7OCDG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.13285&json=true","fetch_graph":"https://pith.science/api/pith-number/BNP7OCDGGAVQS3472EBLGUEIEU/graph.json","fetch_events":"https://pith.science/api/pith-number/BNP7OCDGGAVQS3472EBLGUEIEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU/action/storage_attestation","attest_author":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU/action/author_attestation","sign_citation":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU/action/citation_signature","submit_replication":"https://pith.science/pith/BNP7OCDGGAVQS3472EBLGUEIEU/action/replication_record"}},"created_at":"2026-07-05T11:55:45.773683+00:00","updated_at":"2026-07-05T11:55:45.773683+00:00"}