{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XCTJC5W4L2ILAUMXJVCZOZPO76","short_pith_number":"pith:XCTJC5W4","schema_version":"1.0","canonical_sha256":"b8a69176dc5e90b051974d459765eeff9ca5555f3bde52764aeed5e127adfb6d","source":{"kind":"arxiv","id":"2406.15708","version":2},"attestation_state":"computed","paper":{"title":"Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Hootan Nakhost, Ruoxi Sun, Sercan O. Arik, Xingchen Wan","submitted_at":"2024-06-22T02:07:10Z","abstract_excerpt":"Large language models have demonstrated remarkable capabilities, but their performance is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) methods are designed to automate this and can be broadly categorized into those targeting instructions (instruction optimization, IO) vs. those targeting exemplars (exemplar optimization, EO). Despite their shared objective, these have evolved rather independently, with IO receiving more research attention recently. This paper seeks to bridge this gap by comprehensively comparing the performance of representative IO and E"},"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":"2406.15708","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-22T02:07:10Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9baf1b65fa81dd0fd84ef10975fc2ad2d1ef5f7f815d73c13747733225ac2db3","abstract_canon_sha256":"00f58456d796ea4c8d588d6f88f17a88e2ad9464402ae7de08b3a500b6b38aa8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:10.199166Z","signature_b64":"7ym/7fGpvuhNJo50onyz4T7eL0yskqEuQd1M94afxNS/1v/8Y5VTBDEQ809JxQtptz42QhS7Qp396fjGoEubCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8a69176dc5e90b051974d459765eeff9ca5555f3bde52764aeed5e127adfb6d","last_reissued_at":"2026-07-05T09:32:10.198678Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:10.198678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Hootan Nakhost, Ruoxi Sun, Sercan O. Arik, Xingchen Wan","submitted_at":"2024-06-22T02:07:10Z","abstract_excerpt":"Large language models have demonstrated remarkable capabilities, but their performance is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) methods are designed to automate this and can be broadly categorized into those targeting instructions (instruction optimization, IO) vs. those targeting exemplars (exemplar optimization, EO). Despite their shared objective, these have evolved rather independently, with IO receiving more research attention recently. This paper seeks to bridge this gap by comprehensively comparing the performance of representative IO and E"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.15708","kind":"arxiv","version":2},"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/2406.15708/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":"2406.15708","created_at":"2026-07-05T09:32:10.198732+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.15708v2","created_at":"2026-07-05T09:32:10.198732+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.15708","created_at":"2026-07-05T09:32:10.198732+00:00"},{"alias_kind":"pith_short_12","alias_value":"XCTJC5W4L2IL","created_at":"2026-07-05T09:32:10.198732+00:00"},{"alias_kind":"pith_short_16","alias_value":"XCTJC5W4L2ILAUMX","created_at":"2026-07-05T09:32:10.198732+00:00"},{"alias_kind":"pith_short_8","alias_value":"XCTJC5W4","created_at":"2026-07-05T09:32:10.198732+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16233","citing_title":"FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14197","citing_title":"The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76","json":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76.json","graph_json":"https://pith.science/api/pith-number/XCTJC5W4L2ILAUMXJVCZOZPO76/graph.json","events_json":"https://pith.science/api/pith-number/XCTJC5W4L2ILAUMXJVCZOZPO76/events.json","paper":"https://pith.science/paper/XCTJC5W4"},"agent_actions":{"view_html":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76","download_json":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76.json","view_paper":"https://pith.science/paper/XCTJC5W4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.15708&json=true","fetch_graph":"https://pith.science/api/pith-number/XCTJC5W4L2ILAUMXJVCZOZPO76/graph.json","fetch_events":"https://pith.science/api/pith-number/XCTJC5W4L2ILAUMXJVCZOZPO76/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76/action/storage_attestation","attest_author":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76/action/author_attestation","sign_citation":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76/action/citation_signature","submit_replication":"https://pith.science/pith/XCTJC5W4L2ILAUMXJVCZOZPO76/action/replication_record"}},"created_at":"2026-07-05T09:32:10.198732+00:00","updated_at":"2026-07-05T09:32:10.198732+00:00"}