{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MLRRJJAHWTZCWEJ5CUW3ZC25GL","short_pith_number":"pith:MLRRJJAH","schema_version":"1.0","canonical_sha256":"62e314a407b4f22b113d152dbc8b5d32f4a186d5c0c8f99e538558b1e99531b2","source":{"kind":"arxiv","id":"2402.11347","version":2},"attestation_state":"computed","paper":{"title":"SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bradley Malin, Damien Lopez, Hao Sun, Jiaxin Zhang, Kamalika Das, Sricharan Kumar, Wendi Cui, Zhuohang Li","submitted_at":"2024-02-17T17:47:10Z","abstract_excerpt":"Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive prompts that are defined and represented by suboptimal task performance. To overcome these challenges, we propose a novel Cohesive In-Context Prompt Optimization framework that refines both prompt instructions and examples. However, formulating such an optimization in the discrete and high-dimensional s"},"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":"2402.11347","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-17T17:47:10Z","cross_cats_sorted":[],"title_canon_sha256":"991702564e3c50f97dd5025486f22eab9610c697bcc5f68d70b6072cc27cdcb9","abstract_canon_sha256":"8cc7f6d517558d8a0fba193b58139f087bd7e368be4b1c62064535843e1025eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:48.805139Z","signature_b64":"KKeIPvLfDiyLqKvLBEg0ep50Rqf2IjFUIONCaE5Cev2zjc8tpqN1ecLSE1UTiYAd5dzqUFsqUyM0XxNP2rpoBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62e314a407b4f22b113d152dbc8b5d32f4a186d5c0c8f99e538558b1e99531b2","last_reissued_at":"2026-07-05T11:35:48.804658Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:48.804658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bradley Malin, Damien Lopez, Hao Sun, Jiaxin Zhang, Kamalika Das, Sricharan Kumar, Wendi Cui, Zhuohang Li","submitted_at":"2024-02-17T17:47:10Z","abstract_excerpt":"Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive prompts that are defined and represented by suboptimal task performance. To overcome these challenges, we propose a novel Cohesive In-Context Prompt Optimization framework that refines both prompt instructions and examples. However, formulating such an optimization in the discrete and high-dimensional s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11347","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/2402.11347/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":"2402.11347","created_at":"2026-07-05T11:35:48.804716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11347v2","created_at":"2026-07-05T11:35:48.804716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11347","created_at":"2026-07-05T11:35:48.804716+00:00"},{"alias_kind":"pith_short_12","alias_value":"MLRRJJAHWTZC","created_at":"2026-07-05T11:35:48.804716+00:00"},{"alias_kind":"pith_short_16","alias_value":"MLRRJJAHWTZCWEJ5","created_at":"2026-07-05T11:35:48.804716+00:00"},{"alias_kind":"pith_short_8","alias_value":"MLRRJJAH","created_at":"2026-07-05T11:35:48.804716+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02411","citing_title":"FitText: Evolving Agent Tool Ecologies via Memetic Retrieval","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23640","citing_title":"Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02411","citing_title":"FitText: Evolving Agent Tool Ecologies via Memetic Retrieval","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL","json":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL.json","graph_json":"https://pith.science/api/pith-number/MLRRJJAHWTZCWEJ5CUW3ZC25GL/graph.json","events_json":"https://pith.science/api/pith-number/MLRRJJAHWTZCWEJ5CUW3ZC25GL/events.json","paper":"https://pith.science/paper/MLRRJJAH"},"agent_actions":{"view_html":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL","download_json":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL.json","view_paper":"https://pith.science/paper/MLRRJJAH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11347&json=true","fetch_graph":"https://pith.science/api/pith-number/MLRRJJAHWTZCWEJ5CUW3ZC25GL/graph.json","fetch_events":"https://pith.science/api/pith-number/MLRRJJAHWTZCWEJ5CUW3ZC25GL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL/action/storage_attestation","attest_author":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL/action/author_attestation","sign_citation":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL/action/citation_signature","submit_replication":"https://pith.science/pith/MLRRJJAHWTZCWEJ5CUW3ZC25GL/action/replication_record"}},"created_at":"2026-07-05T11:35:48.804716+00:00","updated_at":"2026-07-05T11:35:48.804716+00:00"}