{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7LJ2G6HRRQGQ5C653BAOIUX347","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e392c621c3ac9cec297f4686eda3b54e58156cef229d11a6f6b3e971a6df6626","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-25T08:23:05Z","title_canon_sha256":"e7ab9474c3d2fce3a350936b6e5daf1a1092a415fa70c9498f1efeeb15d3cc3c"},"schema_version":"1.0","source":{"id":"2405.16122","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.16122","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"arxiv_version","alias_value":"2405.16122v2","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16122","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_12","alias_value":"7LJ2G6HRRQGQ","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_16","alias_value":"7LJ2G6HRRQGQ5C65","created_at":"2026-07-05T09:27:54Z"},{"alias_kind":"pith_short_8","alias_value":"7LJ2G6HR","created_at":"2026-07-05T09:27:54Z"}],"graph_snapshots":[{"event_id":"sha256:86f1b249333b8d76b84fde33351ab5984038b8fd1a4aa6210e43a9136640b071","target":"graph","created_at":"2026-07-05T09:27:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.16122/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream tasks by including input-label exemplars in the prompt without model fine-tuning. However, the quality of these exemplars in the prompt greatly impacts performance, highlighting the need for an effective automated exemplar selection method. Recent studies have explored retrieval-based approaches to select exemplars tailored to individual test queries, which can be undesirable due to extra test-time computation and an inc","authors_text":"Bryan Kian Hsiang Low, Patrick Jaillet, See-kiong Ng, Wenyang Hu, Xiaoqiang Lin, Yao Shu, Zhaoxuan Wu, Zhongxiang Dai","cross_cats":["cs.CL","cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-25T08:23:05Z","title":"Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16122","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:19e908209c434e279e0bb0b1fd77a273c8c85dea382ba1b93e75184f2e49ff9a","target":"record","created_at":"2026-07-05T09:27:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e392c621c3ac9cec297f4686eda3b54e58156cef229d11a6f6b3e971a6df6626","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-05-25T08:23:05Z","title_canon_sha256":"e7ab9474c3d2fce3a350936b6e5daf1a1092a415fa70c9498f1efeeb15d3cc3c"},"schema_version":"1.0","source":{"id":"2405.16122","kind":"arxiv","version":2}},"canonical_sha256":"fad3a378f18c0d0e8bddd840e452fbe7c596d5592ddc9806cf52e796217bb69c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fad3a378f18c0d0e8bddd840e452fbe7c596d5592ddc9806cf52e796217bb69c","first_computed_at":"2026-07-05T09:27:54.633363Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:27:54.633363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/JAKWohdgwXati1H3UgBi+x1woG0lNw4ORG6vSTBe2ym0R6FtpGcL8+UlJunnrn4otvUmWPXq8b+xAWeF1hUCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:27:54.633926Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.16122","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19e908209c434e279e0bb0b1fd77a273c8c85dea382ba1b93e75184f2e49ff9a","sha256:86f1b249333b8d76b84fde33351ab5984038b8fd1a4aa6210e43a9136640b071"],"state_sha256":"88f598749edbcd76490d71b1d6b86c8c28ade00b017c42d78dc1a1442bd0e431"}