{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RTOPIH3CYNGGP4MSVLOAJQZMWV","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":"3420769b8bef333a234f79cfd3276d75ceb9211fae86db20a44253521f3e9c4e","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-18T05:37:48Z","title_canon_sha256":"64f254341d87ee2ea3c38680a19d68313b7a07fed563913c4a9e9813fb1f7e38"},"schema_version":"1.0","source":{"id":"2408.09365","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.09365","created_at":"2026-07-05T10:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"2408.09365v2","created_at":"2026-07-05T10:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.09365","created_at":"2026-07-05T10:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"RTOPIH3CYNGG","created_at":"2026-07-05T10:18:40Z"},{"alias_kind":"pith_short_16","alias_value":"RTOPIH3CYNGGP4MS","created_at":"2026-07-05T10:18:40Z"},{"alias_kind":"pith_short_8","alias_value":"RTOPIH3C","created_at":"2026-07-05T10:18:40Z"}],"graph_snapshots":[{"event_id":"sha256:02c2d89fc9be08e941eb33b4147dcfe473626296add6c97b4c02adb1324e7075","target":"graph","created_at":"2026-07-05T10:18:40Z","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/2408.09365/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hand-crafting high quality prompts to optimize the performance of language models is a complicated and labor-intensive process. Furthermore, when migrating to newer, smaller, or weaker models (possibly due to latency or cost gains), prompts need to be updated to re-optimize the task performance. We propose Concept Distillation (CD), an automatic prompt optimization technique for enhancing weaker models on complex tasks. CD involves: (1) collecting mistakes made by weak models with a base prompt (initialization), (2) using a strong model to generate reasons for these mistakes and create rules/c","authors_text":"Ashwin Srinivasan, Cassiano O. Becker, Ehi Nosakhare, Emmanuel Aboah Boateng, Kabir Walia, Nabiha Asghar, Soundar Srinivasan, Victor Dibia","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-18T05:37:48Z","title":"Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.09365","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:b47160578bd6232c1d8f7564cea2a51e802b9e4e650b802af76bcc07c6e6feaa","target":"record","created_at":"2026-07-05T10:18:40Z","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":"3420769b8bef333a234f79cfd3276d75ceb9211fae86db20a44253521f3e9c4e","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-08-18T05:37:48Z","title_canon_sha256":"64f254341d87ee2ea3c38680a19d68313b7a07fed563913c4a9e9813fb1f7e38"},"schema_version":"1.0","source":{"id":"2408.09365","kind":"arxiv","version":2}},"canonical_sha256":"8cdcf41f62c34c67f192aadc04c32cb5534f8fa01197acf71351352ac072b730","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8cdcf41f62c34c67f192aadc04c32cb5534f8fa01197acf71351352ac072b730","first_computed_at":"2026-07-05T10:18:40.616582Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:18:40.616582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r9jIMApBEjjCGPgYagTLeHGn64wg0MjX5bxsGyjbcU42xc5bmBEQRrnU9ls2Qg7+AjscQ5kP3OkdIUu67oY7AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:18:40.617110Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.09365","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b47160578bd6232c1d8f7564cea2a51e802b9e4e650b802af76bcc07c6e6feaa","sha256:02c2d89fc9be08e941eb33b4147dcfe473626296add6c97b4c02adb1324e7075"],"state_sha256":"dc471bad7735bcc8372abb4419b713df23d93d0b93a8938d7c80d66434250128"}