{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:REKDW66D2O6G7XT6AZOIOM2ZAE","short_pith_number":"pith:REKDW66D","canonical_record":{"source":{"id":"2410.15397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T14:10:22Z","cross_cats_sorted":["cs.CL","cs.CV"],"title_canon_sha256":"0c488e4b3066985dd82c6f9c455cdde07411bebfa8d6e5841b9ccdeacd1759ac","abstract_canon_sha256":"ea22ed9c4f7fc17e8be671f3cadd798399aaa512b121f9ec18b78616d8153a9c"},"schema_version":"1.0"},"canonical_sha256":"89143b7bc3d3bc6fde7e065c87335901384b236be18922b9d794b19d452420d1","source":{"kind":"arxiv","id":"2410.15397","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15397","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15397v1","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15397","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"pith_short_12","alias_value":"REKDW66D2O6G","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"pith_short_16","alias_value":"REKDW66D2O6G7XT6","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"pith_short_8","alias_value":"REKDW66D","created_at":"2026-07-05T09:23:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:REKDW66D2O6G7XT6AZOIOM2ZAE","target":"record","payload":{"canonical_record":{"source":{"id":"2410.15397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T14:10:22Z","cross_cats_sorted":["cs.CL","cs.CV"],"title_canon_sha256":"0c488e4b3066985dd82c6f9c455cdde07411bebfa8d6e5841b9ccdeacd1759ac","abstract_canon_sha256":"ea22ed9c4f7fc17e8be671f3cadd798399aaa512b121f9ec18b78616d8153a9c"},"schema_version":"1.0"},"canonical_sha256":"89143b7bc3d3bc6fde7e065c87335901384b236be18922b9d794b19d452420d1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:18.924670Z","signature_b64":"6mWxcUEU/lCLqBxGkYqo9RT6bCa3wDjHRb91HrDDny54ULyzSvoMM26OYpkYBXdVqIyKqbe3I0+iG7ScfGV5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89143b7bc3d3bc6fde7e065c87335901384b236be18922b9d794b19d452420d1","last_reissued_at":"2026-07-05T09:23:18.924252Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:18.924252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.15397","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:23:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jvyawi/BpZS/Tyxs8v9Q3B6+0tR5vuOrtZH+9RlJaCMxrExpfO9ztevUVjE6ijze1xNUVD3QJE9vuHYm8ADlBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:43:33.321564Z"},"content_sha256":"ca1f6cc3c58c79f9f2d815d846924a48e78fe94255ef1d9f7a8511ff410d4b68","schema_version":"1.0","event_id":"sha256:ca1f6cc3c58c79f9f2d815d846924a48e78fe94255ef1d9f7a8511ff410d4b68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:REKDW66D2O6G7XT6AZOIOM2ZAE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"IPO: Interpretable Prompt Optimization for Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Cees G. M. Snoek, Wenfang Sun, Yingjun Du","submitted_at":"2024-10-20T14:10:22Z","abstract_excerpt":"Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input text prompts, which requires skillful prompt template engineering. Instead, current approaches to prompt optimization learn the prompts through gradient descent, where the prompts are treated as adjustable parameters. However, these methods tend to lead to overfitting of the base classes seen during training and produce prompts that are no longer understandable by humans. This paper introduces a simple but interpretable"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15397","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/2410.15397/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:23:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x8g7a+zzFHSr0oeFFzATlMrQ6X6GdZfs5YxctL5I8aTKA6fML8k6/d3lIMODMaXcUEY/6lVRZSpFiTjYTPLKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:43:33.322096Z"},"content_sha256":"ee01df2a6fe06d6bad7052884ceb5b3cb03fa8c977fa02412082e4a78ef0d33c","schema_version":"1.0","event_id":"sha256:ee01df2a6fe06d6bad7052884ceb5b3cb03fa8c977fa02412082e4a78ef0d33c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/REKDW66D2O6G7XT6AZOIOM2ZAE/bundle.json","state_url":"https://pith.science/pith/REKDW66D2O6G7XT6AZOIOM2ZAE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/REKDW66D2O6G7XT6AZOIOM2ZAE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T19:43:33Z","links":{"resolver":"https://pith.science/pith/REKDW66D2O6G7XT6AZOIOM2ZAE","bundle":"https://pith.science/pith/REKDW66D2O6G7XT6AZOIOM2ZAE/bundle.json","state":"https://pith.science/pith/REKDW66D2O6G7XT6AZOIOM2ZAE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/REKDW66D2O6G7XT6AZOIOM2ZAE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:REKDW66D2O6G7XT6AZOIOM2ZAE","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":"ea22ed9c4f7fc17e8be671f3cadd798399aaa512b121f9ec18b78616d8153a9c","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T14:10:22Z","title_canon_sha256":"0c488e4b3066985dd82c6f9c455cdde07411bebfa8d6e5841b9ccdeacd1759ac"},"schema_version":"1.0","source":{"id":"2410.15397","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15397","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15397v1","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15397","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"pith_short_12","alias_value":"REKDW66D2O6G","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"pith_short_16","alias_value":"REKDW66D2O6G7XT6","created_at":"2026-07-05T09:23:18Z"},{"alias_kind":"pith_short_8","alias_value":"REKDW66D","created_at":"2026-07-05T09:23:18Z"}],"graph_snapshots":[{"event_id":"sha256:ee01df2a6fe06d6bad7052884ceb5b3cb03fa8c977fa02412082e4a78ef0d33c","target":"graph","created_at":"2026-07-05T09:23:18Z","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/2410.15397/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input text prompts, which requires skillful prompt template engineering. Instead, current approaches to prompt optimization learn the prompts through gradient descent, where the prompts are treated as adjustable parameters. However, these methods tend to lead to overfitting of the base classes seen during training and produce prompts that are no longer understandable by humans. This paper introduces a simple but interpretable","authors_text":"Cees G. M. Snoek, Wenfang Sun, Yingjun Du","cross_cats":["cs.CL","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T14:10:22Z","title":"IPO: Interpretable Prompt Optimization for Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15397","kind":"arxiv","version":1},"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:ca1f6cc3c58c79f9f2d815d846924a48e78fe94255ef1d9f7a8511ff410d4b68","target":"record","created_at":"2026-07-05T09:23:18Z","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":"ea22ed9c4f7fc17e8be671f3cadd798399aaa512b121f9ec18b78616d8153a9c","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-20T14:10:22Z","title_canon_sha256":"0c488e4b3066985dd82c6f9c455cdde07411bebfa8d6e5841b9ccdeacd1759ac"},"schema_version":"1.0","source":{"id":"2410.15397","kind":"arxiv","version":1}},"canonical_sha256":"89143b7bc3d3bc6fde7e065c87335901384b236be18922b9d794b19d452420d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89143b7bc3d3bc6fde7e065c87335901384b236be18922b9d794b19d452420d1","first_computed_at":"2026-07-05T09:23:18.924252Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:23:18.924252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6mWxcUEU/lCLqBxGkYqo9RT6bCa3wDjHRb91HrDDny54ULyzSvoMM26OYpkYBXdVqIyKqbe3I0+iG7ScfGV5Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:23:18.924670Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.15397","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ca1f6cc3c58c79f9f2d815d846924a48e78fe94255ef1d9f7a8511ff410d4b68","sha256:ee01df2a6fe06d6bad7052884ceb5b3cb03fa8c977fa02412082e4a78ef0d33c"],"state_sha256":"5447d3236142b70df06ed86f14b908ec646978d0c94d599dcf4d9bbe4b5602e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nlx+Mia576NDMLLfJ/apXyjJFSo61pMscFx5+UegssHT+CY/959Z7v4yOtZe3oCMNujA8aa3MUhFIkDkM7SzCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:43:33.327013Z","bundle_sha256":"2c80e510302ec9fa77bb998fbbefcaaae9b5b9719ccf1594d20000d8ce28cfb8"}}