{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:JG7VL53R3HLKG7NQP2LPMPWNVR","short_pith_number":"pith:JG7VL53R","canonical_record":{"source":{"id":"2302.03668","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-07T18:40:18Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b8d706e934d620b0755d2bb687190153c273905dd71b6592a4419a3d2e07a684","abstract_canon_sha256":"72d9cee9907576994ec4153088ca9a8f491686146683fa59a6affe480c57bdbc"},"schema_version":"1.0"},"canonical_sha256":"49bf55f771d9d6a37db07e96f63ecdac6d960c37b45a13cc5574127dddc9fb2e","source":{"kind":"arxiv","id":"2302.03668","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.03668","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"arxiv_version","alias_value":"2302.03668v2","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03668","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"pith_short_12","alias_value":"JG7VL53R3HLK","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"pith_short_16","alias_value":"JG7VL53R3HLKG7NQ","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"pith_short_8","alias_value":"JG7VL53R","created_at":"2026-07-05T06:16:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:JG7VL53R3HLKG7NQP2LPMPWNVR","target":"record","payload":{"canonical_record":{"source":{"id":"2302.03668","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-07T18:40:18Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b8d706e934d620b0755d2bb687190153c273905dd71b6592a4419a3d2e07a684","abstract_canon_sha256":"72d9cee9907576994ec4153088ca9a8f491686146683fa59a6affe480c57bdbc"},"schema_version":"1.0"},"canonical_sha256":"49bf55f771d9d6a37db07e96f63ecdac6d960c37b45a13cc5574127dddc9fb2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:16:15.760861Z","signature_b64":"GPvH7Eu89mEJUyGLMuiEoOxlKlonYzmGcTNElmMW/BT3LooR6wWWuQxI9dL+s+qTw3Nr0jUaA8JG+4iyS+OjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49bf55f771d9d6a37db07e96f63ecdac6d960c37b45a13cc5574127dddc9fb2e","last_reissued_at":"2026-07-05T06:16:15.760324Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:16:15.760324Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.03668","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:16:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rWG0xVmKMiL4QwqZd3/axhbItiVeHxfqLwMsUjCklwqjEP/HP3csez9gZeWgLfVnVCynOUHk/wmouFZMtE6gAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:52:56.268814Z"},"content_sha256":"0c1fb1c4d0f802288c18cc9ce59df6d58eb3c311d0fa3c3c124b877b707cd1bf","schema_version":"1.0","event_id":"sha256:0c1fb1c4d0f802288c18cc9ce59df6d58eb3c311d0fa3c3c124b877b707cd1bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:JG7VL53R3HLKG7NQP2LPMPWNVR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"John Kirchenbauer, Jonas Geiping, Micah Goldblum, Neel Jain, Tom Goldstein, Yuxin Wen","submitted_at":"2023-02-07T18:40:18Z","abstract_excerpt":"The strength of modern generative models lies in their ability to be controlled through text-based prompts. Typical \"hard\" prompts are made from interpretable words and tokens, and must be hand-crafted by humans. There are also \"soft\" prompts, which consist of continuous feature vectors. These can be discovered using powerful optimization methods, but they cannot be easily interpreted, re-used across models, or plugged into a text-based interface.\n  We describe an approach to robustly optimize hard text prompts through efficient gradient-based optimization. Our approach automatically generates"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03668","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/2302.03668/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":"2026-07-05T06:16:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pkiljafado0cCAGC+42WShdRKl3xBaTggHSNd0w25m5b1z5k4HIFB5h7lrCWQTHDSJ4ZRhz4juHYK29XEf5BAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T00:52:56.269301Z"},"content_sha256":"004793fb608baacbf1b4f100377de1309d6dec989a7c1b4f4b15a5281123da9b","schema_version":"1.0","event_id":"sha256:004793fb608baacbf1b4f100377de1309d6dec989a7c1b4f4b15a5281123da9b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JG7VL53R3HLKG7NQP2LPMPWNVR/bundle.json","state_url":"https://pith.science/pith/JG7VL53R3HLKG7NQP2LPMPWNVR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JG7VL53R3HLKG7NQP2LPMPWNVR/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":"2026-08-07T00:52:56Z","links":{"resolver":"https://pith.science/pith/JG7VL53R3HLKG7NQP2LPMPWNVR","bundle":"https://pith.science/pith/JG7VL53R3HLKG7NQP2LPMPWNVR/bundle.json","state":"https://pith.science/pith/JG7VL53R3HLKG7NQP2LPMPWNVR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JG7VL53R3HLKG7NQP2LPMPWNVR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:JG7VL53R3HLKG7NQP2LPMPWNVR","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":"72d9cee9907576994ec4153088ca9a8f491686146683fa59a6affe480c57bdbc","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-07T18:40:18Z","title_canon_sha256":"b8d706e934d620b0755d2bb687190153c273905dd71b6592a4419a3d2e07a684"},"schema_version":"1.0","source":{"id":"2302.03668","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.03668","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"arxiv_version","alias_value":"2302.03668v2","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.03668","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"pith_short_12","alias_value":"JG7VL53R3HLK","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"pith_short_16","alias_value":"JG7VL53R3HLKG7NQ","created_at":"2026-07-05T06:16:15Z"},{"alias_kind":"pith_short_8","alias_value":"JG7VL53R","created_at":"2026-07-05T06:16:15Z"}],"graph_snapshots":[{"event_id":"sha256:004793fb608baacbf1b4f100377de1309d6dec989a7c1b4f4b15a5281123da9b","target":"graph","created_at":"2026-07-05T06:16:15Z","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/2302.03668/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The strength of modern generative models lies in their ability to be controlled through text-based prompts. Typical \"hard\" prompts are made from interpretable words and tokens, and must be hand-crafted by humans. There are also \"soft\" prompts, which consist of continuous feature vectors. These can be discovered using powerful optimization methods, but they cannot be easily interpreted, re-used across models, or plugged into a text-based interface.\n  We describe an approach to robustly optimize hard text prompts through efficient gradient-based optimization. Our approach automatically generates","authors_text":"John Kirchenbauer, Jonas Geiping, Micah Goldblum, Neel Jain, Tom Goldstein, Yuxin Wen","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-07T18:40:18Z","title":"Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.03668","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":"sha256:0c1fb1c4d0f802288c18cc9ce59df6d58eb3c311d0fa3c3c124b877b707cd1bf","target":"record","created_at":"2026-07-05T06:16:15Z","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":"72d9cee9907576994ec4153088ca9a8f491686146683fa59a6affe480c57bdbc","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-07T18:40:18Z","title_canon_sha256":"b8d706e934d620b0755d2bb687190153c273905dd71b6592a4419a3d2e07a684"},"schema_version":"1.0","source":{"id":"2302.03668","kind":"arxiv","version":2}},"canonical_sha256":"49bf55f771d9d6a37db07e96f63ecdac6d960c37b45a13cc5574127dddc9fb2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49bf55f771d9d6a37db07e96f63ecdac6d960c37b45a13cc5574127dddc9fb2e","first_computed_at":"2026-07-05T06:16:15.760324Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:16:15.760324Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GPvH7Eu89mEJUyGLMuiEoOxlKlonYzmGcTNElmMW/BT3LooR6wWWuQxI9dL+s+qTw3Nr0jUaA8JG+4iyS+OjBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:16:15.760861Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.03668","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c1fb1c4d0f802288c18cc9ce59df6d58eb3c311d0fa3c3c124b877b707cd1bf","sha256:004793fb608baacbf1b4f100377de1309d6dec989a7c1b4f4b15a5281123da9b"],"state_sha256":"fb3e9d5011f378558765f1b2c1a18518a8a1f91c9d5d61e68f2543e7bccadebc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tDmUb785scSRZFY1HAAxKoM/LvC70DxQmIEhbxp2QUMIJmPCkw5l8jH+8t9Th68xeyfBIJjQGmKYUCDiQH0yDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T00:52:56.272984Z","bundle_sha256":"ceb81d5fdb168b26f2b4cbdaf73c6500467c2277b6ebe21a043e9952f2b34193"}}