{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RUXMYVONTWFPWRH4LSEFZ2XLPT","short_pith_number":"pith:RUXMYVON","canonical_record":{"source":{"id":"2312.04076","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-07T06:43:34Z","cross_cats_sorted":[],"title_canon_sha256":"e732c99de7f39abfc9cb66067d360bda75a466c05119acf67aded21a75311cc3","abstract_canon_sha256":"8d8bbcca47f91836aaf55d9f2c736d5ba56b3b14e82fb8757e3855800c124989"},"schema_version":"1.0"},"canonical_sha256":"8d2ecc55cd9d8afb44fc5c885ceaeb7ccb984db3041ceb819bad5f960fc5bd08","source":{"kind":"arxiv","id":"2312.04076","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.04076","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"arxiv_version","alias_value":"2312.04076v2","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.04076","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"pith_short_12","alias_value":"RUXMYVONTWFP","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"pith_short_16","alias_value":"RUXMYVONTWFPWRH4","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"pith_short_8","alias_value":"RUXMYVON","created_at":"2026-07-05T08:03:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RUXMYVONTWFPWRH4LSEFZ2XLPT","target":"record","payload":{"canonical_record":{"source":{"id":"2312.04076","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-07T06:43:34Z","cross_cats_sorted":[],"title_canon_sha256":"e732c99de7f39abfc9cb66067d360bda75a466c05119acf67aded21a75311cc3","abstract_canon_sha256":"8d8bbcca47f91836aaf55d9f2c736d5ba56b3b14e82fb8757e3855800c124989"},"schema_version":"1.0"},"canonical_sha256":"8d2ecc55cd9d8afb44fc5c885ceaeb7ccb984db3041ceb819bad5f960fc5bd08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:46.621474Z","signature_b64":"FwpqLw1NjmaXsLGS2uOjD7c3Qi7NfoAJQmyKQ7aKrLw1VeeOTK5RhqvPuBYmQSai02Jy9R8NZWS8jPh4FNw8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d2ecc55cd9d8afb44fc5c885ceaeb7ccb984db3041ceb819bad5f960fc5bd08","last_reissued_at":"2026-07-05T08:03:46.620991Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:46.620991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.04076","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":"2026-07-05T08:03:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v7mRAjMxV/8C3CJEhBXY9xSPouZbxVzRBz9DTJLtUv31yqe2ooZvJVh0DmUSag4+ZyOTRPC0HA+5fj8SOG+AAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T06:57:50.298051Z"},"content_sha256":"cd249f516cd20cb468f7a36a6497d0e36b09ee370c8c1e7d5ea4c3625f17084b","schema_version":"1.0","event_id":"sha256:cd249f516cd20cb468f7a36a6497d0e36b09ee370c8c1e7d5ea4c3625f17084b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RUXMYVONTWFPWRH4LSEFZ2XLPT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models are Good Prompt Learners for Low-Shot Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haidong Zhu, Jingmin Wei, Ram Nevatia, Xuefeng Hu, Zhaoheng Zheng","submitted_at":"2023-12-07T06:43:34Z","abstract_excerpt":"Low-shot image classification, where training images are limited or inaccessible, has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability, e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. Large Language Models (LLMs), with their vast encyclopedic knowledge, emerge as the complement. Thus, in this paper, we discuss the integration of LLMs to enhance pre-trained VL models, specifically on low-shot classification. However, the domain gap between"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.04076","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/2312.04076/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":"2026-07-05T08:03:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"885vOAS897KdfcErrLMAgIk3jdUQZziqmAOwa5xGxPcs+RAZUgjlg3X929RB/nHrwSC7Lq8WgTzh6OpP9TY2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T06:57:50.298574Z"},"content_sha256":"3fb5674f1325e56eaca68252d7868db3dbdc5f8c5770bfbbe3d378351b5fd755","schema_version":"1.0","event_id":"sha256:3fb5674f1325e56eaca68252d7868db3dbdc5f8c5770bfbbe3d378351b5fd755"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT/bundle.json","state_url":"https://pith.science/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT/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":"2026-08-08T06:57:50Z","links":{"resolver":"https://pith.science/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT","bundle":"https://pith.science/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT/bundle.json","state":"https://pith.science/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RUXMYVONTWFPWRH4LSEFZ2XLPT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RUXMYVONTWFPWRH4LSEFZ2XLPT","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":"8d8bbcca47f91836aaf55d9f2c736d5ba56b3b14e82fb8757e3855800c124989","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-07T06:43:34Z","title_canon_sha256":"e732c99de7f39abfc9cb66067d360bda75a466c05119acf67aded21a75311cc3"},"schema_version":"1.0","source":{"id":"2312.04076","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.04076","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"arxiv_version","alias_value":"2312.04076v2","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.04076","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"pith_short_12","alias_value":"RUXMYVONTWFP","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"pith_short_16","alias_value":"RUXMYVONTWFPWRH4","created_at":"2026-07-05T08:03:46Z"},{"alias_kind":"pith_short_8","alias_value":"RUXMYVON","created_at":"2026-07-05T08:03:46Z"}],"graph_snapshots":[{"event_id":"sha256:3fb5674f1325e56eaca68252d7868db3dbdc5f8c5770bfbbe3d378351b5fd755","target":"graph","created_at":"2026-07-05T08:03:46Z","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/2312.04076/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-shot image classification, where training images are limited or inaccessible, has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability, e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. Large Language Models (LLMs), with their vast encyclopedic knowledge, emerge as the complement. Thus, in this paper, we discuss the integration of LLMs to enhance pre-trained VL models, specifically on low-shot classification. However, the domain gap between","authors_text":"Haidong Zhu, Jingmin Wei, Ram Nevatia, Xuefeng Hu, Zhaoheng Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-07T06:43:34Z","title":"Large Language Models are Good Prompt Learners for Low-Shot Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.04076","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:cd249f516cd20cb468f7a36a6497d0e36b09ee370c8c1e7d5ea4c3625f17084b","target":"record","created_at":"2026-07-05T08:03:46Z","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":"8d8bbcca47f91836aaf55d9f2c736d5ba56b3b14e82fb8757e3855800c124989","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-07T06:43:34Z","title_canon_sha256":"e732c99de7f39abfc9cb66067d360bda75a466c05119acf67aded21a75311cc3"},"schema_version":"1.0","source":{"id":"2312.04076","kind":"arxiv","version":2}},"canonical_sha256":"8d2ecc55cd9d8afb44fc5c885ceaeb7ccb984db3041ceb819bad5f960fc5bd08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d2ecc55cd9d8afb44fc5c885ceaeb7ccb984db3041ceb819bad5f960fc5bd08","first_computed_at":"2026-07-05T08:03:46.620991Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:46.620991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FwpqLw1NjmaXsLGS2uOjD7c3Qi7NfoAJQmyKQ7aKrLw1VeeOTK5RhqvPuBYmQSai02Jy9R8NZWS8jPh4FNw8AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:46.621474Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.04076","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd249f516cd20cb468f7a36a6497d0e36b09ee370c8c1e7d5ea4c3625f17084b","sha256:3fb5674f1325e56eaca68252d7868db3dbdc5f8c5770bfbbe3d378351b5fd755"],"state_sha256":"1dc88f34da8eae859158c193add3c23b5bc98acf5e470eee37e2a2caefe9fb17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hn5zsIJBPD9+LMdnuAr+6qDp6A1xLPJbdxzVIFdNQ3vIX/ePX0ih9EchmzWQIG4oz2kY2uoANlCxmmhgRXnlAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T06:57:50.306212Z","bundle_sha256":"8c2bf881a9210ab6d053a20c75d22c7eed9554cd16fb3921f05137c1aa3b2cc2"}}