{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ","short_pith_number":"pith:P7ZFSW6Y","canonical_record":{"source":{"id":"2408.05775","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-11T13:55:58Z","cross_cats_sorted":[],"title_canon_sha256":"c8df3304045a7666b120c002501e23a88c24d0ad9d896c125efc04b6c74797f8","abstract_canon_sha256":"a968d578285195b4e0b93796eab25f53a071d031d22b2405bbfbd8399b33ed0e"},"schema_version":"1.0"},"canonical_sha256":"7ff2595bd8fb160f10efc731fb6e13a651432a4e70afbc5f8618f5f3df0b604c","source":{"kind":"arxiv","id":"2408.05775","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.05775","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"arxiv_version","alias_value":"2408.05775v1","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.05775","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"pith_short_12","alias_value":"P7ZFSW6Y7MLA","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"pith_short_16","alias_value":"P7ZFSW6Y7MLA6EHP","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"pith_short_8","alias_value":"P7ZFSW6Y","created_at":"2026-07-05T08:54:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ","target":"record","payload":{"canonical_record":{"source":{"id":"2408.05775","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-11T13:55:58Z","cross_cats_sorted":[],"title_canon_sha256":"c8df3304045a7666b120c002501e23a88c24d0ad9d896c125efc04b6c74797f8","abstract_canon_sha256":"a968d578285195b4e0b93796eab25f53a071d031d22b2405bbfbd8399b33ed0e"},"schema_version":"1.0"},"canonical_sha256":"7ff2595bd8fb160f10efc731fb6e13a651432a4e70afbc5f8618f5f3df0b604c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:24.902417Z","signature_b64":"3saVQOWRQeJnJJumNbc2n00ZZe7+kGIH5JDtUo2EuHX4QKpa3loorksaPcCB9Y4xN2FY09EwLlQZqvHqSVXvBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ff2595bd8fb160f10efc731fb6e13a651432a4e70afbc5f8618f5f3df0b604c","last_reissued_at":"2026-07-05T08:54:24.902005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:24.902005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.05775","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":"2026-07-05T08:54:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iJpfFya4W5zseRLaHhLWjBtLpsc/wkpX46nwFojr7x+LTSsqmuLE9u4QvDR4Geq5y8susNLqeMgLxEIfWzmhBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:15:40.551407Z"},"content_sha256":"80016a5e8a6f3dc2f1061b82e7af9d4f4107f9e35bfd9802bd5cc2deda11e8d7","schema_version":"1.0","event_id":"sha256:80016a5e8a6f3dc2f1061b82e7af9d4f4107f9e35bfd9802bd5cc2deda11e8d7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Test-Time Prompt Tuning for Vision-Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Xu, Gangshan Wu, Guozhen Zhang, Haocheng Shen, Limin Wang, Xiaoxin Chen, Yuhan Zhu","submitted_at":"2024-08-11T13:55:58Z","abstract_excerpt":"Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of test-time prompt tuning; however, these methods typically require per-image prompt adaptation during inference, which incurs high computational budgets and limits scalability and practical deployment. To overcome this issue, we introduce Self-TPT, a novel framework leveraging Self-supervised learning for efficient Test-time Prompt Tuning. The key aspect of Self-TPT is that it turns to efficient predefined class adaptat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.05775","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/2408.05775/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":"2026-07-05T08:54:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xhXIjmMDSAuaCSIbs6e8LsqRjlYOSSN835olcGHo0kfBpveoaoiZ3+L3jnjm3UXiwkM4qUmAqqYTZo2rQlwlDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:15:40.552028Z"},"content_sha256":"e7576961feeeff3536b9d9970a9ee2b7602ec4202f3e417fd65d20f03857622a","schema_version":"1.0","event_id":"sha256:e7576961feeeff3536b9d9970a9ee2b7602ec4202f3e417fd65d20f03857622a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ/bundle.json","state_url":"https://pith.science/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ/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":"2026-08-07T17:15:40Z","links":{"resolver":"https://pith.science/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ","bundle":"https://pith.science/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ/bundle.json","state":"https://pith.science/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:P7ZFSW6Y7MLA6EHPY4Y7W3QTUZ","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":"a968d578285195b4e0b93796eab25f53a071d031d22b2405bbfbd8399b33ed0e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-11T13:55:58Z","title_canon_sha256":"c8df3304045a7666b120c002501e23a88c24d0ad9d896c125efc04b6c74797f8"},"schema_version":"1.0","source":{"id":"2408.05775","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.05775","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"arxiv_version","alias_value":"2408.05775v1","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.05775","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"pith_short_12","alias_value":"P7ZFSW6Y7MLA","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"pith_short_16","alias_value":"P7ZFSW6Y7MLA6EHP","created_at":"2026-07-05T08:54:24Z"},{"alias_kind":"pith_short_8","alias_value":"P7ZFSW6Y","created_at":"2026-07-05T08:54:24Z"}],"graph_snapshots":[{"event_id":"sha256:e7576961feeeff3536b9d9970a9ee2b7602ec4202f3e417fd65d20f03857622a","target":"graph","created_at":"2026-07-05T08:54:24Z","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/2408.05775/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of test-time prompt tuning; however, these methods typically require per-image prompt adaptation during inference, which incurs high computational budgets and limits scalability and practical deployment. To overcome this issue, we introduce Self-TPT, a novel framework leveraging Self-supervised learning for efficient Test-time Prompt Tuning. The key aspect of Self-TPT is that it turns to efficient predefined class adaptat","authors_text":"Chen Xu, Gangshan Wu, Guozhen Zhang, Haocheng Shen, Limin Wang, Xiaoxin Chen, Yuhan Zhu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-11T13:55:58Z","title":"Efficient Test-Time Prompt Tuning for Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.05775","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":"sha256:80016a5e8a6f3dc2f1061b82e7af9d4f4107f9e35bfd9802bd5cc2deda11e8d7","target":"record","created_at":"2026-07-05T08:54:24Z","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":"a968d578285195b4e0b93796eab25f53a071d031d22b2405bbfbd8399b33ed0e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-08-11T13:55:58Z","title_canon_sha256":"c8df3304045a7666b120c002501e23a88c24d0ad9d896c125efc04b6c74797f8"},"schema_version":"1.0","source":{"id":"2408.05775","kind":"arxiv","version":1}},"canonical_sha256":"7ff2595bd8fb160f10efc731fb6e13a651432a4e70afbc5f8618f5f3df0b604c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ff2595bd8fb160f10efc731fb6e13a651432a4e70afbc5f8618f5f3df0b604c","first_computed_at":"2026-07-05T08:54:24.902005Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:54:24.902005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3saVQOWRQeJnJJumNbc2n00ZZe7+kGIH5JDtUo2EuHX4QKpa3loorksaPcCB9Y4xN2FY09EwLlQZqvHqSVXvBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:54:24.902417Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.05775","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:80016a5e8a6f3dc2f1061b82e7af9d4f4107f9e35bfd9802bd5cc2deda11e8d7","sha256:e7576961feeeff3536b9d9970a9ee2b7602ec4202f3e417fd65d20f03857622a"],"state_sha256":"296b42951d38f15adcad5f4004018897fbed92bbe0f2ec3d2ba65e8538946f51"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"POeO15IkUtwlXt6xfZUp2nNsKibGs+S1PZLtSlMldhsgOzokXWFr4IrIH8d+SK4QubRxqCv7L30oGlSQZpkvCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:15:40.557268Z","bundle_sha256":"243d6433258f92794bb3acd290386869cb1fcdf8f8790908578f9fdcaefcbeed"}}