{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GGQF5MMK2YGYTDRELSQKWTU6WW","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":"1e2de9fc86b351dbaad6f2730b1e2b7ccbc40e6a06df11545232a9a3c099a32a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T13:26:56Z","title_canon_sha256":"88213beb1683aa4b82f0e6c37c34503b5efdfe93340348725be1c1c5ad9bccc7"},"schema_version":"1.0","source":{"id":"2505.15506","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15506","created_at":"2026-07-05T11:06:46Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15506v1","created_at":"2026-07-05T11:06:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15506","created_at":"2026-07-05T11:06:46Z"},{"alias_kind":"pith_short_12","alias_value":"GGQF5MMK2YGY","created_at":"2026-07-05T11:06:46Z"},{"alias_kind":"pith_short_16","alias_value":"GGQF5MMK2YGYTDRE","created_at":"2026-07-05T11:06:46Z"},{"alias_kind":"pith_short_8","alias_value":"GGQF5MMK","created_at":"2026-07-05T11:06:46Z"}],"graph_snapshots":[{"event_id":"sha256:36cd07b5f26c01a75a5eacd72c763433bf89a6092644df4c4c805bbc6f4aa137","target":"graph","created_at":"2026-07-05T11:06:46Z","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/2505.15506/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, Vision-Language foundation models like CLIP and ALIGN, which are pre-trained on large-scale data have shown remarkable zero-shot generalization to diverse datasets with different classes and even domains. In this work, we take a step further and analyze whether these models can be adapted to target datasets having very different distributions and classes compared to what these models have been trained on, using only a few labeled examples from the target dataset. In such scenarios, finetuning large pretrained models is challenging due to problems of overfitting as well as loss of gen","authors_text":"Anuska Roy, Debarshi Brahma, Soma Biswas","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T13:26:56Z","title":"Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15506","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:eb72d6a6f7fd345bcc53613365e65bd5feced137c685fca0af8214d40b8c20cd","target":"record","created_at":"2026-07-05T11:06:46Z","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":"1e2de9fc86b351dbaad6f2730b1e2b7ccbc40e6a06df11545232a9a3c099a32a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T13:26:56Z","title_canon_sha256":"88213beb1683aa4b82f0e6c37c34503b5efdfe93340348725be1c1c5ad9bccc7"},"schema_version":"1.0","source":{"id":"2505.15506","kind":"arxiv","version":1}},"canonical_sha256":"31a05eb18ad60d898e245ca0ab4e9eb5840951721471ca9ab10c88f6d7a06032","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31a05eb18ad60d898e245ca0ab4e9eb5840951721471ca9ab10c88f6d7a06032","first_computed_at":"2026-07-05T11:06:46.894200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:06:46.894200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8GBSbBC64QPpxoBfPgkMJixGmNRtbjS371SW/YKBhOrwdSh7C7X/JNraqqvT7IWxgOXJiQsgJMl6D+M8/EfPBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:06:46.894637Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.15506","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb72d6a6f7fd345bcc53613365e65bd5feced137c685fca0af8214d40b8c20cd","sha256:36cd07b5f26c01a75a5eacd72c763433bf89a6092644df4c4c805bbc6f4aa137"],"state_sha256":"fcdbeced23cc5aebadcb3e976ec96fc33536a75ca906221697bfec3c5c21f770"}