{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:C4W65WLDKVGS62QBXKI4LPCDWC","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":"bf96309b4858c34e4e1ec1d5e26b17fbacd02b6dd8974f2bf15a6bfa690a60cb","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-19T17:59:39Z","title_canon_sha256":"67185cfefc3972a7f90cb30f12ecb0298db75aadbed214cb09187e27a9fc0931"},"schema_version":"1.0","source":{"id":"2403.12964","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.12964","created_at":"2026-07-05T09:32:43Z"},{"alias_kind":"arxiv_version","alias_value":"2403.12964v2","created_at":"2026-07-05T09:32:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12964","created_at":"2026-07-05T09:32:43Z"},{"alias_kind":"pith_short_12","alias_value":"C4W65WLDKVGS","created_at":"2026-07-05T09:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"C4W65WLDKVGS62QB","created_at":"2026-07-05T09:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"C4W65WLD","created_at":"2026-07-05T09:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:cf8a526e36a179eab552d91c362f6380f28498a4e4b3b33433064e4ea8ede588","target":"graph","created_at":"2026-07-05T09:32:43Z","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/2403.12964/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale pre-trained Vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-efficient manner. However, when only a few labeled samples are available, adapting VLMs to distinguish subtle differences between similar classes in specific downstream tasks remains challenging. In this work, we propose a Simple yet effective Negative Learning approach, SimNL, to more efficiently exploit the task-specific knowledge from few-shot labeled samples. Unlike previous methods that focus on identifying a set of","authors_text":"Ce Zhang, Katia Sycara, Simon Stepputtis, Yaqi Xie","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-19T17:59:39Z","title":"Enhancing Vision-Language Few-Shot Adaptation with Negative Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12964","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":"sha256:3c3bf68f258740e707c4fae57c391b8aa640fb6dba017d3435dbc435e29ee6f9","target":"record","created_at":"2026-07-05T09:32:43Z","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":"bf96309b4858c34e4e1ec1d5e26b17fbacd02b6dd8974f2bf15a6bfa690a60cb","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-19T17:59:39Z","title_canon_sha256":"67185cfefc3972a7f90cb30f12ecb0298db75aadbed214cb09187e27a9fc0931"},"schema_version":"1.0","source":{"id":"2403.12964","kind":"arxiv","version":2}},"canonical_sha256":"172deed963554d2f6a01ba91c5bc43b0bb35547d6ac7fe09047dd436b4a89a25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"172deed963554d2f6a01ba91c5bc43b0bb35547d6ac7fe09047dd436b4a89a25","first_computed_at":"2026-07-05T09:32:43.213140Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:32:43.213140Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XuRWMJ8kXqhi6F8Q8Wd/j38HoWqCRtBqyMgpTGY+nGu5jSWeWOTCivUQwnMqJfi/LFWkUXKeXeypLoirb2SvDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:32:43.213664Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.12964","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c3bf68f258740e707c4fae57c391b8aa640fb6dba017d3435dbc435e29ee6f9","sha256:cf8a526e36a179eab552d91c362f6380f28498a4e4b3b33433064e4ea8ede588"],"state_sha256":"1a270af0079d74450effa67d85d534670a22970cad88b44f58b6dbff080de406"}