{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YGVCJACOWDX5SEKVAKYZ43SVTC","short_pith_number":"pith:YGVCJACO","schema_version":"1.0","canonical_sha256":"c1aa24804eb0efd9115502b19e6e559889fca928c0177f5477634ad501946201","source":{"kind":"arxiv","id":"2410.12790","version":1},"attestation_state":"computed","paper":{"title":"Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ce Zhang, Katia Sycara, Simon Stepputtis, Yaqi Xie","submitted_at":"2024-10-16T17:59:49Z","abstract_excerpt":"Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have applied this setting to advanced pre-trained vision-language models (VLMs), developing approaches such as test-time prompt tuning to further extend their practical applicability. However, these methods typically focus solely on adapting VLMs from a single modality and fail to accumulate task-specific knowledge as more samples are processed. To address this, we introduce Dual Prototype Evolving (DPE), a novel test-time "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.12790","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-16T17:59:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bbee513389f06b4888be6fd11edc7add0ccc67fab0f1cfffb50d4cc58641a6af","abstract_canon_sha256":"0fee3e9b944189907c41f1a512fa384e70f1dbce4a2c04255b404cd7e0cab094"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:34.021771Z","signature_b64":"iIdnVYRS1ebyL1YmRAXMFWF6skfGbFcWy6QAhC2OVNHXxz7f8bk2JIKnrTe8XZP70r22hA1Z9dmzIAu2QWNGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1aa24804eb0efd9115502b19e6e559889fca928c0177f5477634ad501946201","last_reissued_at":"2026-07-05T09:21:34.020996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:34.020996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ce Zhang, Katia Sycara, Simon Stepputtis, Yaqi Xie","submitted_at":"2024-10-16T17:59:49Z","abstract_excerpt":"Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have applied this setting to advanced pre-trained vision-language models (VLMs), developing approaches such as test-time prompt tuning to further extend their practical applicability. However, these methods typically focus solely on adapting VLMs from a single modality and fail to accumulate task-specific knowledge as more samples are processed. To address this, we introduce Dual Prototype Evolving (DPE), a novel test-time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12790","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/2410.12790/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.12790","created_at":"2026-07-05T09:21:34.021124+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.12790v1","created_at":"2026-07-05T09:21:34.021124+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12790","created_at":"2026-07-05T09:21:34.021124+00:00"},{"alias_kind":"pith_short_12","alias_value":"YGVCJACOWDX5","created_at":"2026-07-05T09:21:34.021124+00:00"},{"alias_kind":"pith_short_16","alias_value":"YGVCJACOWDX5SEKV","created_at":"2026-07-05T09:21:34.021124+00:00"},{"alias_kind":"pith_short_8","alias_value":"YGVCJACO","created_at":"2026-07-05T09:21:34.021124+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28719","citing_title":"ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC","json":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC.json","graph_json":"https://pith.science/api/pith-number/YGVCJACOWDX5SEKVAKYZ43SVTC/graph.json","events_json":"https://pith.science/api/pith-number/YGVCJACOWDX5SEKVAKYZ43SVTC/events.json","paper":"https://pith.science/paper/YGVCJACO"},"agent_actions":{"view_html":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC","download_json":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC.json","view_paper":"https://pith.science/paper/YGVCJACO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.12790&json=true","fetch_graph":"https://pith.science/api/pith-number/YGVCJACOWDX5SEKVAKYZ43SVTC/graph.json","fetch_events":"https://pith.science/api/pith-number/YGVCJACOWDX5SEKVAKYZ43SVTC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC/action/storage_attestation","attest_author":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC/action/author_attestation","sign_citation":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC/action/citation_signature","submit_replication":"https://pith.science/pith/YGVCJACOWDX5SEKVAKYZ43SVTC/action/replication_record"}},"created_at":"2026-07-05T09:21:34.021124+00:00","updated_at":"2026-07-05T09:21:34.021124+00:00"}