{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GUPUS3DTY5LRCNH7K7MFNHWGEY","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":"6db49f01e4d229aacece26686736c92633577ae92e1bf62579adaa7cdec38e8f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:07:08Z","title_canon_sha256":"32348d95af17dac34b77b407addf39cbb653307111e0e3185f71f5f07e06d276"},"schema_version":"1.0","source":{"id":"2605.17577","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17577","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17577v1","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17577","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_12","alias_value":"GUPUS3DTY5LR","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_16","alias_value":"GUPUS3DTY5LRCNH7","created_at":"2026-05-20T00:04:46Z"},{"alias_kind":"pith_short_8","alias_value":"GUPUS3DT","created_at":"2026-05-20T00:04:46Z"}],"graph_snapshots":[{"event_id":"sha256:adeb7669c9b70e1131048e7f4a956eb1e0657bbe9ff7f67827df66100f15c4f9","target":"graph","created_at":"2026-05-20T00:04:46Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.590760Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T21:21:57.521597Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.17577/integrity.json","findings":[],"snapshot_sha256":"ee7e1febd20ac89d3dd6fd3e10f6ece55004d73ef7f7ee347086401e37531df9","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to imperceptible adversarial perturbations, raising serious safety concerns for open-world deployment. To enhance robustness without requiring downstream task-specific retraining, we propose TAME, a novel test-time defense. Building upon our prior Test-Time Adversarial Prompt Tuning (TAPT), TAME introduces an architectural reformulation by replacing TAPT's single adaptive prompt with an input-conditioned Mixture-of-Experts (MoE) framework, enabling more exp","authors_text":"Jiaming Zhang, Jiaqi Yu, Jingjing Chen, Kai Chen, Ruofan Wang, Xingjun Ma, Xin Wang, Yixu Wang, Yu-Gang Jiang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:07:08Z","title":"TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17577","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:f10523cb54110db4b809e717912b0c0ceb5ffa0ae5fc2debd8642309f1983695","target":"record","created_at":"2026-05-20T00:04:46Z","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":"6db49f01e4d229aacece26686736c92633577ae92e1bf62579adaa7cdec38e8f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T18:07:08Z","title_canon_sha256":"32348d95af17dac34b77b407addf39cbb653307111e0e3185f71f5f07e06d276"},"schema_version":"1.0","source":{"id":"2605.17577","kind":"arxiv","version":1}},"canonical_sha256":"351f496c73c7571134ff57d8569ec626165a4cb5ef9a0fb9b83d6ce3ac6bb0b2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"351f496c73c7571134ff57d8569ec626165a4cb5ef9a0fb9b83d6ce3ac6bb0b2","first_computed_at":"2026-05-20T00:04:46.870530Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:46.870530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kf+7c0DNFuiKKc0VWI1zC+YP0WgoSAQRkR5swkzo9QQR+LkANAQyENC/gggqVzs+UDFfepSPpY3k2KWXqAdLCw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:46.871479Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17577","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f10523cb54110db4b809e717912b0c0ceb5ffa0ae5fc2debd8642309f1983695","sha256:adeb7669c9b70e1131048e7f4a956eb1e0657bbe9ff7f67827df66100f15c4f9"],"state_sha256":"aa12ed1f4564ec3fe6ee5d6a817518917ffe9c7457c9bbbe8cda0ecee97ae2e4"}