{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3XPQI5PH53OBDYQ7Q63RBLOVSL","short_pith_number":"pith:3XPQI5PH","canonical_record":{"source":{"id":"2312.02546","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T07:29:14Z","cross_cats_sorted":[],"title_canon_sha256":"7a4dcf76f999cb7b5f69175a6fa52b1005a99759f7c3467767e13fbe26817047","abstract_canon_sha256":"cdbd56259bfa010a336e713a9a931df7ff05387b5462ad5f1970e9c57652af2b"},"schema_version":"1.0"},"canonical_sha256":"dddf0475e7eedc11e21f87b710add592c0307dae4d14c853248622d08c8b2543","source":{"kind":"arxiv","id":"2312.02546","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.02546","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2312.02546v2","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02546","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"3XPQI5PH53OB","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"3XPQI5PH53OBDYQ7","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"3XPQI5PH","created_at":"2026-07-05T08:24:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3XPQI5PH53OBDYQ7Q63RBLOVSL","target":"record","payload":{"canonical_record":{"source":{"id":"2312.02546","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T07:29:14Z","cross_cats_sorted":[],"title_canon_sha256":"7a4dcf76f999cb7b5f69175a6fa52b1005a99759f7c3467767e13fbe26817047","abstract_canon_sha256":"cdbd56259bfa010a336e713a9a931df7ff05387b5462ad5f1970e9c57652af2b"},"schema_version":"1.0"},"canonical_sha256":"dddf0475e7eedc11e21f87b710add592c0307dae4d14c853248622d08c8b2543","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:23.340307Z","signature_b64":"nnamTQXCR8SK3C4WnuOa5sD+i4boIyYpovnROKxJgyq9yfCeAUh7ryRMaF/A/UNz0QIFpxbOTviZ4bpCHqZUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dddf0475e7eedc11e21f87b710add592c0307dae4d14c853248622d08c8b2543","last_reissued_at":"2026-07-05T08:24:23.339850Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:23.339850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.02546","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EVkXjFZbXwOAjHSSJXQp88pvOkZy+KCEGpdSO0/EXhLpc1NHgTonpFsCdpYeLp83GlF/dmDp1NyYwCTfeG/aAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:14:37.186002Z"},"content_sha256":"ebcef07f9880a228e1f5a8ee220838c7329b0f95e03fb9c085396322f2011e86","schema_version":"1.0","event_id":"sha256:ebcef07f9880a228e1f5a8ee220838c7329b0f95e03fb9c085396322f2011e86"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3XPQI5PH53OBDYQ7Q63RBLOVSL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Liu, Hang Su, Shibao Zheng, Tongliang Liu, Yinpeng Dong, Zhuo Huang","submitted_at":"2023-12-05T07:29:14Z","abstract_excerpt":"Although vision models such as Contrastive Language-Image Pre-Training (CLIP) show impressive generalization performance, their zero-shot robustness is still limited under Out-of-Distribution (OOD) scenarios without fine-tuning. Instead of undesirably providing human supervision as commonly done, it is possible to take advantage of Multi-modal Large Language Models (MLLMs) that hold powerful visual understanding abilities. However, MLLMs are shown to struggle with vision problems due to the incompatibility of tasks, thus hindering their utilization. In this paper, we propose to effectively lev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02546","kind":"arxiv","version":2},"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/2312.02546/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":"2026-07-05T08:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dMN4RSPvJvm03hQDpT6Inh/7Pnc1xwHYC4iTxhjBsoVYDKORCoISyA4KwojjjcomqzmatygGO9ajjaluN6UTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T15:14:37.186499Z"},"content_sha256":"f8a56266c61f7fbcb4e3d1cfa638990f7d9f8fc9dee9e785e9ce7ce11feac3ab","schema_version":"1.0","event_id":"sha256:f8a56266c61f7fbcb4e3d1cfa638990f7d9f8fc9dee9e785e9ce7ce11feac3ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL/bundle.json","state_url":"https://pith.science/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL/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":"2026-08-09T15:14:37Z","links":{"resolver":"https://pith.science/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL","bundle":"https://pith.science/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL/bundle.json","state":"https://pith.science/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3XPQI5PH53OBDYQ7Q63RBLOVSL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3XPQI5PH53OBDYQ7Q63RBLOVSL","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":"cdbd56259bfa010a336e713a9a931df7ff05387b5462ad5f1970e9c57652af2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T07:29:14Z","title_canon_sha256":"7a4dcf76f999cb7b5f69175a6fa52b1005a99759f7c3467767e13fbe26817047"},"schema_version":"1.0","source":{"id":"2312.02546","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.02546","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2312.02546v2","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02546","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"3XPQI5PH53OB","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"3XPQI5PH53OBDYQ7","created_at":"2026-07-05T08:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"3XPQI5PH","created_at":"2026-07-05T08:24:23Z"}],"graph_snapshots":[{"event_id":"sha256:f8a56266c61f7fbcb4e3d1cfa638990f7d9f8fc9dee9e785e9ce7ce11feac3ab","target":"graph","created_at":"2026-07-05T08:24:23Z","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/2312.02546/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although vision models such as Contrastive Language-Image Pre-Training (CLIP) show impressive generalization performance, their zero-shot robustness is still limited under Out-of-Distribution (OOD) scenarios without fine-tuning. Instead of undesirably providing human supervision as commonly done, it is possible to take advantage of Multi-modal Large Language Models (MLLMs) that hold powerful visual understanding abilities. However, MLLMs are shown to struggle with vision problems due to the incompatibility of tasks, thus hindering their utilization. In this paper, we propose to effectively lev","authors_text":"Chang Liu, Hang Su, Shibao Zheng, Tongliang Liu, Yinpeng Dong, Zhuo Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T07:29:14Z","title":"Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02546","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:ebcef07f9880a228e1f5a8ee220838c7329b0f95e03fb9c085396322f2011e86","target":"record","created_at":"2026-07-05T08:24:23Z","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":"cdbd56259bfa010a336e713a9a931df7ff05387b5462ad5f1970e9c57652af2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T07:29:14Z","title_canon_sha256":"7a4dcf76f999cb7b5f69175a6fa52b1005a99759f7c3467767e13fbe26817047"},"schema_version":"1.0","source":{"id":"2312.02546","kind":"arxiv","version":2}},"canonical_sha256":"dddf0475e7eedc11e21f87b710add592c0307dae4d14c853248622d08c8b2543","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dddf0475e7eedc11e21f87b710add592c0307dae4d14c853248622d08c8b2543","first_computed_at":"2026-07-05T08:24:23.339850Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:24:23.339850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nnamTQXCR8SK3C4WnuOa5sD+i4boIyYpovnROKxJgyq9yfCeAUh7ryRMaF/A/UNz0QIFpxbOTviZ4bpCHqZUDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:24:23.340307Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.02546","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ebcef07f9880a228e1f5a8ee220838c7329b0f95e03fb9c085396322f2011e86","sha256:f8a56266c61f7fbcb4e3d1cfa638990f7d9f8fc9dee9e785e9ce7ce11feac3ab"],"state_sha256":"ab817bcbb9e5d77c7556f7d7293f16a3b9223a02a6d7429efd48e0085f4ae53f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3I7xPJso9NTImNcKrodDXTl+SFMjhHmzcLLQ8EbqwzErPUCfjWufZzcwI0dxLnnKFUQfXr6ScFGcRpN6shw1CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T15:14:37.190046Z","bundle_sha256":"51642d5e5298d37961fa059eaaab7bc69c1d8f751f18a34df0a423dc51b5a8b3"}}