{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MGNOO27C5YUTUBL4MFZIF37NIM","short_pith_number":"pith:MGNOO27C","canonical_record":{"source":{"id":"2405.17821","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-28T04:41:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"89821a7428c3904d1851e2c5709cf83d95a3fcac3366f082563534e2e23f7bbe","abstract_canon_sha256":"8b63e149717a0471f58109c2dded19f340095a6d3efd883cb9968ed4067662dd"},"schema_version":"1.0"},"canonical_sha256":"619ae76be2ee293a057c617282efed432ceee387460dfe42e0aa5b5b86dbaa79","source":{"kind":"arxiv","id":"2405.17821","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17821","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17821v2","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17821","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"pith_short_12","alias_value":"MGNOO27C5YUT","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"pith_short_16","alias_value":"MGNOO27C5YUTUBL4","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"pith_short_8","alias_value":"MGNOO27C","created_at":"2026-07-05T09:49:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MGNOO27C5YUTUBL4MFZIF37NIM","target":"record","payload":{"canonical_record":{"source":{"id":"2405.17821","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-28T04:41:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"89821a7428c3904d1851e2c5709cf83d95a3fcac3366f082563534e2e23f7bbe","abstract_canon_sha256":"8b63e149717a0471f58109c2dded19f340095a6d3efd883cb9968ed4067662dd"},"schema_version":"1.0"},"canonical_sha256":"619ae76be2ee293a057c617282efed432ceee387460dfe42e0aa5b5b86dbaa79","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:42.899974Z","signature_b64":"eh0qDTcloK9cstAV1dnPErTab2mBGLv626XzmWN9cCDO2JLJ6CPRvZKqpTjy94X3GVTd0k+epDB7FVfImFRGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"619ae76be2ee293a057c617282efed432ceee387460dfe42e0aa5b5b86dbaa79","last_reissued_at":"2026-07-05T09:49:42.899447Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:42.899447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.17821","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":"2026-07-05T09:49:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sUBHJWSDUcsBM5eEI7rj5sAKM1NiCN6X4RUazRg/jQa4BtfT0Rq1iLFuCUJY30IfxfkiP9AxDSt381wFcjnYAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:54:33.297913Z"},"content_sha256":"0b9be69a592aa4d985b4d2109b5b46f0aca1663c016db944d4f9d320545da915","schema_version":"1.0","event_id":"sha256:0b9be69a592aa4d985b4d2109b5b46f0aca1663c016db944d4f9d320545da915"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MGNOO27C5YUTUBL4MFZIF37NIM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Changick Kim, Donguk Kim, Jaehyuk Jang, Sangmin Woo, Yubin Choi","submitted_at":"2024-05-28T04:41:02Z","abstract_excerpt":"Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produce \"hallucinatory\" outputs that misinterpret visual information, posing challenges in reliability and trustworthiness. We propose RITUAL, a simple decoding method that reduces hallucinations by leveraging randomly transformed images as complementary inputs during decoding, adjusting the output probability distribution without additional training or external models. Our key insight is that random transformations expose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17821","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/2405.17821/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":"2026-07-05T09:49:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"grCRaQfbjIdRn4L3EJtVDWsbNAOeQ6y/sXMbm4ENKJ0ODtdxdLuMCrvbHTeWrV2WjqzTpnGcHX1VGWAufPPSAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:54:33.298865Z"},"content_sha256":"e7380f4a848d95e9f6145cdda31b5b4513462c24e20d512ed7b0f14e7f3da004","schema_version":"1.0","event_id":"sha256:e7380f4a848d95e9f6145cdda31b5b4513462c24e20d512ed7b0f14e7f3da004"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MGNOO27C5YUTUBL4MFZIF37NIM/bundle.json","state_url":"https://pith.science/pith/MGNOO27C5YUTUBL4MFZIF37NIM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MGNOO27C5YUTUBL4MFZIF37NIM/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":"2026-08-08T05:54:33Z","links":{"resolver":"https://pith.science/pith/MGNOO27C5YUTUBL4MFZIF37NIM","bundle":"https://pith.science/pith/MGNOO27C5YUTUBL4MFZIF37NIM/bundle.json","state":"https://pith.science/pith/MGNOO27C5YUTUBL4MFZIF37NIM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MGNOO27C5YUTUBL4MFZIF37NIM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MGNOO27C5YUTUBL4MFZIF37NIM","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":"8b63e149717a0471f58109c2dded19f340095a6d3efd883cb9968ed4067662dd","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-28T04:41:02Z","title_canon_sha256":"89821a7428c3904d1851e2c5709cf83d95a3fcac3366f082563534e2e23f7bbe"},"schema_version":"1.0","source":{"id":"2405.17821","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17821","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17821v2","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17821","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"pith_short_12","alias_value":"MGNOO27C5YUT","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"pith_short_16","alias_value":"MGNOO27C5YUTUBL4","created_at":"2026-07-05T09:49:42Z"},{"alias_kind":"pith_short_8","alias_value":"MGNOO27C","created_at":"2026-07-05T09:49:42Z"}],"graph_snapshots":[{"event_id":"sha256:e7380f4a848d95e9f6145cdda31b5b4513462c24e20d512ed7b0f14e7f3da004","target":"graph","created_at":"2026-07-05T09:49:42Z","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/2405.17821/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produce \"hallucinatory\" outputs that misinterpret visual information, posing challenges in reliability and trustworthiness. We propose RITUAL, a simple decoding method that reduces hallucinations by leveraging randomly transformed images as complementary inputs during decoding, adjusting the output probability distribution without additional training or external models. Our key insight is that random transformations expose","authors_text":"Changick Kim, Donguk Kim, Jaehyuk Jang, Sangmin Woo, Yubin Choi","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-28T04:41:02Z","title":"RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17821","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":"sha256:0b9be69a592aa4d985b4d2109b5b46f0aca1663c016db944d4f9d320545da915","target":"record","created_at":"2026-07-05T09:49:42Z","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":"8b63e149717a0471f58109c2dded19f340095a6d3efd883cb9968ed4067662dd","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-28T04:41:02Z","title_canon_sha256":"89821a7428c3904d1851e2c5709cf83d95a3fcac3366f082563534e2e23f7bbe"},"schema_version":"1.0","source":{"id":"2405.17821","kind":"arxiv","version":2}},"canonical_sha256":"619ae76be2ee293a057c617282efed432ceee387460dfe42e0aa5b5b86dbaa79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"619ae76be2ee293a057c617282efed432ceee387460dfe42e0aa5b5b86dbaa79","first_computed_at":"2026-07-05T09:49:42.899447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:49:42.899447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eh0qDTcloK9cstAV1dnPErTab2mBGLv626XzmWN9cCDO2JLJ6CPRvZKqpTjy94X3GVTd0k+epDB7FVfImFRGCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:49:42.899974Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.17821","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b9be69a592aa4d985b4d2109b5b46f0aca1663c016db944d4f9d320545da915","sha256:e7380f4a848d95e9f6145cdda31b5b4513462c24e20d512ed7b0f14e7f3da004"],"state_sha256":"1335747ff021741d1452e14a3db29f3c2949a2d2bb512158cbdf4371eeaa7462"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WB0zvOVFzyMYQTSRehyj3WotXD8Nc0/j3sjZ29tfCX+k6PyL0RuHcODCp3EDCZsmM3z2mY8wyLM0Rn1ARf6gDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T05:54:33.304199Z","bundle_sha256":"aa3fad99bb927d424579bb766cc86f9e8ccc191c174d834752071abde03adbab"}}