{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:I4QXRJI5C6UGWJANX2VQDYZF3D","short_pith_number":"pith:I4QXRJI5","canonical_record":{"source":{"id":"2601.06600","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-10T15:43:30Z","cross_cats_sorted":[],"title_canon_sha256":"8d38befa857f4ce96df5ff08c55227f9151f7c7cb2964d5c14e4506dd5b2de75","abstract_canon_sha256":"ea9069f2680b490df9a1d222f81639a0a893663cbb0c707edfdef08b6f4e3679"},"schema_version":"1.0"},"canonical_sha256":"472178a51d17a86b240dbeab01e325d8ed57e5451bb0eeb13f9655ac9938e9d5","source":{"kind":"arxiv","id":"2601.06600","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.06600","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2601.06600v3","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.06600","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"I4QXRJI5C6UG","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"I4QXRJI5C6UGWJAN","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"I4QXRJI5","created_at":"2026-05-20T00:02:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:I4QXRJI5C6UGWJANX2VQDYZF3D","target":"record","payload":{"canonical_record":{"source":{"id":"2601.06600","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-10T15:43:30Z","cross_cats_sorted":[],"title_canon_sha256":"8d38befa857f4ce96df5ff08c55227f9151f7c7cb2964d5c14e4506dd5b2de75","abstract_canon_sha256":"ea9069f2680b490df9a1d222f81639a0a893663cbb0c707edfdef08b6f4e3679"},"schema_version":"1.0"},"canonical_sha256":"472178a51d17a86b240dbeab01e325d8ed57e5451bb0eeb13f9655ac9938e9d5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:08.334192Z","signature_b64":"ctO1+3+/tGahQXH7W4NcXwXQhWlLQYgonofMtuKIfkGBsoKdpvv3KlNn3zXkgEw02Mfx0Ow69fPSUqPICM4zDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"472178a51d17a86b240dbeab01e325d8ed57e5451bb0eeb13f9655ac9938e9d5","last_reissued_at":"2026-05-20T00:02:08.333367Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:08.333367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.06600","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:02:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vEa5eR21wqVcRZzDkNzH91AJR11RSTorY1gzCAcjGl5uIUx4Gi0Y5IvdUleK1wxgnvPlAMSy4Eum28sSnS3FDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:47:36.820097Z"},"content_sha256":"d8e80cd533f4cea6d904ec81d85cf3240fca4f9b4ab517e7a310cc75bed16456","schema_version":"1.0","event_id":"sha256:d8e80cd533f4cea6d904ec81d85cf3240fca4f9b4ab517e7a310cc75bed16456"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:I4QXRJI5C6UGWJANX2VQDYZF3D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Multimodal LLMs vary widely in resisting cognitive biases when evaluating Chinese short-video health misinformation.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chang Chen, Jen-tse Huang, Mark Dredze, Michelle R. Kaufman, Shiyang Lai, Wenxuan Wang","submitted_at":"2026-01-10T15:43:30Z","abstract_excerpt":"Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annotations for three deceptive patterns-experimental err"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Models are susceptible to biases like authoritative channel IDs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The manually annotated dataset of 200 videos accurately represents deceptive patterns in short-video misinformation and that the belief score reliably measures the models' susceptibility to cognitive biases.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Multimodal LLMs exhibit different levels of susceptibility to misinformation in short videos, with Gemini-2.5-Pro showing the highest resistance (belief score 71.5) and o3 the lowest (35.2).","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multimodal LLMs vary widely in resisting cognitive biases when evaluating Chinese short-video health misinformation.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"bc2f24df47ab0a9f29e52801f41a48237da6f2a47baf11ec922919a990febb03"},"source":{"id":"2601.06600","kind":"arxiv","version":3},"verdict":{"id":"7d87807d-6358-44c3-ba39-d2a1dede7436","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T14:58:45.543139Z","strongest_claim":"Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Models are susceptible to biases like authoritative channel IDs.","one_line_summary":"Multimodal LLMs exhibit different levels of susceptibility to misinformation in short videos, with Gemini-2.5-Pro showing the highest resistance (belief score 71.5) and o3 the lowest (35.2).","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The manually annotated dataset of 200 videos accurately represents deceptive patterns in short-video misinformation and that the belief score reliably measures the models' susceptibility to cognitive biases.","pith_extraction_headline":"Multimodal LLMs vary widely in resisting cognitive biases when evaluating Chinese short-video health misinformation."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.06600/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":"7d87807d-6358-44c3-ba39-d2a1dede7436"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:02:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pfmhfNJJqeHszapNqai+bOYvpcAgN/J8dRRrCzVlNw3hEqFf3DYmeeXdF4OdZ0+PW4VPCIo8z3zvk+use8sVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T19:47:36.820744Z"},"content_sha256":"06ab6672f9a8bb4d6f5618ce76fdfcd71c4468dbe11c4cfd6e42fa14e381b936","schema_version":"1.0","event_id":"sha256:06ab6672f9a8bb4d6f5618ce76fdfcd71c4468dbe11c4cfd6e42fa14e381b936"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I4QXRJI5C6UGWJANX2VQDYZF3D/bundle.json","state_url":"https://pith.science/pith/I4QXRJI5C6UGWJANX2VQDYZF3D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I4QXRJI5C6UGWJANX2VQDYZF3D/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":"2026-05-30T19:47:36Z","links":{"resolver":"https://pith.science/pith/I4QXRJI5C6UGWJANX2VQDYZF3D","bundle":"https://pith.science/pith/I4QXRJI5C6UGWJANX2VQDYZF3D/bundle.json","state":"https://pith.science/pith/I4QXRJI5C6UGWJANX2VQDYZF3D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I4QXRJI5C6UGWJANX2VQDYZF3D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:I4QXRJI5C6UGWJANX2VQDYZF3D","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":"ea9069f2680b490df9a1d222f81639a0a893663cbb0c707edfdef08b6f4e3679","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-10T15:43:30Z","title_canon_sha256":"8d38befa857f4ce96df5ff08c55227f9151f7c7cb2964d5c14e4506dd5b2de75"},"schema_version":"1.0","source":{"id":"2601.06600","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.06600","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2601.06600v3","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.06600","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"I4QXRJI5C6UG","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"I4QXRJI5C6UGWJAN","created_at":"2026-05-20T00:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"I4QXRJI5","created_at":"2026-05-20T00:02:08Z"}],"graph_snapshots":[{"event_id":"sha256:06ab6672f9a8bb4d6f5618ce76fdfcd71c4468dbe11c4cfd6e42fa14e381b936","target":"graph","created_at":"2026-05-20T00:02:08Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Models are susceptible to biases like authoritative channel IDs."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The manually annotated dataset of 200 videos accurately represents deceptive patterns in short-video misinformation and that the belief score reliably measures the models' susceptibility to cognitive biases."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Multimodal LLMs exhibit different levels of susceptibility to misinformation in short videos, with Gemini-2.5-Pro showing the highest resistance (belief score 71.5) and o3 the lowest (35.2)."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Multimodal LLMs vary widely in resisting cognitive biases when evaluating Chinese short-video health misinformation."}],"snapshot_sha256":"bc2f24df47ab0a9f29e52801f41a48237da6f2a47baf11ec922919a990febb03"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2601.06600/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annotations for three deceptive patterns-experimental err","authors_text":"Chang Chen, Jen-tse Huang, Mark Dredze, Michelle R. Kaufman, Shiyang Lai, Wenxuan Wang","cross_cats":[],"headline":"Multimodal LLMs vary widely in resisting cognitive biases when evaluating Chinese short-video health misinformation.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-10T15:43:30Z","title":"Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.06600","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-16T14:58:45.543139Z","id":"7d87807d-6358-44c3-ba39-d2a1dede7436","model_set":{"reader":"grok-4.3"},"one_line_summary":"Multimodal LLMs exhibit different levels of susceptibility to misinformation in short videos, with Gemini-2.5-Pro showing the highest resistance (belief score 71.5) and o3 the lowest (35.2).","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Multimodal LLMs vary widely in resisting cognitive biases when evaluating Chinese short-video health misinformation.","strongest_claim":"Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Models are susceptible to biases like authoritative channel IDs.","weakest_assumption":"The manually annotated dataset of 200 videos accurately represents deceptive patterns in short-video misinformation and that the belief score reliably measures the models' susceptibility to cognitive biases."}},"verdict_id":"7d87807d-6358-44c3-ba39-d2a1dede7436"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d8e80cd533f4cea6d904ec81d85cf3240fca4f9b4ab517e7a310cc75bed16456","target":"record","created_at":"2026-05-20T00:02:08Z","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":"ea9069f2680b490df9a1d222f81639a0a893663cbb0c707edfdef08b6f4e3679","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-10T15:43:30Z","title_canon_sha256":"8d38befa857f4ce96df5ff08c55227f9151f7c7cb2964d5c14e4506dd5b2de75"},"schema_version":"1.0","source":{"id":"2601.06600","kind":"arxiv","version":3}},"canonical_sha256":"472178a51d17a86b240dbeab01e325d8ed57e5451bb0eeb13f9655ac9938e9d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"472178a51d17a86b240dbeab01e325d8ed57e5451bb0eeb13f9655ac9938e9d5","first_computed_at":"2026-05-20T00:02:08.333367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:08.333367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ctO1+3+/tGahQXH7W4NcXwXQhWlLQYgonofMtuKIfkGBsoKdpvv3KlNn3zXkgEw02Mfx0Ow69fPSUqPICM4zDg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:08.334192Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.06600","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8e80cd533f4cea6d904ec81d85cf3240fca4f9b4ab517e7a310cc75bed16456","sha256:06ab6672f9a8bb4d6f5618ce76fdfcd71c4468dbe11c4cfd6e42fa14e381b936"],"state_sha256":"eb55830555fef72b33e4fd712854ce023b8bac06160023f13b806249149c3f87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8t4FIDruOLQ8g/NGzvoNTSTWVYvW4v8zRUWEyNo6Jr48N7OF6OoijPiY4HFyubfZgT02Dwx4se2t+yRt35mHBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T19:47:36.823475Z","bundle_sha256":"15f46dbeababd7543a8e5f5a4904fd628583017145dbc1b96c6ead8a16634d78"}}