{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ST42HZWGVGU367NKJHCMQX4YJU","short_pith_number":"pith:ST42HZWG","canonical_record":{"source":{"id":"2502.11163","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-16T15:28:34Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"a9ab500ec1eab30f3ac309c9aaef34fa08aae9a215452c1ee3ace63fbc72a9f1","abstract_canon_sha256":"d4aa19be52317131aed0b2f4cafb874f1fea5d57867c38fcc1ed2b53e7d4ff19"},"schema_version":"1.0"},"canonical_sha256":"94f9a3e6c6a9a9bf7daa49c4c85f984d00fbd426479360cf7c0d27343a5d18f2","source":{"kind":"arxiv","id":"2502.11163","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11163","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11163v3","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11163","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"pith_short_12","alias_value":"ST42HZWGVGU3","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"pith_short_16","alias_value":"ST42HZWGVGU367NK","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"pith_short_8","alias_value":"ST42HZWG","created_at":"2026-07-05T12:06:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ST42HZWGVGU367NKJHCMQX4YJU","target":"record","payload":{"canonical_record":{"source":{"id":"2502.11163","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-16T15:28:34Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"a9ab500ec1eab30f3ac309c9aaef34fa08aae9a215452c1ee3ace63fbc72a9f1","abstract_canon_sha256":"d4aa19be52317131aed0b2f4cafb874f1fea5d57867c38fcc1ed2b53e7d4ff19"},"schema_version":"1.0"},"canonical_sha256":"94f9a3e6c6a9a9bf7daa49c4c85f984d00fbd426479360cf7c0d27343a5d18f2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:05.927008Z","signature_b64":"mc7WLDQew5RYrw0Ke3XSMfBCafG+7WGCOA7c8T5sZR0FJXyrDFbJrE6dh22l946vwLAq80fzDvDe4JVhG/yRBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94f9a3e6c6a9a9bf7daa49c4c85f984d00fbd426479360cf7c0d27343a5d18f2","last_reissued_at":"2026-07-05T12:06:05.926512Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:05.926512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.11163","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":"2026-07-05T12:06:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aMwxJ8qwqLuaCOI6lQdizIvR0bumqG4pT6Zo3JpZdIra6PQNh4RkUqAnKhKsQnd67K/ycNc1qGLvlKWC0C7dDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T12:26:37.690632Z"},"content_sha256":"24ca8fee99e6145857306d1092858a47c90ecf905e23de2d4840f3c82d564714","schema_version":"1.0","event_id":"sha256:24ca8fee99e6145857306d1092858a47c90ecf905e23de2d4840f3c82d564714"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ST42HZWGVGU367NKJHCMQX4YJU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AI Sees Your Location, But With A Bias Toward The Wealthy World","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Jen-tse Huang, Jieyu Zhao, Jingyuan Huang, Wenxuan Wang, Xiaoyuan Liu, Ziyi Liu","submitted_at":"2025-02-16T15:28:34Z","abstract_excerpt":"Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, VLMs still show regional biases in this task. To systematically evaluate these issues, we introduce a benchmark consisting of 1,200 images paired with detailed geographic metadata. Evaluating four VLMs, we find that while these models demonstrate the ability to recognize geographic information from images, achieving up to 53.8% accuracy in city prediction, they exhibit significant biases. Specifically, performance is substantially higher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11163","kind":"arxiv","version":3},"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/2502.11163/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":"2026-07-05T12:06:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rk+assjHNQLM8MilJlCmaxdTVTzrL4Ae4+5vI5ZkRxuK1cvkFrclI61XfNJy683feefK8L7u2kkatmustl0jDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T12:26:37.691006Z"},"content_sha256":"03f6e87fdc01150124181101151f11656b325b08221998f2b75ad1bb6dabff1c","schema_version":"1.0","event_id":"sha256:03f6e87fdc01150124181101151f11656b325b08221998f2b75ad1bb6dabff1c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ST42HZWGVGU367NKJHCMQX4YJU/bundle.json","state_url":"https://pith.science/pith/ST42HZWGVGU367NKJHCMQX4YJU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ST42HZWGVGU367NKJHCMQX4YJU/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":"2026-07-06T12:26:37Z","links":{"resolver":"https://pith.science/pith/ST42HZWGVGU367NKJHCMQX4YJU","bundle":"https://pith.science/pith/ST42HZWGVGU367NKJHCMQX4YJU/bundle.json","state":"https://pith.science/pith/ST42HZWGVGU367NKJHCMQX4YJU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ST42HZWGVGU367NKJHCMQX4YJU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ST42HZWGVGU367NKJHCMQX4YJU","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":"d4aa19be52317131aed0b2f4cafb874f1fea5d57867c38fcc1ed2b53e7d4ff19","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-16T15:28:34Z","title_canon_sha256":"a9ab500ec1eab30f3ac309c9aaef34fa08aae9a215452c1ee3ace63fbc72a9f1"},"schema_version":"1.0","source":{"id":"2502.11163","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11163","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11163v3","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11163","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"pith_short_12","alias_value":"ST42HZWGVGU3","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"pith_short_16","alias_value":"ST42HZWGVGU367NK","created_at":"2026-07-05T12:06:05Z"},{"alias_kind":"pith_short_8","alias_value":"ST42HZWG","created_at":"2026-07-05T12:06:05Z"}],"graph_snapshots":[{"event_id":"sha256:03f6e87fdc01150124181101151f11656b325b08221998f2b75ad1bb6dabff1c","target":"graph","created_at":"2026-07-05T12:06:05Z","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/2502.11163/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, VLMs still show regional biases in this task. To systematically evaluate these issues, we introduce a benchmark consisting of 1,200 images paired with detailed geographic metadata. Evaluating four VLMs, we find that while these models demonstrate the ability to recognize geographic information from images, achieving up to 53.8% accuracy in city prediction, they exhibit significant biases. Specifically, performance is substantially higher","authors_text":"Jen-tse Huang, Jieyu Zhao, Jingyuan Huang, Wenxuan Wang, Xiaoyuan Liu, Ziyi Liu","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-16T15:28:34Z","title":"AI Sees Your Location, But With A Bias Toward The Wealthy World"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11163","kind":"arxiv","version":3},"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":"sha256:24ca8fee99e6145857306d1092858a47c90ecf905e23de2d4840f3c82d564714","target":"record","created_at":"2026-07-05T12:06:05Z","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":"d4aa19be52317131aed0b2f4cafb874f1fea5d57867c38fcc1ed2b53e7d4ff19","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-16T15:28:34Z","title_canon_sha256":"a9ab500ec1eab30f3ac309c9aaef34fa08aae9a215452c1ee3ace63fbc72a9f1"},"schema_version":"1.0","source":{"id":"2502.11163","kind":"arxiv","version":3}},"canonical_sha256":"94f9a3e6c6a9a9bf7daa49c4c85f984d00fbd426479360cf7c0d27343a5d18f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94f9a3e6c6a9a9bf7daa49c4c85f984d00fbd426479360cf7c0d27343a5d18f2","first_computed_at":"2026-07-05T12:06:05.926512Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:06:05.926512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mc7WLDQew5RYrw0Ke3XSMfBCafG+7WGCOA7c8T5sZR0FJXyrDFbJrE6dh22l946vwLAq80fzDvDe4JVhG/yRBA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:06:05.927008Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.11163","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:24ca8fee99e6145857306d1092858a47c90ecf905e23de2d4840f3c82d564714","sha256:03f6e87fdc01150124181101151f11656b325b08221998f2b75ad1bb6dabff1c"],"state_sha256":"338dd4c4690647bb6022554c6434bb2086d06077b18e1c68c4a1c89b429cae56"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AwQIkL6q598ndMDmihS9z6ZWeLO9pWQMa9xzmqPVBx3/QwfYT2Vx8SyjrSx1VwRVtW1MIGHWNZzXoSalzd2PCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T12:26:37.692949Z","bundle_sha256":"cd2219016735f7ebba53154dc7399cd77fa361bbce2110b2d7cb73b11c764a67"}}