{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MHRPZI4BQ5JJPAGTI5MDASZTDL","short_pith_number":"pith:MHRPZI4B","canonical_record":{"source":{"id":"2602.04729","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-02-04T16:35:48Z","cross_cats_sorted":[],"title_canon_sha256":"92c75ac858f8ced9a429e054170242f26694220e1aa02ed7ce0e93f89e17d766","abstract_canon_sha256":"eb41e97cffd0dc1a53321398fe239cf0b7669dbfeae76ac5fddc77c50f375821"},"schema_version":"1.0"},"canonical_sha256":"61e2fca38187529780d34758304b331ad009d15fe91b61687eb23787870d264e","source":{"kind":"arxiv","id":"2602.04729","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.04729","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2602.04729v2","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.04729","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"MHRPZI4BQ5JJ","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"MHRPZI4BQ5JJPAGT","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"MHRPZI4B","created_at":"2026-05-29T01:05:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MHRPZI4BQ5JJPAGTI5MDASZTDL","target":"record","payload":{"canonical_record":{"source":{"id":"2602.04729","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-02-04T16:35:48Z","cross_cats_sorted":[],"title_canon_sha256":"92c75ac858f8ced9a429e054170242f26694220e1aa02ed7ce0e93f89e17d766","abstract_canon_sha256":"eb41e97cffd0dc1a53321398fe239cf0b7669dbfeae76ac5fddc77c50f375821"},"schema_version":"1.0"},"canonical_sha256":"61e2fca38187529780d34758304b331ad009d15fe91b61687eb23787870d264e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:03.364701Z","signature_b64":"cbF1Y87O5Vs/pYhSOzR55M9Plm1xltvYmzXwwYuWWw7SfNHtgrz3ptZrTKdbapM0+UvP2oa6kXzRzX8ibPTYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61e2fca38187529780d34758304b331ad009d15fe91b61687eb23787870d264e","last_reissued_at":"2026-05-29T01:05:03.363778Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:03.363778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.04729","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":"2026-05-29T01:05:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K+VKBpNuxALJOzfMTUC0H6I9y6zv8Tt/wUqCFrC2uKv4iqZAq9YtZMKOXfNQ7SDL23fUGAxGgFfCj+JifIDNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T06:15:31.793006Z"},"content_sha256":"5d43904aabf529916e2fe3e88f644ec86f497ca784b980f3b18a5d313426dfef","schema_version":"1.0","event_id":"sha256:5d43904aabf529916e2fe3e88f644ec86f497ca784b980f3b18a5d313426dfef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MHRPZI4BQ5JJPAGTI5MDASZTDL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"\"Be My Cheese?\": Cultural Nuance Benchmarking for Machine Translation in Multilingual LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Casey Ford, Cory Holland, Jennifer Barajas, Madison Van Doren, Riley VanMeter","submitted_at":"2026-02-04T16:35:48Z","abstract_excerpt":"We present a large-scale human evaluation benchmark for assessing cultural localisation in machine translation produced by state-of-the-art multilingual large language models (LLMs). Existing MT benchmarks emphasise token-level and grammatical accuracy, but often overlook the pragmatic and culturally grounded competencies required for real-world localisation. Building on a pilot study of 87 translations across 20 languages, we evaluate 7 multilingual LLMs across 15 target languages with 5 native-speaker raters per language. Each rater scored both full-text translations and segment-level instan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.04729","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/2602.04729/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":"2026-05-29T01:05:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bSvBugTdKjDt5PlUEd1DREtyr7dQr/R6SgF2pP2CZQjFTnsHRmoYy28fWrFIovRsB3VU02mxXN4s6dy27eAIDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T06:15:31.793390Z"},"content_sha256":"11847cf8cce9f4eee3b3ddd7d432a6f2108407054023a5286e45b975c8f07320","schema_version":"1.0","event_id":"sha256:11847cf8cce9f4eee3b3ddd7d432a6f2108407054023a5286e45b975c8f07320"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL/bundle.json","state_url":"https://pith.science/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL/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":"2026-06-20T06:15:31Z","links":{"resolver":"https://pith.science/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL","bundle":"https://pith.science/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL/bundle.json","state":"https://pith.science/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MHRPZI4BQ5JJPAGTI5MDASZTDL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MHRPZI4BQ5JJPAGTI5MDASZTDL","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":"eb41e97cffd0dc1a53321398fe239cf0b7669dbfeae76ac5fddc77c50f375821","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-02-04T16:35:48Z","title_canon_sha256":"92c75ac858f8ced9a429e054170242f26694220e1aa02ed7ce0e93f89e17d766"},"schema_version":"1.0","source":{"id":"2602.04729","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.04729","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2602.04729v2","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.04729","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"MHRPZI4BQ5JJ","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"MHRPZI4BQ5JJPAGT","created_at":"2026-05-29T01:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"MHRPZI4B","created_at":"2026-05-29T01:05:03Z"}],"graph_snapshots":[{"event_id":"sha256:11847cf8cce9f4eee3b3ddd7d432a6f2108407054023a5286e45b975c8f07320","target":"graph","created_at":"2026-05-29T01:05:03Z","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/2602.04729/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a large-scale human evaluation benchmark for assessing cultural localisation in machine translation produced by state-of-the-art multilingual large language models (LLMs). Existing MT benchmarks emphasise token-level and grammatical accuracy, but often overlook the pragmatic and culturally grounded competencies required for real-world localisation. Building on a pilot study of 87 translations across 20 languages, we evaluate 7 multilingual LLMs across 15 target languages with 5 native-speaker raters per language. Each rater scored both full-text translations and segment-level instan","authors_text":"Casey Ford, Cory Holland, Jennifer Barajas, Madison Van Doren, Riley VanMeter","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-02-04T16:35:48Z","title":"\"Be My Cheese?\": Cultural Nuance Benchmarking for Machine Translation in Multilingual LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.04729","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":"sha256:5d43904aabf529916e2fe3e88f644ec86f497ca784b980f3b18a5d313426dfef","target":"record","created_at":"2026-05-29T01:05:03Z","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":"eb41e97cffd0dc1a53321398fe239cf0b7669dbfeae76ac5fddc77c50f375821","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-02-04T16:35:48Z","title_canon_sha256":"92c75ac858f8ced9a429e054170242f26694220e1aa02ed7ce0e93f89e17d766"},"schema_version":"1.0","source":{"id":"2602.04729","kind":"arxiv","version":2}},"canonical_sha256":"61e2fca38187529780d34758304b331ad009d15fe91b61687eb23787870d264e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61e2fca38187529780d34758304b331ad009d15fe91b61687eb23787870d264e","first_computed_at":"2026-05-29T01:05:03.363778Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T01:05:03.363778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cbF1Y87O5Vs/pYhSOzR55M9Plm1xltvYmzXwwYuWWw7SfNHtgrz3ptZrTKdbapM0+UvP2oa6kXzRzX8ibPTYDQ==","signature_status":"signed_v1","signed_at":"2026-05-29T01:05:03.364701Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.04729","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d43904aabf529916e2fe3e88f644ec86f497ca784b980f3b18a5d313426dfef","sha256:11847cf8cce9f4eee3b3ddd7d432a6f2108407054023a5286e45b975c8f07320"],"state_sha256":"f8a148d72bc9973f85499b51afb822eec8328e91959ef0346faa49264a77beed"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MlQjlRMlhapfXJzSkjf7dUy1zuYKazjye41+SfwnzpwwGM+4v4Kv+fb2+/cY1W8eUHI2imP8BXdTJuDzLqRXDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T06:15:31.795361Z","bundle_sha256":"8d62311afc9536ca39942396e4c24677e4bde2f4834dbfbdd7f3819bc6deb744"}}