{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:RJWGEDU6KMOZXTCTILE4QYFPCA","short_pith_number":"pith:RJWGEDU6","canonical_record":{"source":{"id":"2605.14404","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T05:45:24Z","cross_cats_sorted":[],"title_canon_sha256":"a5784fa4ec03b091b35f02bdeff1969c42fc74e6070b7cdcb60aa7364316475a","abstract_canon_sha256":"565d8c4e9b283ddf40e9c1c72d03e2569bfdf6a7ba8433400dbd8d55c39a297e"},"schema_version":"1.0"},"canonical_sha256":"8a6c620e9e531d9bcc5342c9c860af10082c7cceb001d22140a292f05789b546","source":{"kind":"arxiv","id":"2605.14404","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14404","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14404v1","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14404","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"pith_short_12","alias_value":"RJWGEDU6KMOZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"RJWGEDU6KMOZXTCT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"RJWGEDU6","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:RJWGEDU6KMOZXTCTILE4QYFPCA","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14404","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T05:45:24Z","cross_cats_sorted":[],"title_canon_sha256":"a5784fa4ec03b091b35f02bdeff1969c42fc74e6070b7cdcb60aa7364316475a","abstract_canon_sha256":"565d8c4e9b283ddf40e9c1c72d03e2569bfdf6a7ba8433400dbd8d55c39a297e"},"schema_version":"1.0"},"canonical_sha256":"8a6c620e9e531d9bcc5342c9c860af10082c7cceb001d22140a292f05789b546","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:07.449034Z","signature_b64":"s17vM5/BBN9sUhcpjvipvU6VsSEKrHftQ74z508GMweomQSEOvIP6gT9AzTurMr0eqBASC5skaIqQ4HMBdePAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a6c620e9e531d9bcc5342c9c860af10082c7cceb001d22140a292f05789b546","last_reissued_at":"2026-05-17T23:39:07.448278Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:07.448278Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14404","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WnD5IrJMwI3vmQ3porVCvrmU88ukUejobL3MDe9KTaPFcRhjGE8hNbikE9m0/hLt7LwIpkqN6+yHuPvdabZhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:15:03.689543Z"},"content_sha256":"f60e49c71c657f72f5446ab9a932c4baab0b0403b3683eceb9acb17e052f3f7f","schema_version":"1.0","event_id":"sha256:f60e49c71c657f72f5446ab9a932c4baab0b0403b3683eceb9acb17e052f3f7f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:RJWGEDU6KMOZXTCTILE4QYFPCA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hyeonjin Kim, Kyomin Hwang, Nojun Kwak, Sangyeon Cho","submitted_at":"2026-05-14T05:45:24Z","abstract_excerpt":"While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII). For LLMs trained on multilingual corpora, Multilingual Machine Unlearning (MMU) aims to remove information across multiple languages. However, prior MMU evaluations fail to capture such cross-linguistic distribution of information, being largely limited to direct extensions of per-language evaluation protocols. To this end, we propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and th"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and the Knowledge Persistence Score (KPS). KSS measures the overall unlearning quality across multiple languages, while KPS more specifically aims to assess consistent removal of information among different language pairs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That KSS and KPS accurately capture cross-linguistic information distribution and validly measure unlearning quality, without requiring external validation against actual leakage rates or human judgments of forgetting.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"92516adf852d8f7d16073bb03e8a59e312e9b7ecc75259c6a5054dcee8465ded"},"source":{"id":"2605.14404","kind":"arxiv","version":1},"verdict":{"id":"26722ead-a3c2-416c-a6b6-83715332775e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:09:59.775135Z","strongest_claim":"We propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and the Knowledge Persistence Score (KPS). KSS measures the overall unlearning quality across multiple languages, while KPS more specifically aims to assess consistent removal of information among different language pairs.","one_line_summary":"New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That KSS and KPS accurately capture cross-linguistic information distribution and validly measure unlearning quality, without requiring external validation against actual leakage rates or human judgments of forgetting.","pith_extraction_headline":"Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs."},"references":{"count":55,"sample":[{"doi":"","year":1972,"title":"Aho and Jeffrey D","work_id":"b1f5cb43-a3c7-4ea0-85e7-9ccc9dfe1588","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1983,"title":"Publications Manual , year = \"1983\", publisher =","work_id":"aca2b566-99e0-4ebb-9c7a-a81219531259","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/322234.322243","year":1981,"title":"Chandra and Dexter C","work_id":"c3270592-bd69-4213-95e1-4aaf8312be9b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Scalable training of","work_id":"aef70eae-f816-4598-84ec-429a2c09f5fc","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1997,"title":"Dan Gusfield , title =. 1997","work_id":"852d89f5-1e7b-4296-b4f2-71e578b5e9f6","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":55,"snapshot_sha256":"30153cd5e858ba6f20a0faa854dfe4fcdaecfe792c6e34ccbcf2d8adf650070a","internal_anchors":12},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c3a1e10b01d23eaa759e25b6b35ddbbbe7f54ec5f43caeb02081f8b3da475965"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"26722ead-a3c2-416c-a6b6-83715332775e"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dlgXQ9/b5D9eeRSKC14gLgbhiaxItoq9JNt3Fgf5F3JOKHGGChhY7xTIVhVJwPKvgrMyLjIgSuaCJdmwFKcmCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:15:03.690091Z"},"content_sha256":"62c64ff155acce646bad414847db17752ad4e9c412f6b0bee6393d0eb3be7aa0","schema_version":"1.0","event_id":"sha256:62c64ff155acce646bad414847db17752ad4e9c412f6b0bee6393d0eb3be7aa0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RJWGEDU6KMOZXTCTILE4QYFPCA/bundle.json","state_url":"https://pith.science/pith/RJWGEDU6KMOZXTCTILE4QYFPCA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RJWGEDU6KMOZXTCTILE4QYFPCA/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":"2026-05-25T17:15:03Z","links":{"resolver":"https://pith.science/pith/RJWGEDU6KMOZXTCTILE4QYFPCA","bundle":"https://pith.science/pith/RJWGEDU6KMOZXTCTILE4QYFPCA/bundle.json","state":"https://pith.science/pith/RJWGEDU6KMOZXTCTILE4QYFPCA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RJWGEDU6KMOZXTCTILE4QYFPCA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RJWGEDU6KMOZXTCTILE4QYFPCA","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":"565d8c4e9b283ddf40e9c1c72d03e2569bfdf6a7ba8433400dbd8d55c39a297e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T05:45:24Z","title_canon_sha256":"a5784fa4ec03b091b35f02bdeff1969c42fc74e6070b7cdcb60aa7364316475a"},"schema_version":"1.0","source":{"id":"2605.14404","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14404","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14404v1","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14404","created_at":"2026-05-17T23:39:07Z"},{"alias_kind":"pith_short_12","alias_value":"RJWGEDU6KMOZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"RJWGEDU6KMOZXTCT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"RJWGEDU6","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:62c64ff155acce646bad414847db17752ad4e9c412f6b0bee6393d0eb3be7aa0","target":"graph","created_at":"2026-05-17T23:39:07Z","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":"We propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and the Knowledge Persistence Score (KPS). KSS measures the overall unlearning quality across multiple languages, while KPS more specifically aims to assess consistent removal of information among different language pairs."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That KSS and KPS accurately capture cross-linguistic information distribution and validly measure unlearning quality, without requiring external validation against actual leakage rates or human judgments of forgetting."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs."}],"snapshot_sha256":"92516adf852d8f7d16073bb03e8a59e312e9b7ecc75259c6a5054dcee8465ded"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c3a1e10b01d23eaa759e25b6b35ddbbbe7f54ec5f43caeb02081f8b3da475965"},"paper":{"abstract_excerpt":"While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII). For LLMs trained on multilingual corpora, Multilingual Machine Unlearning (MMU) aims to remove information across multiple languages. However, prior MMU evaluations fail to capture such cross-linguistic distribution of information, being largely limited to direct extensions of per-language evaluation protocols. To this end, we propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and th","authors_text":"Hyeonjin Kim, Kyomin Hwang, Nojun Kwak, Sangyeon Cho","cross_cats":[],"headline":"Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T05:45:24Z","title":"Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation"},"references":{"count":55,"internal_anchors":12,"resolved_work":55,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Aho and Jeffrey D","work_id":"b1f5cb43-a3c7-4ea0-85e7-9ccc9dfe1588","year":1972},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Publications Manual , year = \"1983\", publisher =","work_id":"aca2b566-99e0-4ebb-9c7a-a81219531259","year":1983},{"cited_arxiv_id":"","doi":"10.1145/322234.322243","is_internal_anchor":false,"ref_index":3,"title":"Chandra and Dexter C","work_id":"c3270592-bd69-4213-95e1-4aaf8312be9b","year":1981},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Scalable training of","work_id":"aef70eae-f816-4598-84ec-429a2c09f5fc","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Dan Gusfield , title =. 1997","work_id":"852d89f5-1e7b-4296-b4f2-71e578b5e9f6","year":1997}],"snapshot_sha256":"30153cd5e858ba6f20a0faa854dfe4fcdaecfe792c6e34ccbcf2d8adf650070a"},"source":{"id":"2605.14404","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T02:09:59.775135Z","id":"26722ead-a3c2-416c-a6b6-83715332775e","model_set":{"reader":"grok-4.3"},"one_line_summary":"New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs.","strongest_claim":"We propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and the Knowledge Persistence Score (KPS). KSS measures the overall unlearning quality across multiple languages, while KPS more specifically aims to assess consistent removal of information among different language pairs.","weakest_assumption":"That KSS and KPS accurately capture cross-linguistic information distribution and validly measure unlearning quality, without requiring external validation against actual leakage rates or human judgments of forgetting."}},"verdict_id":"26722ead-a3c2-416c-a6b6-83715332775e"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f60e49c71c657f72f5446ab9a932c4baab0b0403b3683eceb9acb17e052f3f7f","target":"record","created_at":"2026-05-17T23:39:07Z","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":"565d8c4e9b283ddf40e9c1c72d03e2569bfdf6a7ba8433400dbd8d55c39a297e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T05:45:24Z","title_canon_sha256":"a5784fa4ec03b091b35f02bdeff1969c42fc74e6070b7cdcb60aa7364316475a"},"schema_version":"1.0","source":{"id":"2605.14404","kind":"arxiv","version":1}},"canonical_sha256":"8a6c620e9e531d9bcc5342c9c860af10082c7cceb001d22140a292f05789b546","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a6c620e9e531d9bcc5342c9c860af10082c7cceb001d22140a292f05789b546","first_computed_at":"2026-05-17T23:39:07.448278Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:07.448278Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s17vM5/BBN9sUhcpjvipvU6VsSEKrHftQ74z508GMweomQSEOvIP6gT9AzTurMr0eqBASC5skaIqQ4HMBdePAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:07.449034Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14404","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f60e49c71c657f72f5446ab9a932c4baab0b0403b3683eceb9acb17e052f3f7f","sha256:62c64ff155acce646bad414847db17752ad4e9c412f6b0bee6393d0eb3be7aa0"],"state_sha256":"5b1b5230107bedc849975e535f07e0aa434e5596c5a6fa43b61a4cd438e87138"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6kw4g3k3WBbfZBx1xcFEeTuoj/znJWH2hN28nsCvHI/J9/DFJihb0SdY3Abl+sYKKwFxPodkFEDt8bTDNQgOBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:15:03.693238Z","bundle_sha256":"d664232b3bd3abf18cc1dad248ed8a1e89f902518d63a3f903096f7889bf9c30"}}