{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QQXDKOGUPDFEZ7JRRSNRRPW4IE","short_pith_number":"pith:QQXDKOGU","schema_version":"1.0","canonical_sha256":"842e3538d478ca4cfd318c9b18bedc4127e66fd293009244e545add9a7fb5608","source":{"kind":"arxiv","id":"2606.21869","version":1},"attestation_state":"computed","paper":{"title":"The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alissa Shen, Jae-Won Chung, Joan Nwatu, Mosharaf Chowdhury, Naihao Deng, Rada Mihalcea, Yiming Feng, Yulong Chen","submitted_at":"2026-06-20T04:12:29Z","abstract_excerpt":"Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different languages remain poorly understood. We present a systematic study of inference energy consumption across languages with ML.Energy framework (Chung et al., 2026). We find striking disparities: energy consumption per output token varies by up to 8.3 times across languages, while total energy for a fixed set of requests varies by up to 179 times between the cheapest (English, 17.6 kJ) and the most expensive (Pashto, 3,147 kJ) languages. Our analysis shows "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.21869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-20T04:12:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"232f5de8bd4b1b45a3ef4792b27e3d89f972a34076f8a35ae6f660715c191afe","abstract_canon_sha256":"6dd4f40ed74936400225a8f1aac889ca290fb6ac162d375bf69d1cae68f65306"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:01.495694Z","signature_b64":"cuFb9RIXXnjZpxhHeaK1tqsf9lOpOA0y+BHvjcFmsTz2ZdbO5Ki/+GkqrzLghs5kcwYU755kFtL5/Uqp794iCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"842e3538d478ca4cfd318c9b18bedc4127e66fd293009244e545add9a7fb5608","last_reissued_at":"2026-06-23T02:13:01.495301Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:01.495301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alissa Shen, Jae-Won Chung, Joan Nwatu, Mosharaf Chowdhury, Naihao Deng, Rada Mihalcea, Yiming Feng, Yulong Chen","submitted_at":"2026-06-20T04:12:29Z","abstract_excerpt":"Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different languages remain poorly understood. We present a systematic study of inference energy consumption across languages with ML.Energy framework (Chung et al., 2026). We find striking disparities: energy consumption per output token varies by up to 8.3 times across languages, while total energy for a fixed set of requests varies by up to 179 times between the cheapest (English, 17.6 kJ) and the most expensive (Pashto, 3,147 kJ) languages. Our analysis shows "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21869","kind":"arxiv","version":1},"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/2606.21869/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.21869","created_at":"2026-06-23T02:13:01.495393+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21869v1","created_at":"2026-06-23T02:13:01.495393+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21869","created_at":"2026-06-23T02:13:01.495393+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQXDKOGUPDFE","created_at":"2026-06-23T02:13:01.495393+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQXDKOGUPDFEZ7JR","created_at":"2026-06-23T02:13:01.495393+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQXDKOGU","created_at":"2026-06-23T02:13:01.495393+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE","json":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE.json","graph_json":"https://pith.science/api/pith-number/QQXDKOGUPDFEZ7JRRSNRRPW4IE/graph.json","events_json":"https://pith.science/api/pith-number/QQXDKOGUPDFEZ7JRRSNRRPW4IE/events.json","paper":"https://pith.science/paper/QQXDKOGU"},"agent_actions":{"view_html":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE","download_json":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE.json","view_paper":"https://pith.science/paper/QQXDKOGU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21869&json=true","fetch_graph":"https://pith.science/api/pith-number/QQXDKOGUPDFEZ7JRRSNRRPW4IE/graph.json","fetch_events":"https://pith.science/api/pith-number/QQXDKOGUPDFEZ7JRRSNRRPW4IE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE/action/storage_attestation","attest_author":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE/action/author_attestation","sign_citation":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE/action/citation_signature","submit_replication":"https://pith.science/pith/QQXDKOGUPDFEZ7JRRSNRRPW4IE/action/replication_record"}},"created_at":"2026-06-23T02:13:01.495393+00:00","updated_at":"2026-06-23T02:13:01.495393+00:00"}