{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VK3YHCC4FYB3EAF4TRJSBJRURL","short_pith_number":"pith:VK3YHCC4","schema_version":"1.0","canonical_sha256":"aab783885c2e03b200bc9c5320a6348ac89e603328ba08396f2d437b4cbbb22f","source":{"kind":"arxiv","id":"2407.11009","version":1},"attestation_state":"computed","paper":{"title":"CharED: Character-wise Ensemble Decoding for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"David Alvarez-Melis, Dmitriy Katz, Eva Tuecke, Kevin Gu, Mikhail Yurochkin, Raya Horesh","submitted_at":"2024-06-25T22:35:07Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable potential for problem solving, with open source models achieving increasingly impressive performance on benchmarks measuring areas from logical reasoning to mathematical ability. Ensembling models can further improve capabilities across a variety of domains. However, conventional methods of combining models at inference time such as shallow fusion necessitate a shared vocabulary and tokenization, and alternatives like fine-tuning for domain-specific performance are both time consuming and computationally expensive. We therefore present an infe"},"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":"2407.11009","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-25T22:35:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4198afdf5f595cac66a79d909605e48e410b29bfae97b77edd7a4e06396e73e4","abstract_canon_sha256":"ab35420e37984769d59b983bc237048411eeffabc3777bdf3065ff7559f37867"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:16.987347Z","signature_b64":"bz8V4R2dGJV4JrPP30CAQnsi5HpyK/Af9v/D+vWVtknjKK21q8gRtuc7f9GcA4uH1YhU3jzcMwk8MOPSq0ptBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aab783885c2e03b200bc9c5320a6348ac89e603328ba08396f2d437b4cbbb22f","last_reissued_at":"2026-07-05T08:44:16.986829Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:16.986829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CharED: Character-wise Ensemble Decoding for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"David Alvarez-Melis, Dmitriy Katz, Eva Tuecke, Kevin Gu, Mikhail Yurochkin, Raya Horesh","submitted_at":"2024-06-25T22:35:07Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable potential for problem solving, with open source models achieving increasingly impressive performance on benchmarks measuring areas from logical reasoning to mathematical ability. Ensembling models can further improve capabilities across a variety of domains. However, conventional methods of combining models at inference time such as shallow fusion necessitate a shared vocabulary and tokenization, and alternatives like fine-tuning for domain-specific performance are both time consuming and computationally expensive. We therefore present an infe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11009","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/2407.11009/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":"2407.11009","created_at":"2026-07-05T08:44:16.986894+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.11009v1","created_at":"2026-07-05T08:44:16.986894+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11009","created_at":"2026-07-05T08:44:16.986894+00:00"},{"alias_kind":"pith_short_12","alias_value":"VK3YHCC4FYB3","created_at":"2026-07-05T08:44:16.986894+00:00"},{"alias_kind":"pith_short_16","alias_value":"VK3YHCC4FYB3EAF4","created_at":"2026-07-05T08:44:16.986894+00:00"},{"alias_kind":"pith_short_8","alias_value":"VK3YHCC4","created_at":"2026-07-05T08:44:16.986894+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/VK3YHCC4FYB3EAF4TRJSBJRURL","json":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL.json","graph_json":"https://pith.science/api/pith-number/VK3YHCC4FYB3EAF4TRJSBJRURL/graph.json","events_json":"https://pith.science/api/pith-number/VK3YHCC4FYB3EAF4TRJSBJRURL/events.json","paper":"https://pith.science/paper/VK3YHCC4"},"agent_actions":{"view_html":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL","download_json":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL.json","view_paper":"https://pith.science/paper/VK3YHCC4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.11009&json=true","fetch_graph":"https://pith.science/api/pith-number/VK3YHCC4FYB3EAF4TRJSBJRURL/graph.json","fetch_events":"https://pith.science/api/pith-number/VK3YHCC4FYB3EAF4TRJSBJRURL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL/action/storage_attestation","attest_author":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL/action/author_attestation","sign_citation":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL/action/citation_signature","submit_replication":"https://pith.science/pith/VK3YHCC4FYB3EAF4TRJSBJRURL/action/replication_record"}},"created_at":"2026-07-05T08:44:16.986894+00:00","updated_at":"2026-07-05T08:44:16.986894+00:00"}