{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BDEUCIDKOGOTYUAD7WUPRXFHOH","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":"70c46c99e6793bea39d11b113b9d4e8cd5497fa3d60421181c55913a14359caa","cross_cats_sorted":["cs.AI","cs.CL","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T16:00:51Z","title_canon_sha256":"f270359a9926b8cadbf02133cb4cd830c159433c03c078e26fdd75e5f60ca57c"},"schema_version":"1.0","source":{"id":"2507.10419","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.10419","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2507.10419v3","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.10419","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"BDEUCIDKOGOT","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"BDEUCIDKOGOTYUAD","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"BDEUCIDK","created_at":"2026-06-03T01:05:44Z"}],"graph_snapshots":[{"event_id":"sha256:edb3764b3ba479c97a2d314335f938b801a6156c2769ab29c4718ffd4a442a6d","target":"graph","created_at":"2026-06-03T01:05:44Z","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/2507.10419/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose LoRA-MCL, a training scheme that extends next-token prediction in language models with a method designed to decode diverse, plausible sentence continuations at inference time. Traditional language modeling is an intrinsically ill-posed problem: given a context, multiple futures may be equally plausible. Our approach leverages Multiple Choice Learning (MCL) and the winner-takes-all loss to efficiently handle ambiguity through Low-Rank Adaptation. We provide a theoretical interpretation of applying MCL to language modeling, assuming the data is generated from a mixture of distribution","authors_text":"Andrei Bursuc, Ga\\\"el Richard, Hugo Malard, Mathieu Fontaine, Patrick P\\'erez, Slim Essid, Victor Letzelter","cross_cats":["cs.AI","cs.CL","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T16:00:51Z","title":"Multiple Choice Learning of Low-Rank Adapters for Language Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.10419","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":"sha256:229faa696241d96a288c376119bd217eba910a23217968afd393745301eb5b06","target":"record","created_at":"2026-06-03T01:05:44Z","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":"70c46c99e6793bea39d11b113b9d4e8cd5497fa3d60421181c55913a14359caa","cross_cats_sorted":["cs.AI","cs.CL","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-14T16:00:51Z","title_canon_sha256":"f270359a9926b8cadbf02133cb4cd830c159433c03c078e26fdd75e5f60ca57c"},"schema_version":"1.0","source":{"id":"2507.10419","kind":"arxiv","version":3}},"canonical_sha256":"08c941206a719d3c5003fda8f8dca771d963fc6c3142a4e0419349a53afd505c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"08c941206a719d3c5003fda8f8dca771d963fc6c3142a4e0419349a53afd505c","first_computed_at":"2026-06-03T01:05:44.351301Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:44.351301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bU5QFpmCB4RsvlMpS2F8AJJNJ62Y9OgMuSkCOAiPzM4N4Pr+aiDoOP00EgWR8p1GkWKo2W2gJI/vxcwTbCnpCw==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:44.351797Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.10419","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:229faa696241d96a288c376119bd217eba910a23217968afd393745301eb5b06","sha256:edb3764b3ba479c97a2d314335f938b801a6156c2769ab29c4718ffd4a442a6d"],"state_sha256":"8fc2c01ab13d01b6cc5a4309f3755e0a4097b2b3a1317d2804aff045e0e0b2b1"}