{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:UGNUBCGAXLITSYUC23RU7QSBK2","short_pith_number":"pith:UGNUBCGA","canonical_record":{"source":{"id":"2508.09654","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T09:37:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7b738a66610bcf045412c803f5086b30936c41487925a5e8f63022b083fabaae","abstract_canon_sha256":"d1e18304e4c8ce805c8fe47669a1bc2cca06014ee07ef719304bc5b6a163657c"},"schema_version":"1.0"},"canonical_sha256":"a19b4088c0bad1396282d6e34fc24156bc238a803c3df6e5444d822f74d6c1ce","source":{"kind":"arxiv","id":"2508.09654","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09654","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09654v1","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09654","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"pith_short_12","alias_value":"UGNUBCGAXLIT","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"pith_short_16","alias_value":"UGNUBCGAXLITSYUC","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"pith_short_8","alias_value":"UGNUBCGA","created_at":"2026-07-05T11:53:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:UGNUBCGAXLITSYUC23RU7QSBK2","target":"record","payload":{"canonical_record":{"source":{"id":"2508.09654","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T09:37:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7b738a66610bcf045412c803f5086b30936c41487925a5e8f63022b083fabaae","abstract_canon_sha256":"d1e18304e4c8ce805c8fe47669a1bc2cca06014ee07ef719304bc5b6a163657c"},"schema_version":"1.0"},"canonical_sha256":"a19b4088c0bad1396282d6e34fc24156bc238a803c3df6e5444d822f74d6c1ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:19.197326Z","signature_b64":"P+/6UrVPAar0r9AmKboouoz8Z2/7pwY15RyitaRw/u1tMsm9to7gyWV97Aqb7w+NVVp8qqpd0b1NSYE2oGvkBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a19b4088c0bad1396282d6e34fc24156bc238a803c3df6e5444d822f74d6c1ce","last_reissued_at":"2026-07-05T11:53:19.196809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:19.196809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.09654","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":"2026-07-05T11:53:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"98WWiwO5eIAqSvTtx3FyQ+X2ano4BTIOln/H0TJJWpNmCE3hUeT7J6z0sn+U7h42aedacre6v91439pkHntkBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:12:17.781514Z"},"content_sha256":"709461fb5d265b61eabca736de594d80a157c63e3f2e03bed885ec232d06321d","schema_version":"1.0","event_id":"sha256:709461fb5d265b61eabca736de594d80a157c63e3f2e03bed885ec232d06321d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:UGNUBCGAXLITSYUC23RU7QSBK2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Diversity in Language Models: When Temperature Fails, Change the Loss","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Alexandre Allauzen, Alexandre Verine, Benjamin Negrevergne, Florian Le Bronnec, Kunhao Zheng, Yann Chevaleyre","submitted_at":"2025-08-13T09:37:53Z","abstract_excerpt":"Increasing diversity in language models is a challenging yet essential objective. A common approach is to raise the decoding temperature. In this work, we investigate this approach through a simplistic yet common case to provide insights into why decreasing temperature can improve quality (Precision), while increasing it often fails to boost coverage (Recall). Our analysis reveals that for a model to be effectively tunable through temperature adjustments, it must be trained toward coverage. To address this, we propose rethinking loss functions in language models by leveraging the Precision-Rec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09654","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/2508.09654/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":"2026-07-05T11:53:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NMc8N04T706GL8uB6yj9QgTelAbGfUc/XLVl6ieNQr9EIisb28TqM5OVl+/YzXbLfYdfDkkGLM5KwTbGAbG7CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T06:12:17.782148Z"},"content_sha256":"5e6dd33687ae1d11efe4b02e040e6efabe93ceea83ea677c1f4835c78e4eb0bd","schema_version":"1.0","event_id":"sha256:5e6dd33687ae1d11efe4b02e040e6efabe93ceea83ea677c1f4835c78e4eb0bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UGNUBCGAXLITSYUC23RU7QSBK2/bundle.json","state_url":"https://pith.science/pith/UGNUBCGAXLITSYUC23RU7QSBK2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UGNUBCGAXLITSYUC23RU7QSBK2/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":"2026-08-04T06:12:17Z","links":{"resolver":"https://pith.science/pith/UGNUBCGAXLITSYUC23RU7QSBK2","bundle":"https://pith.science/pith/UGNUBCGAXLITSYUC23RU7QSBK2/bundle.json","state":"https://pith.science/pith/UGNUBCGAXLITSYUC23RU7QSBK2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UGNUBCGAXLITSYUC23RU7QSBK2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UGNUBCGAXLITSYUC23RU7QSBK2","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":"d1e18304e4c8ce805c8fe47669a1bc2cca06014ee07ef719304bc5b6a163657c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T09:37:53Z","title_canon_sha256":"7b738a66610bcf045412c803f5086b30936c41487925a5e8f63022b083fabaae"},"schema_version":"1.0","source":{"id":"2508.09654","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09654","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09654v1","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09654","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"pith_short_12","alias_value":"UGNUBCGAXLIT","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"pith_short_16","alias_value":"UGNUBCGAXLITSYUC","created_at":"2026-07-05T11:53:19Z"},{"alias_kind":"pith_short_8","alias_value":"UGNUBCGA","created_at":"2026-07-05T11:53:19Z"}],"graph_snapshots":[{"event_id":"sha256:5e6dd33687ae1d11efe4b02e040e6efabe93ceea83ea677c1f4835c78e4eb0bd","target":"graph","created_at":"2026-07-05T11:53:19Z","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/2508.09654/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Increasing diversity in language models is a challenging yet essential objective. A common approach is to raise the decoding temperature. In this work, we investigate this approach through a simplistic yet common case to provide insights into why decreasing temperature can improve quality (Precision), while increasing it often fails to boost coverage (Recall). Our analysis reveals that for a model to be effectively tunable through temperature adjustments, it must be trained toward coverage. To address this, we propose rethinking loss functions in language models by leveraging the Precision-Rec","authors_text":"Alexandre Allauzen, Alexandre Verine, Benjamin Negrevergne, Florian Le Bronnec, Kunhao Zheng, Yann Chevaleyre","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T09:37:53Z","title":"Improving Diversity in Language Models: When Temperature Fails, Change the Loss"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09654","kind":"arxiv","version":1},"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":"sha256:709461fb5d265b61eabca736de594d80a157c63e3f2e03bed885ec232d06321d","target":"record","created_at":"2026-07-05T11:53:19Z","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":"d1e18304e4c8ce805c8fe47669a1bc2cca06014ee07ef719304bc5b6a163657c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-13T09:37:53Z","title_canon_sha256":"7b738a66610bcf045412c803f5086b30936c41487925a5e8f63022b083fabaae"},"schema_version":"1.0","source":{"id":"2508.09654","kind":"arxiv","version":1}},"canonical_sha256":"a19b4088c0bad1396282d6e34fc24156bc238a803c3df6e5444d822f74d6c1ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a19b4088c0bad1396282d6e34fc24156bc238a803c3df6e5444d822f74d6c1ce","first_computed_at":"2026-07-05T11:53:19.196809Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:53:19.196809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P+/6UrVPAar0r9AmKboouoz8Z2/7pwY15RyitaRw/u1tMsm9to7gyWV97Aqb7w+NVVp8qqpd0b1NSYE2oGvkBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:53:19.197326Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.09654","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:709461fb5d265b61eabca736de594d80a157c63e3f2e03bed885ec232d06321d","sha256:5e6dd33687ae1d11efe4b02e040e6efabe93ceea83ea677c1f4835c78e4eb0bd"],"state_sha256":"5edfa707884551a4a550ffc9584cc218f3a99ce7c7de85a08461720e94e442b4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q0HlCunwaz8SEchCsmMIy0X9vKYQXv3Dos1MuIXSsXYLt7fKt4jqcX63Ao/dWw6ufCHSmRQ4Qq7qT6gGoER/Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T06:12:17.788802Z","bundle_sha256":"2661d8053949976ec06676f51ddcc5248d0a5a40c33f814d6f0b2bca28b31ba4"}}