{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:F2CZHFXK6O2B2I7TIBPU4NGAUI","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":"94a512d9769d71c544b9e15d41cc82e1269c0b891389d9308d7b5c918a9293f4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-23T22:09:58Z","title_canon_sha256":"b9f7694ed4ec545612a476b606a66df0889f37d53e3163e419b1c52c5c8abe8e"},"schema_version":"1.0","source":{"id":"2411.15661","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.15661","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"arxiv_version","alias_value":"2411.15661v2","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15661","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_12","alias_value":"F2CZHFXK6O2B","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_16","alias_value":"F2CZHFXK6O2B2I7T","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_8","alias_value":"F2CZHFXK","created_at":"2026-07-05T10:14:07Z"}],"graph_snapshots":[{"event_id":"sha256:21a63906ed4fe108f887050ad45b066d06b3a7228dcb3a79de49c25a741ddbf8","target":"graph","created_at":"2026-07-05T10:14:07Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.15661/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autoregressive language models like GPT aim to predict next tokens, while autoencoding models such as BERT are trained on tasks such as predicting masked tokens. We train a decoder-only architecture for predicting the second to last token for a sequence of tokens. Our approach yields higher computational training efficiency than BERT-style models by employing a structured deterministic approach to masking tokens. We use our model to improve the next token predictions of a standard GPT by combining both predictions in a ``generate-then-refine'' approach. We demonstrate on different variants of ","authors_text":"Johannes Schneider","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-23T22:09:58Z","title":"Improving Next Tokens via Second-to-Last Predictions with Generate and Refine"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15661","kind":"arxiv","version":2},"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":"sha256:7d26ff3dd0614bb79f1bd7dc911d2bb19b14adffd63ea5a39f72754e1edd49db","target":"record","created_at":"2026-07-05T10:14:07Z","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":"94a512d9769d71c544b9e15d41cc82e1269c0b891389d9308d7b5c918a9293f4","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-23T22:09:58Z","title_canon_sha256":"b9f7694ed4ec545612a476b606a66df0889f37d53e3163e419b1c52c5c8abe8e"},"schema_version":"1.0","source":{"id":"2411.15661","kind":"arxiv","version":2}},"canonical_sha256":"2e859396eaf3b41d23f3405f4e34c0a227583fda214785942689eb65607b5468","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e859396eaf3b41d23f3405f4e34c0a227583fda214785942689eb65607b5468","first_computed_at":"2026-07-05T10:14:07.155323Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:14:07.155323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HOuh+jUT8V7nY8zFdCf+wmD0xVMTip9s1lqj7CbG+PaKc8lzlEnXQ5+t9jwKzHcX0dCVMCJwuTdlkoZbWj1dCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:14:07.155800Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.15661","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7d26ff3dd0614bb79f1bd7dc911d2bb19b14adffd63ea5a39f72754e1edd49db","sha256:21a63906ed4fe108f887050ad45b066d06b3a7228dcb3a79de49c25a741ddbf8"],"state_sha256":"3080e899097b9bd6abac62d7ff18a9e418ea08ef5c54ab8467bb8d1a4ccf24e7"}