{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PMIJFM6KJDOL6BKKAKDB4M6KCV","short_pith_number":"pith:PMIJFM6K","canonical_record":{"source":{"id":"2608.13277","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-08-13T14:13:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5c31e59a5deda62866b7a86c18d2a354bcbc732457f005ee1c0daa87edde40c3","abstract_canon_sha256":"5928e0a5b0263697a4cf68c4c27e5281b19df8aaeef7d33cff1c055889b6cb17"},"schema_version":"1.0"},"canonical_sha256":"7b1092b3ca48dcbf054a02861e33ca155ec497e10883f61ebe0a4353e2a80bab","source":{"kind":"arxiv","id":"2608.13277","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.13277","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"arxiv_version","alias_value":"2608.13277v1","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.13277","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"pith_short_12","alias_value":"PMIJFM6KJDOL","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"pith_short_16","alias_value":"PMIJFM6KJDOL6BKK","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"pith_short_8","alias_value":"PMIJFM6K","created_at":"2026-08-14T01:00:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PMIJFM6KJDOL6BKKAKDB4M6KCV","target":"record","payload":{"canonical_record":{"source":{"id":"2608.13277","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-08-13T14:13:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5c31e59a5deda62866b7a86c18d2a354bcbc732457f005ee1c0daa87edde40c3","abstract_canon_sha256":"5928e0a5b0263697a4cf68c4c27e5281b19df8aaeef7d33cff1c055889b6cb17"},"schema_version":"1.0"},"canonical_sha256":"7b1092b3ca48dcbf054a02861e33ca155ec497e10883f61ebe0a4353e2a80bab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-14T01:00:50.258216Z","signature_b64":"nqhCvQSt5oRqJlfM3oVvz70cdyy14Se5FwwyT776v2dg52FqgfeMI7R3axepVpw3ugNC8Vf0rQ/+iRHA7mTlCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b1092b3ca48dcbf054a02861e33ca155ec497e10883f61ebe0a4353e2a80bab","last_reissued_at":"2026-08-14T01:00:50.256178Z","signature_status":"signed_v1","first_computed_at":"2026-08-14T01:00:50.256178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.13277","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":"2026-08-14T01:00:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CQQGsH3/G19wli+cKkVW5nI3T1PC7vviA81yqNKK01zHbJ4Pstq1FbFaSBjttBr1P7D6rshW3tP2MIY7+xWnDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T16:07:30.963970Z"},"content_sha256":"95bf132b75954ebb0039e553643a3bdc80756ec74b098731c73fc32fc2c054f7","schema_version":"1.0","event_id":"sha256:95bf132b75954ebb0039e553643a3bdc80756ec74b098731c73fc32fc2c054f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PMIJFM6KJDOL6BKKAKDB4M6KCV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Keith Rush, Lucio Dery, Mohammed Sabry, Sean Augenstein","submitted_at":"2026-08-13T14:13:46Z","abstract_excerpt":"We ask whether language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model. We introduce Mixture of Training (MoT), a scaffolded modular pre-training procedure that partitions a target Transformer into contiguous layer blocks, trains each block inside a frozen pretrained aligner scaffold, and then recomposes the trained blocks with an optional short end-to-end adaptation pass. On a 1.3B-parameter Gemma-style model trained on C4, MoT provides a small-scale proof of mechanism: independently trained depth slice"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.13277","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/2608.13277/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":"2026-08-14T01:00:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1sgUSGd8//S/GuHJQMXHjfxso0y1CdvMNbCBieL4UQtnJk22ChiPcf1o53FWHBatTeJutwFV0+UhyV7bRjDqBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T16:07:30.964473Z"},"content_sha256":"5a4cb87904a8050588ecabcf9cb1459ae7b09e283b0a4c73c9802bd93a2f909b","schema_version":"1.0","event_id":"sha256:5a4cb87904a8050588ecabcf9cb1459ae7b09e283b0a4c73c9802bd93a2f909b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV/bundle.json","state_url":"https://pith.science/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV/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":"2026-08-22T16:07:30Z","links":{"resolver":"https://pith.science/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV","bundle":"https://pith.science/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV/bundle.json","state":"https://pith.science/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PMIJFM6KJDOL6BKKAKDB4M6KCV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PMIJFM6KJDOL6BKKAKDB4M6KCV","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":"5928e0a5b0263697a4cf68c4c27e5281b19df8aaeef7d33cff1c055889b6cb17","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-08-13T14:13:46Z","title_canon_sha256":"5c31e59a5deda62866b7a86c18d2a354bcbc732457f005ee1c0daa87edde40c3"},"schema_version":"1.0","source":{"id":"2608.13277","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.13277","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"arxiv_version","alias_value":"2608.13277v1","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.13277","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"pith_short_12","alias_value":"PMIJFM6KJDOL","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"pith_short_16","alias_value":"PMIJFM6KJDOL6BKK","created_at":"2026-08-14T01:00:50Z"},{"alias_kind":"pith_short_8","alias_value":"PMIJFM6K","created_at":"2026-08-14T01:00:50Z"}],"graph_snapshots":[{"event_id":"sha256:5a4cb87904a8050588ecabcf9cb1459ae7b09e283b0a4c73c9802bd93a2f909b","target":"graph","created_at":"2026-08-14T01:00:50Z","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/2608.13277/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We ask whether language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model. We introduce Mixture of Training (MoT), a scaffolded modular pre-training procedure that partitions a target Transformer into contiguous layer blocks, trains each block inside a frozen pretrained aligner scaffold, and then recomposes the trained blocks with an optional short end-to-end adaptation pass. On a 1.3B-parameter Gemma-style model trained on C4, MoT provides a small-scale proof of mechanism: independently trained depth slice","authors_text":"Keith Rush, Lucio Dery, Mohammed Sabry, Sean Augenstein","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-08-13T14:13:46Z","title":"Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.13277","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":"sha256:95bf132b75954ebb0039e553643a3bdc80756ec74b098731c73fc32fc2c054f7","target":"record","created_at":"2026-08-14T01:00:50Z","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":"5928e0a5b0263697a4cf68c4c27e5281b19df8aaeef7d33cff1c055889b6cb17","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-08-13T14:13:46Z","title_canon_sha256":"5c31e59a5deda62866b7a86c18d2a354bcbc732457f005ee1c0daa87edde40c3"},"schema_version":"1.0","source":{"id":"2608.13277","kind":"arxiv","version":1}},"canonical_sha256":"7b1092b3ca48dcbf054a02861e33ca155ec497e10883f61ebe0a4353e2a80bab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7b1092b3ca48dcbf054a02861e33ca155ec497e10883f61ebe0a4353e2a80bab","first_computed_at":"2026-08-14T01:00:50.256178Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-14T01:00:50.256178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nqhCvQSt5oRqJlfM3oVvz70cdyy14Se5FwwyT776v2dg52FqgfeMI7R3axepVpw3ugNC8Vf0rQ/+iRHA7mTlCg==","signature_status":"signed_v1","signed_at":"2026-08-14T01:00:50.258216Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.13277","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95bf132b75954ebb0039e553643a3bdc80756ec74b098731c73fc32fc2c054f7","sha256:5a4cb87904a8050588ecabcf9cb1459ae7b09e283b0a4c73c9802bd93a2f909b"],"state_sha256":"f077a6d634c72bd2f7a0d7f6feeb4993c7fcaffd7fc693e69bfad4874b36150f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y8wzUuE3Cp3+W/wt8czcSGNvdTl3m4JWRBnfNxRbHW/uBpPtvoRlfApEOobelg8dq0hjFhF0evJLVExM980VCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T16:07:30.968227Z","bundle_sha256":"6385d4da27594fe467cf5e78b446feb45b5cb9aad611534f787065b3f0569bba"}}