{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RTYZIH6IISHER4XLSF3AKYTDFM","short_pith_number":"pith:RTYZIH6I","canonical_record":{"source":{"id":"2308.01684","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-03T10:52:52Z","cross_cats_sorted":[],"title_canon_sha256":"afcacf980f51b2f017b66c2824012afd7252c204099dc7eec47ac2c052b32ab2","abstract_canon_sha256":"162d4e38f4d5fba583afe4315d44cef05942f8992eb14412b8fc7c5c9faa414d"},"schema_version":"1.0"},"canonical_sha256":"8cf1941fc8448e48f2eb91760562632b095188f6ec1b4951db6c2af8ab02a459","source":{"kind":"arxiv","id":"2308.01684","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01684","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01684v2","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01684","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"pith_short_12","alias_value":"RTYZIH6IISHE","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"pith_short_16","alias_value":"RTYZIH6IISHER4XL","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"pith_short_8","alias_value":"RTYZIH6I","created_at":"2026-07-05T07:04:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RTYZIH6IISHER4XLSF3AKYTDFM","target":"record","payload":{"canonical_record":{"source":{"id":"2308.01684","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-03T10:52:52Z","cross_cats_sorted":[],"title_canon_sha256":"afcacf980f51b2f017b66c2824012afd7252c204099dc7eec47ac2c052b32ab2","abstract_canon_sha256":"162d4e38f4d5fba583afe4315d44cef05942f8992eb14412b8fc7c5c9faa414d"},"schema_version":"1.0"},"canonical_sha256":"8cf1941fc8448e48f2eb91760562632b095188f6ec1b4951db6c2af8ab02a459","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:00.289704Z","signature_b64":"acnsydcqIzmrMajs/HR4VI3rmcMHH1fGAUDNHBxj6YVfz21ILonw1NDwdKj7f86wreWfCmhSThDTEgB7KHe/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cf1941fc8448e48f2eb91760562632b095188f6ec1b4951db6c2af8ab02a459","last_reissued_at":"2026-07-05T07:04:00.289250Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:00.289250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.01684","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:04:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QpL44i5F0i4ldT6PWkKOeaVZnQMPl/eLAhlx6ttin6aI5qF4FWCvRkUvzQif6PLTk8zSSFiEh6pzv05cbG8HAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:55:34.187264Z"},"content_sha256":"bc46c9ff31c020a216e682bbb14d37a889a07c2ae0cb53f6cd2e8d96c4d04f40","schema_version":"1.0","event_id":"sha256:bc46c9ff31c020a216e682bbb14d37a889a07c2ae0cb53f6cd2e8d96c4d04f40"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RTYZIH6IISHER4XLSF3AKYTDFM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bolei Ma, David R\\\"ugamer, Ercong Nie, Han Yang, Zheyu Zhang","submitted_at":"2023-08-03T10:52:52Z","abstract_excerpt":"Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability. This ability could be applied to building babylike models, i.e. models at small scales, improving training efficiency. In this paper, we propose a \"CoThought\" pipeline, which efficiently trains smaller \"baby\" language models (BabyLMs) by leveraging the Chain of Thought prompting of LLMs. Our pipeline restructures a dataset of less than 100M in size using GPT-3.5-turbo, transforming it into task-oriented, human-readable t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01684","kind":"arxiv","version":2},"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/2308.01684/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":"2026-07-05T07:04:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6dgFOREuxywKfEURCo1WPWC4kwjQstCe/yo+bzl79ss7KME0zyIKEuyqVSz1BFIRqJyjHT6XBKPWH0+LNpETBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:55:34.187646Z"},"content_sha256":"38f7ba7eb919d8e65a7f8f5b5b33df4228c500c29efec808008449e8a72e5588","schema_version":"1.0","event_id":"sha256:38f7ba7eb919d8e65a7f8f5b5b33df4228c500c29efec808008449e8a72e5588"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RTYZIH6IISHER4XLSF3AKYTDFM/bundle.json","state_url":"https://pith.science/pith/RTYZIH6IISHER4XLSF3AKYTDFM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RTYZIH6IISHER4XLSF3AKYTDFM/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":"2026-07-06T16:55:34Z","links":{"resolver":"https://pith.science/pith/RTYZIH6IISHER4XLSF3AKYTDFM","bundle":"https://pith.science/pith/RTYZIH6IISHER4XLSF3AKYTDFM/bundle.json","state":"https://pith.science/pith/RTYZIH6IISHER4XLSF3AKYTDFM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RTYZIH6IISHER4XLSF3AKYTDFM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RTYZIH6IISHER4XLSF3AKYTDFM","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":"162d4e38f4d5fba583afe4315d44cef05942f8992eb14412b8fc7c5c9faa414d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-03T10:52:52Z","title_canon_sha256":"afcacf980f51b2f017b66c2824012afd7252c204099dc7eec47ac2c052b32ab2"},"schema_version":"1.0","source":{"id":"2308.01684","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01684","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01684v2","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01684","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"pith_short_12","alias_value":"RTYZIH6IISHE","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"pith_short_16","alias_value":"RTYZIH6IISHER4XL","created_at":"2026-07-05T07:04:00Z"},{"alias_kind":"pith_short_8","alias_value":"RTYZIH6I","created_at":"2026-07-05T07:04:00Z"}],"graph_snapshots":[{"event_id":"sha256:38f7ba7eb919d8e65a7f8f5b5b33df4228c500c29efec808008449e8a72e5588","target":"graph","created_at":"2026-07-05T07:04:00Z","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/2308.01684/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability. This ability could be applied to building babylike models, i.e. models at small scales, improving training efficiency. In this paper, we propose a \"CoThought\" pipeline, which efficiently trains smaller \"baby\" language models (BabyLMs) by leveraging the Chain of Thought prompting of LLMs. Our pipeline restructures a dataset of less than 100M in size using GPT-3.5-turbo, transforming it into task-oriented, human-readable t","authors_text":"Bolei Ma, David R\\\"ugamer, Ercong Nie, Han Yang, Zheyu Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-03T10:52:52Z","title":"Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01684","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:bc46c9ff31c020a216e682bbb14d37a889a07c2ae0cb53f6cd2e8d96c4d04f40","target":"record","created_at":"2026-07-05T07:04:00Z","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":"162d4e38f4d5fba583afe4315d44cef05942f8992eb14412b8fc7c5c9faa414d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-08-03T10:52:52Z","title_canon_sha256":"afcacf980f51b2f017b66c2824012afd7252c204099dc7eec47ac2c052b32ab2"},"schema_version":"1.0","source":{"id":"2308.01684","kind":"arxiv","version":2}},"canonical_sha256":"8cf1941fc8448e48f2eb91760562632b095188f6ec1b4951db6c2af8ab02a459","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8cf1941fc8448e48f2eb91760562632b095188f6ec1b4951db6c2af8ab02a459","first_computed_at":"2026-07-05T07:04:00.289250Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:04:00.289250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"acnsydcqIzmrMajs/HR4VI3rmcMHH1fGAUDNHBxj6YVfz21ILonw1NDwdKj7f86wreWfCmhSThDTEgB7KHe/DA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:04:00.289704Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.01684","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc46c9ff31c020a216e682bbb14d37a889a07c2ae0cb53f6cd2e8d96c4d04f40","sha256:38f7ba7eb919d8e65a7f8f5b5b33df4228c500c29efec808008449e8a72e5588"],"state_sha256":"fe96b43f6f8516f3863cd5fdfadb8f239c78f6b72f0ded5fe1e55a7cff8d296f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wt326x1r9QIxSGOD1617LsxQv2QuCu1Wt2FzYVTsC6Z71I7BWallxWD15WOOzTBAol7vDAgCmW2PDfxxUzbIBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T16:55:34.189631Z","bundle_sha256":"b38643143d2bcf213d2b181d06e1f3dd80386bc93e6411b83ed1e74b1ac1d8d0"}}