{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IDV7GTK75LIQMY3JJROKCUANCH","short_pith_number":"pith:IDV7GTK7","canonical_record":{"source":{"id":"2507.01887","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-02T16:57:01Z","cross_cats_sorted":[],"title_canon_sha256":"0471df53e9513b4f0094949af30dbebb8eea7e27d5a894f891c845b8a94d665f","abstract_canon_sha256":"9acf7b4d706ecf301060548958ccb08f351e8ddbaff18f26e49dd0dc97bef382"},"schema_version":"1.0"},"canonical_sha256":"40ebf34d5fead10663694c5ca1500d11e57b11909f4766b29b1a199e646fef2a","source":{"kind":"arxiv","id":"2507.01887","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.01887","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"arxiv_version","alias_value":"2507.01887v1","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01887","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"pith_short_12","alias_value":"IDV7GTK75LIQ","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"pith_short_16","alias_value":"IDV7GTK75LIQMY3J","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"pith_short_8","alias_value":"IDV7GTK7","created_at":"2026-07-05T11:30:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IDV7GTK75LIQMY3JJROKCUANCH","target":"record","payload":{"canonical_record":{"source":{"id":"2507.01887","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-02T16:57:01Z","cross_cats_sorted":[],"title_canon_sha256":"0471df53e9513b4f0094949af30dbebb8eea7e27d5a894f891c845b8a94d665f","abstract_canon_sha256":"9acf7b4d706ecf301060548958ccb08f351e8ddbaff18f26e49dd0dc97bef382"},"schema_version":"1.0"},"canonical_sha256":"40ebf34d5fead10663694c5ca1500d11e57b11909f4766b29b1a199e646fef2a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:57.544507Z","signature_b64":"GvEF9J8A0rwHnvlVn4XUZ4v6ckRXY+v2P6dRnql2G2CFfS7JjLQ2/J91crdIlBHfX7dtO2dhdq2bgRvGDQGeDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40ebf34d5fead10663694c5ca1500d11e57b11909f4766b29b1a199e646fef2a","last_reissued_at":"2026-07-05T11:30:57.543992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:57.543992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.01887","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":"2026-07-05T11:30:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7nEffjNUfArLU4bjpD+SK8Go65iJcvqYazVfUlnmqzaAkrywX8V2R1Kco06Ipt3uj28S1WdHfbDQ2czo51HQDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T04:50:56.099749Z"},"content_sha256":"b28d18066c967375706cd9ba3ff11eae0643620d2774c2142614b6fcea0cc1cf","schema_version":"1.0","event_id":"sha256:b28d18066c967375706cd9ba3ff11eae0643620d2774c2142614b6fcea0cc1cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IDV7GTK75LIQMY3JJROKCUANCH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chenghao Zhu, Dongyi Ding, Meiling Tao, Tiannan Wang, Wangchunshu Zhou, Yuchen Eleanor Jiang","submitted_at":"2025-07-02T16:57:01Z","abstract_excerpt":"Large language models (LLMs) excel at reasoning tasks requiring long thought sequences for planning, reflection, and refinement. However, their substantial model size and high computational demands are impractical for widespread deployment. Yet, small language models (SLMs) often struggle to learn long-form CoT reasoning due to their limited capacity, a phenomenon we refer to as the \"SLMs Learnability Gap\". To address this, we introduce \\textbf{Mi}d-\\textbf{Co}T \\textbf{T}eacher \\textbf{A}ssistant Distillation (MiCoTAl), a framework for improving long CoT distillation for SLMs. MiCoTA employs "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01887","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/2507.01887/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":"2026-07-05T11:30:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yBjAZUp0oI/KXZEleFb48yUc+q93iiqqbQFRSrjCk3Q0xYJl+PyvIPnt1PhwA8Vqo3DUwSpFKDYF7GNMHQS1CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T04:50:56.100290Z"},"content_sha256":"abb5f25ccc06e897b727fc12fbd7d6a9d21abf762acce00c8b5dbbd5307a7023","schema_version":"1.0","event_id":"sha256:abb5f25ccc06e897b727fc12fbd7d6a9d21abf762acce00c8b5dbbd5307a7023"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IDV7GTK75LIQMY3JJROKCUANCH/bundle.json","state_url":"https://pith.science/pith/IDV7GTK75LIQMY3JJROKCUANCH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IDV7GTK75LIQMY3JJROKCUANCH/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":"2026-08-07T04:50:56Z","links":{"resolver":"https://pith.science/pith/IDV7GTK75LIQMY3JJROKCUANCH","bundle":"https://pith.science/pith/IDV7GTK75LIQMY3JJROKCUANCH/bundle.json","state":"https://pith.science/pith/IDV7GTK75LIQMY3JJROKCUANCH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IDV7GTK75LIQMY3JJROKCUANCH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IDV7GTK75LIQMY3JJROKCUANCH","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":"9acf7b4d706ecf301060548958ccb08f351e8ddbaff18f26e49dd0dc97bef382","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-02T16:57:01Z","title_canon_sha256":"0471df53e9513b4f0094949af30dbebb8eea7e27d5a894f891c845b8a94d665f"},"schema_version":"1.0","source":{"id":"2507.01887","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.01887","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"arxiv_version","alias_value":"2507.01887v1","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01887","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"pith_short_12","alias_value":"IDV7GTK75LIQ","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"pith_short_16","alias_value":"IDV7GTK75LIQMY3J","created_at":"2026-07-05T11:30:57Z"},{"alias_kind":"pith_short_8","alias_value":"IDV7GTK7","created_at":"2026-07-05T11:30:57Z"}],"graph_snapshots":[{"event_id":"sha256:abb5f25ccc06e897b727fc12fbd7d6a9d21abf762acce00c8b5dbbd5307a7023","target":"graph","created_at":"2026-07-05T11:30:57Z","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/2507.01887/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) excel at reasoning tasks requiring long thought sequences for planning, reflection, and refinement. However, their substantial model size and high computational demands are impractical for widespread deployment. Yet, small language models (SLMs) often struggle to learn long-form CoT reasoning due to their limited capacity, a phenomenon we refer to as the \"SLMs Learnability Gap\". To address this, we introduce \\textbf{Mi}d-\\textbf{Co}T \\textbf{T}eacher \\textbf{A}ssistant Distillation (MiCoTAl), a framework for improving long CoT distillation for SLMs. MiCoTA employs ","authors_text":"Chenghao Zhu, Dongyi Ding, Meiling Tao, Tiannan Wang, Wangchunshu Zhou, Yuchen Eleanor Jiang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-02T16:57:01Z","title":"MiCoTA: Bridging the Learnability Gap with Intermediate CoT and Teacher Assistants"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01887","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:b28d18066c967375706cd9ba3ff11eae0643620d2774c2142614b6fcea0cc1cf","target":"record","created_at":"2026-07-05T11:30:57Z","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":"9acf7b4d706ecf301060548958ccb08f351e8ddbaff18f26e49dd0dc97bef382","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-07-02T16:57:01Z","title_canon_sha256":"0471df53e9513b4f0094949af30dbebb8eea7e27d5a894f891c845b8a94d665f"},"schema_version":"1.0","source":{"id":"2507.01887","kind":"arxiv","version":1}},"canonical_sha256":"40ebf34d5fead10663694c5ca1500d11e57b11909f4766b29b1a199e646fef2a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40ebf34d5fead10663694c5ca1500d11e57b11909f4766b29b1a199e646fef2a","first_computed_at":"2026-07-05T11:30:57.543992Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:30:57.543992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GvEF9J8A0rwHnvlVn4XUZ4v6ckRXY+v2P6dRnql2G2CFfS7JjLQ2/J91crdIlBHfX7dtO2dhdq2bgRvGDQGeDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:30:57.544507Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.01887","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b28d18066c967375706cd9ba3ff11eae0643620d2774c2142614b6fcea0cc1cf","sha256:abb5f25ccc06e897b727fc12fbd7d6a9d21abf762acce00c8b5dbbd5307a7023"],"state_sha256":"56445dd7ff5cbf2ba79d4ce99b0ebb3fb57d4971be5357596ea27ec138da70c8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AJwOpY3taU05inwsL4g8H+fxnt3UoVvl3pTJP7E5yO22mpQDcU3piyaA1yL+k70XbZiBTE24zq5EGyWgasA9Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T04:50:56.104020Z","bundle_sha256":"dc38863b8b3e2651c4929aaf7abb6db6eb32efabd06f5b32689617d4865cf03c"}}