{"as_of":"2026-08-08T23:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:766162e9703ef5eb755305a50439547e553f1d732e92baf6f1647da1852948c1","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:39:04.514038Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.24181/citation-record","integrity":"/paper/2505.24181/integrity","json":"/paper/2505.24181/citation-record.json","paper":"/paper/2505.24181"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:57.588290Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:57.588290Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:cf4e66e83c68422127178d4400c11f4311e24c49c6b4f788aee91aeb98c25591","observation_id":"dde7aa3c-ed48-48dc-9334-dde0d9709418","resolution":{"observed_at":"2026-08-07T12:38:57.588290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03493","last_updated":"2022-10-07T12:28:21Z","snapshot_observed_at":"2026-07-06T14:01:50.333970Z","submitted_at":"2022-10-07T12:28:21Z","title":"Automatic Chain of Thought Prompting in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-07T12:38:57.689702Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:57.689702Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:37903ba4eaf0f71d16d5c31c37279d6a9b5377f79873b941ae3ee2de796ee62f","observation_id":"b5544222-9afa-4a04-8d7a-509298b1f0b5","resolution":{"observed_at":"2026-08-07T12:38:57.689702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12246","last_updated":"2024-07-21T08:01:00Z","snapshot_observed_at":"2026-08-06T00:07:59.786773Z","submitted_at":"2023-02-23T18:58:59Z","title":"Active Prompting with Chain-of-Thought for Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12246","snapshot_observed_at":"2026-08-07T12:38:57.802936Z","title":"Active prompting with chain-of-thought for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:57.802936Z"},"links":{"cited_paper":"/paper/2302.12246","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:b7082a377f40bf90f4e00b75bffa8d5f37d190c3a682314bdce27e425a60490c","observation_id":"c754ec0a-1e7d-4e88-a383-b57181211238","resolution":{"observed_at":"2026-08-07T12:38:57.802936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05171","last_updated":"2025-02-17T17:14:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-07T18:55:02Z","title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05171","snapshot_observed_at":"2026-08-07T12:38:57.951899Z","title":"Scaling up test-time compute with latent reasoning: A recurrent depth approach","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:57.951899Z"},"links":{"cited_paper":"/paper/2502.05171","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:ebf8797aedad78a3a7a8d5f9b375218d4a3075de1c7ad0249ac7754de240c1d0","observation_id":"cb28468c-7a37-4518-924f-54e70fca265f","resolution":{"observed_at":"2026-08-07T12:38:57.951899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:06.229590Z","title":"On the inductive bias of stacking towards improving reasoning","venue":null,"work_id":"b310846c-7006-45aa-8379-7b57f88a6dbf","year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.094665Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:30dd979c20181d913479267b213a1fef801fb3f4b879775fb7afed4a90c0978b","observation_id":"7d448e25-ed0b-457a-b9fe-a9ade83eb831","resolution":{"observed_at":"2026-08-07T12:39:06.395439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12214","last_updated":"2025-02-17T04:37:22Z","snapshot_observed_at":"2026-08-07T18:13:46.126284Z","submitted_at":"2025-02-17T04:37:22Z","title":"Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12214","snapshot_observed_at":"2026-08-07T12:38:58.257097Z","title":"Zero token-driven deep thinking in llms: Unlocking the full potential of existing parameters via cyclic refinement","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.257097Z"},"links":{"cited_paper":"/paper/2502.12214","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:c6417cdca7fc364be0c8a1e31f8c1944ae881ac5356069713ab170617fadf62e","observation_id":"313c5f69-0ace-448c-9ea0-494ca7f5c280","resolution":{"observed_at":"2026-08-07T12:38:58.257097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17416","last_updated":"2025-02-24T18:49:05Z","snapshot_observed_at":"2026-08-07T17:52:21.542087Z","submitted_at":"2025-02-24T18:49:05Z","title":"Reasoning with Latent Thoughts: On the Power of Looped Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.17416","snapshot_observed_at":"2026-08-07T12:38:58.395003Z","title":"Reasoning with latent thoughts: On the power of looped transformers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.395003Z"},"links":{"cited_paper":"/paper/2502.17416","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:407609ae71a26ecf0bef412562ce3fcd70b70f9dda31a765b797ef5023b499bb","observation_id":"9f4f3ba8-e72d-40ff-9ee7-72bce75c6385","resolution":{"observed_at":"2026-08-07T12:38:58.395003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20672","last_updated":"2025-02-28T16:44:24Z","snapshot_observed_at":"2026-07-06T19:40:30.194147Z","submitted_at":"2024-10-28T02:15:45Z","title":"Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20672","snapshot_observed_at":"2026-08-07T12:38:58.565382Z","title":"Relaxed recursive transformers: Effective parameter sharing with layer-wise lora.arXiv preprint arXiv:2410.20672, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.565382Z"},"links":{"cited_paper":"/paper/2410.20672","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:68559ffa9b7dfdadd8fa1027ef1b572c06809dd40dbd890bf829cffa806de736","observation_id":"630c0bab-e66f-40da-9c36-a46d52065b34","resolution":{"observed_at":"2026-08-07T12:38:58.565382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02406","last_updated":"2023-04-11T19:39:17Z","snapshot_observed_at":"2026-07-06T14:00:01.450718Z","submitted_at":"2022-10-05T17:28:20Z","title":"Decomposed Prompting: A Modular Approach for Solving Complex Tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02406","snapshot_observed_at":"2026-08-07T12:38:58.706064Z","title":"Decomposed prompting: A modular approach for solving complex tasks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.706064Z"},"links":{"cited_paper":"/paper/2210.02406","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:d42dba701e8f4bd29a07c6cc272297618ba69bde5892d85a7844876b6d42f37b","observation_id":"10cbbbc7-e76b-49f5-ae66-853b43d1d359","resolution":{"observed_at":"2026-08-07T12:38:58.706064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10625","last_updated":"2023-04-16T22:08:08Z","snapshot_observed_at":"2026-08-06T09:00:42.886249Z","submitted_at":"2022-05-21T15:34:53Z","title":"Least-to-Most Prompting Enables Complex Reasoning in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10625","snapshot_observed_at":"2026-08-07T12:38:58.829085Z","title":"Least-to-most prompting enables complex reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.829085Z"},"links":{"cited_paper":"/paper/2205.10625","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:9c4e075232b6bbde8021e52dac5262e6449cdf5665b6ade03cb86dee72a42fe3","observation_id":"2adcf3b0-d984-479c-a18c-2544c3837247","resolution":{"observed_at":"2026-08-07T12:38:58.829085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05653","last_updated":"2023-10-03T02:48:42Z","snapshot_observed_at":"2026-08-07T14:43:09.380195Z","submitted_at":"2023-09-11T17:47:22Z","title":"MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05653","snapshot_observed_at":"2026-08-07T12:38:58.990243Z","title":"Mammoth: Building math generalist models through hybrid instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:58.990243Z"},"links":{"cited_paper":"/paper/2309.05653","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:3d539629f9617b73bd25e51a4397e632dda3354545d1d566f0cf80801ae3bf75","observation_id":"506b41a4-bf4b-49d9-9a15-72af3a406299","resolution":{"observed_at":"2026-08-07T12:38:58.990243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12284","last_updated":"2024-05-03T17:36:07Z","snapshot_observed_at":"2026-08-02T15:00:50.388422Z","submitted_at":"2023-09-21T17:45:42Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12284","snapshot_observed_at":"2026-08-07T12:38:59.160941Z","title":"Metamath: Bootstrap your own mathematical questions for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.160941Z"},"links":{"cited_paper":"/paper/2309.12284","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:e89ae59e32d6bb71b7da2b87463f5a23e59e4573594959558b99fcd2cf33f14e","observation_id":"0d73ee2a-9e30-4d1c-adc5-174283bca839","resolution":{"observed_at":"2026-08-07T12:38:59.160941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08935","last_updated":"2024-02-19T14:07:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-14T13:41:54Z","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08935","snapshot_observed_at":"2026-08-07T12:38:59.313349Z","title":"Math-shepherd: Verify and reinforce llms step-by-step without human annotations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.313349Z"},"links":{"cited_paper":"/paper/2312.08935","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:69c39731065061c5dea3e87423aa54b9d8a1ddb34fbaaee54eb8af8a06caec03","observation_id":"389e2b64-ab36-4053-a795-36153396c59b","resolution":{"observed_at":"2026-08-07T12:38:59.313349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05673","last_updated":"2025-05-27T03:51:13Z","snapshot_observed_at":"2026-07-06T18:27:41.409205Z","submitted_at":"2024-06-09T07:06:58Z","title":"Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05673","snapshot_observed_at":"2026-08-07T12:38:59.455169Z","title":"Flow of reasoning: Efficient training of llm policy with divergent thinking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.455169Z"},"links":{"cited_paper":"/paper/2406.05673","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:adedced7dad9046e55abc64b8a158124a6d17eb1f9c583b999d457ddd27dcd62","observation_id":"1ca5d673-53dc-49c1-84be-7ffa980db0f6","resolution":{"observed_at":"2026-08-07T12:38:59.455169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04642","last_updated":"2024-03-07T16:36:29Z","snapshot_observed_at":"2026-08-07T01:30:54.327974Z","submitted_at":"2024-03-07T16:36:29Z","title":"Teaching Large Language Models to Reason with Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04642","snapshot_observed_at":"2026-08-07T12:38:59.544322Z","title":"Teaching large language models to reason with reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.544322Z"},"links":{"cited_paper":"/paper/2403.04642","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:dd8f3494d1b650cec7c09f0b10672d1adae29baf141c5f2320f0c0ac22049812","observation_id":"7e35f750-3716-42a7-88b1-4dbb4526b9d0","resolution":{"observed_at":"2026-08-07T12:38:59.544322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:59.702864Z","title":"Towards revealing the mystery behind chain of thought: a theoretical perspective","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.702864Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:702aedda479a4a12380838c6549a73b53f85fdc67b879abb683aba535043b35f","observation_id":"442c5e99-b733-4c97-8aa2-459008e02f7f","resolution":{"observed_at":"2026-08-07T12:38:59.702864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.07686","last_updated":"2022-10-13T06:24:37Z","snapshot_observed_at":"2026-08-07T08:06:38.051129Z","submitted_at":"2022-09-16T02:54:00Z","title":"Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.07686","snapshot_observed_at":"2026-08-07T12:38:59.858611Z","title":"Text and patterns: For effective chain of thought, it takes two to tango","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.858611Z"},"links":{"cited_paper":"/paper/2209.07686","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:9715cc62b22522f8014c8447d7d31e1a55fde42cd02ff0a5d004fb02ba9d23b8","observation_id":"d0f4a3e7-eec9-429e-9811-e6e2436cd3dd","resolution":{"observed_at":"2026-08-07T12:38:59.858611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09629","last_updated":"2024-03-18T07:56:48Z","snapshot_observed_at":"2026-07-31T09:25:34.205362Z","submitted_at":"2024-03-14T17:58:16Z","title":"Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09629","snapshot_observed_at":"2026-08-07T12:38:59.965565Z","title":"Quiet-star: Language models can teach themselves to think before speaking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:59.965565Z"},"links":{"cited_paper":"/paper/2403.09629","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:a5b6636c0e893b3b2cb308ce4c3024fb422c94acb9d17317a7322001742bfa8e","observation_id":"412d19c6-9ed3-41f9-994b-9684f7cccb7f","resolution":{"observed_at":"2026-08-07T12:38:59.965565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01460","last_updated":"2023-11-02T17:59:49Z","snapshot_observed_at":"2026-08-05T13:45:13.159592Z","submitted_at":"2023-11-02T17:59:49Z","title":"Implicit Chain of Thought Reasoning via Knowledge Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01460","snapshot_observed_at":"2026-08-07T12:39:00.066896Z","title":"Implicit chain of thought reasoning via knowledge distillation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.066896Z"},"links":{"cited_paper":"/paper/2311.01460","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:4addddbf026f9e15288d139e16fc0c6a35254f22cf6dd6b5aa6c0834831b934a","observation_id":"baa17150-f9e6-4f3c-a93c-994f0a5da3ad","resolution":{"observed_at":"2026-08-07T12:39:00.066896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06769","last_updated":"2025-11-03T00:53:34Z","snapshot_observed_at":"2026-08-07T06:05:27.895209Z","submitted_at":"2024-12-09T18:55:56Z","title":"Training Large Language Models to Reason in a Continuous Latent Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06769","snapshot_observed_at":"2026-08-07T12:39:00.128176Z","title":"Training large language models to reason in a continuous latent space","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.128176Z"},"links":{"cited_paper":"/paper/2412.06769","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:24c82ff5e3bbc0574cb7dd9dd64f1583a5bc6697e9496fcd4f1994c12d4ccee2","observation_id":"8eef7af6-2169-4f1a-9e79-346184e6a43f","resolution":{"observed_at":"2026-08-07T12:39:00.128176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03819","last_updated":"2019-03-05T16:46:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-07-10T18:39:15Z","title":"Universal Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03819","snapshot_observed_at":"2026-08-07T12:39:00.231480Z","title":"Uni- versal transformers","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.231480Z"},"links":{"cited_paper":"/paper/1807.03819","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:f9a43f796146c4ebd4dccf62bbf969de67113ed5d863af12075be10f0211bfff","observation_id":"ef4547e2-1f80-4b00-8d1d-3a9278426291","resolution":{"observed_at":"2026-08-07T12:39:00.231480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:00.341507Z","title":"Looped transformers as programmable computers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.341507Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:95ca717bd199b8777d2471b7c7cf54b327edffcb9bc49db00898df4fbb1cba77","observation_id":"f426f003-757e-443c-991e-ed36f67f5254","resolution":{"observed_at":"2026-08-07T12:39:00.341507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15647","last_updated":"2025-05-12T03:51:20Z","snapshot_observed_at":"2026-08-07T10:02:47.344567Z","submitted_at":"2024-09-24T01:21:17Z","title":"Looped Transformers for Length Generalization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15647","snapshot_observed_at":"2026-08-07T12:39:00.469205Z","title":"Looped transformers for length generalization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.469205Z"},"links":{"cited_paper":"/paper/2409.15647","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:f03d8ce9d56df400cee71c735736e91c66020396f58a29048b8e99367dc48c7b","observation_id":"18ca2c93-1902-4af6-bec1-5eca8dbfa16a","resolution":{"observed_at":"2026-08-07T12:39:00.469205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07096","last_updated":"2023-10-11T00:38:57Z","snapshot_observed_at":"2026-07-06T16:30:50.253688Z","submitted_at":"2023-10-11T00:38:57Z","title":"Sparse Universal Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07096","snapshot_observed_at":"2026-08-07T12:39:00.595174Z","title":"Sparse universal transformer","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.595174Z"},"links":{"cited_paper":"/paper/2310.07096","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:51c890f693f9040890344306e689f745fe2ac27b5f6ea67adf977bbcf9700da1","observation_id":"0be0c23a-6cb7-4995-b056-ce92355cf768","resolution":{"observed_at":"2026-08-07T12:39:00.595174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15166","last_updated":"2024-04-04T01:53:38Z","snapshot_observed_at":"2026-08-02T22:58:10.491259Z","submitted_at":"2023-12-23T05:11:37Z","title":"SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.15166","snapshot_observed_at":"2026-08-07T12:39:00.710124Z","title":"Solar 10.7 b: Scaling large language models with simple yet effective depth up-scaling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.710124Z"},"links":{"cited_paper":"/paper/2312.15166","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:4d95c47755626eb36301232aaff5c47b6ed30bb69b448a6824ec400d99dc8fa6","observation_id":"42830951-779f-4436-8dae-cfbc2b4694e2","resolution":{"observed_at":"2026-08-07T12:39:00.710124Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-07T12:39:00.869712Z","title":"Distilling the knowledge in a neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:00.869712Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:22683f9dd58d6523b61a5e2b5386dba59d0b16ff6f1d9fab1bb68138d365783f","observation_id":"d2c56fc4-33c1-4159-a480-c4f16df5e15c","resolution":{"observed_at":"2026-08-07T12:39:00.869712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:01.014315Z","title":"Knowledge distillation: A survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.014315Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:c8b0acb3cd7d9080018ccaf1fe0f1d4019f6c1b954ccde80faba3b424b902542","observation_id":"6fbc69df-34b4-4764-a4b6-4a45d8a3c26c","resolution":{"observed_at":"2026-08-07T12:39:01.014315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:05.949084Z","title":"Distilling knowledge via knowledge review","venue":null,"work_id":"cc1b4f3a-2358-4fac-b6c7-906c197e7f70","year":2021},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.182373Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:4ed8f56e8d6dfe3fbef8c6d2e54d3c63b2d0cf7cd6c74fc3305af042b748ce7a","observation_id":"82929eb2-9a40-46bd-ba10-b015aa6cc8d7","resolution":{"observed_at":"2026-08-07T12:39:06.085085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:01.309476Z","title":"Sequence-level knowledge distillation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.309476Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:84d031bdbdd0d196a6872f8d0ff7a8f59531ae944fd08b9bea189b27b844ad5c","observation_id":"3bf4fef3-6d33-425d-b912-c3026e9b5dc7","resolution":{"observed_at":"2026-08-07T12:39:01.309476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-07T12:39:01.463910Z","title":"Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.463910Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:29b73115ef80e3b97f02b7c3797e1cfed84b2e92af4b27d910f2ffe58ed2cf94","observation_id":"d38df3ce-806f-439c-af65-295494651df8","resolution":{"observed_at":"2026-08-07T12:39:01.463910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07013","last_updated":"2024-11-09T01:35:32Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T08:43:32Z","title":"Knowledge Distillation of Black-Box Large Language Models","version":2},"cited_work":{"arxiv_id":"2401.07013","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.07013","snapshot_observed_at":"2026-08-07T12:39:04.696308Z","title":"Knowledge Distillation of Black-Box Large Language Models","venue":"cs.CL","work_id":"7aee2a80-d906-44d3-90c8-a77d8f7e3f34","year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.590937Z"},"links":{"cited_paper":"/paper/2401.07013","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:92f17e0d642e9f256b03e230f762beaaf0d5cf1bd7434a857d068027c8231feb","observation_id":"14285c21-d0ad-49fb-946a-5807509d2a78","resolution":{"observed_at":"2026-08-07T12:39:04.865329Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.02019","last_updated":"2023-10-24T17:58:42Z","snapshot_observed_at":"2026-07-06T16:02:19.253303Z","submitted_at":"2023-08-03T20:20:01Z","title":"Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.02019","snapshot_observed_at":"2026-08-07T12:39:01.792404Z","title":"Baby llama: knowledge distillation from an en- semble of teachers trained on a small dataset with no performance penalty","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.792404Z"},"links":{"cited_paper":"/paper/2308.02019","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:2e9c973586f704f8d394a71840bfb6acd5e5b1efbaac090d1d718fd8d487deac","observation_id":"7ef4a804-9d0b-4784-8126-ff010bf97736","resolution":{"observed_at":"2026-08-07T12:39:01.792404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:05.681716Z","title":"Less is more: Task-aware layer-wise distillation for language model compression","venue":null,"work_id":"126fed50-e54a-4fc5-b34c-e872fe488b34","year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:01.947628Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:2f628a9b52170bd6d149cdf8aab2c5b1645dfedbab266e7c266b9244a3a3d3f8","observation_id":"1bb69e46-97fc-4539-be92-676476e448e2","resolution":{"observed_at":"2026-08-07T12:39:05.794012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:02.060479Z","title":"On the efficacy of knowledge distillation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.060479Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:f5fbfaa21c923943d4936381536099b7571a7137accf7c85827a036f1ce55f58","observation_id":"e476cb93-e62d-4242-a20d-9fa7adadab03","resolution":{"observed_at":"2026-08-07T12:39:02.060479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:02.170928Z","title":"Improved knowledge distillation via teacher assistant","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.170928Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:fda4662336cdd257ee58095ce56f763a86004cc9a0d18448a4e9cae7a5df6a2d","observation_id":"04849e67-5717-454d-8d36-ebca2d9c7489","resolution":{"observed_at":"2026-08-07T12:39:02.170928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08606","last_updated":"2025-07-25T16:55:43Z","snapshot_observed_at":"2026-08-08T04:26:43.927211Z","submitted_at":"2025-02-12T17:52:47Z","title":"Distillation Scaling Laws","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.08606","snapshot_observed_at":"2026-08-07T12:39:02.284171Z","title":"Distillation scaling laws","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.284171Z"},"links":{"cited_paper":"/paper/2502.08606","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:dbc80b880a589e814082ce085ff5b5d3e349761d0efc6cd92d8e82577ca3a1a7","observation_id":"7e70aa5e-28f4-4b46-9fbd-1b76d123f189","resolution":{"observed_at":"2026-08-07T12:39:02.284171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:02.394080Z","title":"Qwen2.5: A party of foundation models, September 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.394080Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:35b6e1b2b1b2f80aecc5c9d064fd6a62a60e59d85c0444939fd1652885813b2a","observation_id":"9b232ca3-5d9f-449f-b267-2cc340b1e6ea","resolution":{"observed_at":"2026-08-07T12:39:02.394080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:02.468310Z","title":"Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.468310Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:a256916cf13d052a220c954946bd234915217f822c70ec2e109af60e5aea0534","observation_id":"42f26d79-e360-44cc-87fa-62f8e73b84a0","resolution":{"observed_at":"2026-08-07T12:39:02.468310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-07T12:39:02.566365Z","title":"Qwen2 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.566365Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:c1caaa27d48b54dfb77bd922fa225c382ea52d59708c90db2af3b25bd5e3d20a","observation_id":"11039d38-8b1c-4a15-ac1d-1bd663373f48","resolution":{"observed_at":"2026-08-07T12:39:02.566365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:02.715723Z","title":"Llamafactory: Unified efficient fine-tuning of 100+ language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.715723Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:57fe0c56f142c40e1baa3d770b1f3858e64e02e01d70fd4088a1c5fd36d556d8","observation_id":"01ee158a-021f-472b-a3c8-aa00a2f29eb7","resolution":{"observed_at":"2026-08-07T12:39:02.715723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.03277","last_updated":"2023-04-06T17:58:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-06T17:58:09Z","title":"Instruction Tuning with GPT-4","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.03277","snapshot_observed_at":"2026-08-07T12:39:02.839854Z","title":"Instruction tuning with gpt-4","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.839854Z"},"links":{"cited_paper":"/paper/2304.03277","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:ad556819d4f021c0a9b0a7d5e41bcac4fdc8c58a4ac8e09c6e946029b6ea00ed","observation_id":"35f82636-5f70-4a7b-8e4a-17c1677b1682","resolution":{"observed_at":"2026-08-07T12:39:02.839854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:05.439815Z","title":"Alpaca-cot: An instruction fine-tuning platform with instruction data collection and unified large language models interface","venue":null,"work_id":"d698ed09-9c50-4b25-a789-e8e6acd8aeed","year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.907156Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:be8a95af01c9ad0cf1e8c59fc04c60cb9833ad1f2e8fec92f69ee303a99cc24f","observation_id":"42b94e28-e775-47d2-bc4c-569bda335970","resolution":{"observed_at":"2026-08-07T12:39:05.548689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:05.255834Z","title":"WikiQA: A challenge dataset for open-domain question answering","venue":null,"work_id":"b5e4c956-412b-45a3-a9d7-ad36d0b6c1ef","year":2015},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.014947Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:05440036e58e7fbe92325ae3389176ebdf6673ebe85a536f966ef4d65a4c765f","observation_id":"5ae7f865-bba1-42c4-a332-6745888e0355","resolution":{"observed_at":"2026-08-07T12:39:05.326404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:03.123651Z","title":"Code alpaca: An instruction-following llama model for code generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.123651Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:1454b3f0d2b49ed0aef6cccbfbe7cfb87d5270fb5c85939dfac5f53dfd6bb9d5","observation_id":"16eb53f3-4dc6-4d3c-a7e2-5ea84c40a0b6","resolution":{"observed_at":"2026-08-07T12:39:03.123651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02657","last_updated":"2024-12-08T13:03:38Z","snapshot_observed_at":"2026-08-08T09:33:25.935927Z","submitted_at":"2024-04-03T11:40:17Z","title":"Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02657","snapshot_observed_at":"2026-08-07T12:39:03.256973Z","title":"Rethinking kullback-leibler divergence in knowledge distillation for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.256973Z"},"links":{"cited_paper":"/paper/2404.02657","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:d3b2b306946ec4d6df9dd996416323e829ff7ceddca859692d95bab8224dc174","observation_id":"1a2e754c-441d-433e-82b2-00cc8baccd60","resolution":{"observed_at":"2026-08-07T12:39:03.256973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:03.371133Z","title":"The language model evaluation harness, 07 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.371133Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:61928deb08660e58e285eb65977d7c9ad2738e4671d91cc18660db8d5a3ad226","observation_id":"e1f90421-514d-4098-80ee-5681845a4196","resolution":{"observed_at":"2026-08-07T12:39:03.371133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-07T12:39:03.496024Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.496024Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:90dd9afcd54595db00bbc6b575ff040e4c315715e4aa87175ddbf6c6b1b8ab3d","observation_id":"5c14a53f-ea7c-4ca0-9937-38af2789aca1","resolution":{"observed_at":"2026-08-07T12:39:03.496024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:03.649952Z","title":"Can a suit of armor conduct electricity? a new dataset for open book question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.649952Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:4911af274b6f7f59d2db112f19274bd63210eaccb4e600a8d68d0b2c250885a8","observation_id":"cff1fc21-6759-4fdf-8161-a842998c528e","resolution":{"observed_at":"2026-08-07T12:39:03.649952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:03.809506Z","title":"TruthfulQA: Measuring how models mimic human falsehoods","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.809506Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:eef98c505718940660d89c2b9a9eccfde35eb9cf575f23d2e4f7b8415baa1aa9","observation_id":"dcb00a52-b591-4748-8654-26589f9663cd","resolution":{"observed_at":"2026-08-07T12:39:03.809506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:03.933993Z","title":"Training verifiers to solve math word problems, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:03.933993Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:6d4b87fb6b26e2565863c304b89c16241e6841cd9db783c3aa1524b7ee763730","observation_id":"2459a287-42da-4e4c-9a83-08be351e21e0","resolution":{"observed_at":"2026-08-07T12:39:03.933993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:04.032383Z","title":"Measuring massive multitask language understanding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:04.032383Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:b427a4be0c00b1d4f28a27eae0c1ffedb8791712cc2322751f34640594c9dc05","observation_id":"6adf213a-003e-43a6-82a2-a4031cb5b3e1","resolution":{"observed_at":"2026-08-07T12:39:04.032383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:39:04.212066Z","title":"Coqa: A conversational question answering challenge","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:04.212066Z"},"links":{"citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:764b4f0d75021bc8b87bdb81a5abe819d080df8f2587bc512c991485471da962","observation_id":"869e1db9-55b1-4c01-a24d-4c0d66542232","resolution":{"observed_at":"2026-08-07T12:39:04.212066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.07461","last_updated":"2019-02-22T23:53:34Z","snapshot_observed_at":"2026-07-06T06:34:26.609892Z","submitted_at":"2018-04-20T06:35:04Z","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.07461","snapshot_observed_at":"2026-08-07T12:39:04.370193Z","title":"Glue: A multi-task benchmark and analysis platform for natural language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:04.370193Z"},"links":{"cited_paper":"/paper/1804.07461","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:06232876ac2b3ac2ee7e10b4a306bf9fdbd27b8b1227c5d76ebed5a54e92658d","observation_id":"53ebe8ae-81dc-4217-aae2-0f77c72c3473","resolution":{"observed_at":"2026-08-07T12:39:04.370193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-02T19:23:53.535075Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-07T12:39:04.514038Z","title":"half–half–0","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:04.514038Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2505.24181"},"observation_digest":"sha256:37259be5ee3771489ce39e151204484595dd5e9a0ca6f157c6ecfc00de97caa9","observation_id":"96f43904-4d0e-4ab3-a015-075e711a745f","resolution":{"observed_at":"2026-08-07T12:39:04.514038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.24181","last_updated":"2025-05-30T03:43:24Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T12:28:41.940043Z","submitted_at":"2025-05-30T03:43:24Z","title":"SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":47,"verified_exact":1,"verified_fuzzy":5},"total_outbound_references":54},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2505.24181."}