{"as_of":"2026-08-18T12:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b45aab854b1b55ec69369c8cd36510bed728a7811ab802bfc80b95e23ac790fe","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:53:51.837228Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2505.00031/citation-record","integrity":"/paper/2505.00031/integrity","json":"/paper/2505.00031/citation-record.json","paper":"/paper/2505.00031"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-16T05:53:51.715403Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.715403Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:a3a838829abf61e7f839f614558b78f0901043afde69cd2ad5fdbd18edbdb4fb","observation_id":"0ab0af00-0203-4b81-b785-11a0652a0886","resolution":{"observed_at":"2026-08-16T05:53:51.715403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-16T05:53:51.738521Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.738521Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:d1acf86bb9ec6a12b6b9ccc695cce9242a49253d7bee21e2c24f49a6239e7dc1","observation_id":"39a24eeb-1631-4de6-b590-2bcd7168e6d0","resolution":{"observed_at":"2026-08-16T05:53:51.738521Z","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":"2026-08-16T05:53:52.128954Z","title":null,"venue":null,"work_id":"3dd515e0-e245-4bbd-a506-c242c4d5f3b1","year":2025},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.837228Z"},"links":{"citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:8d2d4bbad872403bf30acf06abe4f0d75e09a5dac1130e486d889e74c3e44466","observation_id":"6358ce5b-8da5-44df-a18b-00271ea8c8af","resolution":{"observed_at":"2026-08-16T05:53:52.134251Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02226","last_updated":"2024-04-21T03:39:21Z","snapshot_observed_at":"2026-08-16T14:55:24.465196Z","submitted_at":"2023-10-03T17:32:41Z","title":"Think before you speak: Training Language Models With Pause Tokens","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02226","snapshot_observed_at":"2026-08-16T05:53:51.747135Z","title":"Think before you speak: Training language models with pause tokens","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.747135Z"},"links":{"cited_paper":"/paper/2310.02226","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:ecca501e28d1a0202d93a83a01d30d416022b923efc4b908f2106f856808b58e","observation_id":"3f3a6948-d32a-4551-8dac-14bb9c7cd380","resolution":{"observed_at":"2026-08-16T05:53:51.747135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08998","last_updated":"2023-08-21T10:23:42Z","snapshot_observed_at":"2026-08-13T14:38:32.919809Z","submitted_at":"2023-08-17T14:12:48Z","title":"Reinforced Self-Training (ReST) for Language Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08998","snapshot_observed_at":"2026-08-16T05:53:51.750876Z","title":"Reinforced self-training (rest) for language modeling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.750876Z"},"links":{"cited_paper":"/paper/2308.08998","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:c3098a1d7e8ad259436c6281219ed9b39c6efb7ac31b598a5448cdab7bb68988","observation_id":"3c770e85-b40e-4e74-9a33-fb49f8825862","resolution":{"observed_at":"2026-08-16T05:53:51.750876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-08-13T20:44:28.824685Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-16T05:53:51.755225Z","title":"Measuring massive multitask language understanding","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.755225Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:bba68ba73f2358bc5bd4d8c675918c22fc33bdc287d69d8c66e9f264ff8e1720","observation_id":"d5a7d881-c75e-4b4e-b94f-138901121c4e","resolution":{"observed_at":"2026-08-16T05:53:51.755225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-08-16T05:53:51.759395Z","title":"Measuring mathematical problem solving with the math dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.759395Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:01c5efe954a403b6ad740bbbe51deed2511af4476de6b5ef00d326108f7d1001","observation_id":"36cebb4a-fe61-4f14-a0b7-78133d42363f","resolution":{"observed_at":"2026-08-16T05:53:51.759395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10033","last_updated":"2024-12-17T13:16:56Z","snapshot_observed_at":"2026-08-18T11:34:42.151830Z","submitted_at":"2024-09-16T06:51:32Z","title":"Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10033","snapshot_observed_at":"2026-08-16T05:53:51.762891Z","title":"Can gpt-o1 kill all bugs? arXiv preprint arXiv:2409.10033,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.762891Z"},"links":{"cited_paper":"/paper/2409.10033","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:3f163950a28b8f7448d898f132b1839a7cd9f7da6168ceef027fa20a4afd2efe","observation_id":"c1461a95-2c4a-43ef-9505-210ab9cc8b96","resolution":{"observed_at":"2026-08-16T05:53:51.762891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11610","last_updated":"2022-10-25T17:45:17Z","snapshot_observed_at":"2026-08-09T02:27:34.152063Z","submitted_at":"2022-10-20T21:53:54Z","title":"Large Language Models Can Self-Improve","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.11610","snapshot_observed_at":"2026-08-16T05:53:51.767042Z","title":"Large language models can self-improve","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.767042Z"},"links":{"cited_paper":"/paper/2210.11610","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:94412b271ff1acdcc98fccaafb029de36b90b7aa49211ad03bcf552b7509eef1","observation_id":"974ae1e5-e596-4248-851e-4af61fd8b113","resolution":{"observed_at":"2026-08-16T05:53:51.767042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06682","last_updated":"2024-10-16T23:19:46Z","snapshot_observed_at":"2026-08-16T13:55:15.115349Z","submitted_at":"2024-05-05T18:56:46Z","title":"Self-Reflection in LLM Agents: Effects on Problem-Solving Performance","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06682","snapshot_observed_at":"2026-08-16T05:53:51.770949Z","title":"Self-reflection in llm agents: Effects on problem-solving perfor- mance","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.770949Z"},"links":{"cited_paper":"/paper/2405.06682","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:4091e4784ae048b78fc5e74dce78541229a02f85d6b97feea3488245f5375408","observation_id":"13a0a7a4-b23b-4a0e-a922-1a6ed87dade6","resolution":{"observed_at":"2026-08-16T05:53:51.770949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06585","last_updated":"2024-04-18T03:12:09Z","snapshot_observed_at":"2026-08-16T14:35:47.075840Z","submitted_at":"2023-12-11T18:17:43Z","title":"Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06585","snapshot_observed_at":"2026-08-16T05:53:51.778603Z","title":"Beyond human data: Scaling self-training for problem-solving with language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.778603Z"},"links":{"cited_paper":"/paper/2312.06585","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:c585d6184ddcf33f705b3dafb00d5cdf8c5db324527c6b9cbab6eff0c09b9471","observation_id":"c0da9749-2e12-439c-b471-e5f0f21f102a","resolution":{"observed_at":"2026-08-16T05:53:51.778603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-16T05:53:51.782163Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.782163Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:a86d964d3ed8832d36b9bd40813d0b703160eccb42cb3d492a401a834acd4beb","observation_id":"8ef6ab95-f4de-4e23-95e2-88f0341a69ad","resolution":{"observed_at":"2026-08-16T05:53:51.782163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.09529","last_updated":"2017-06-29T00:54:47Z","snapshot_observed_at":"2026-08-14T20:50:56.570615Z","submitted_at":"2017-06-29T00:54:47Z","title":"Learning to Learn: Meta-Critic Networks for Sample Efficient Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.09529","snapshot_observed_at":"2026-08-16T05:53:51.786072Z","title":"Learning to learn: Meta-critic networks for sample efficient learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.786072Z"},"links":{"cited_paper":"/paper/1706.09529","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:03bebe4534ff429d85afbb3ffa8850e6989961726bbb6725461d9cdf6fda7e4c","observation_id":"93ac5fe7-0ce6-41ce-beb7-997ced2abae7","resolution":{"observed_at":"2026-08-16T05:53:51.786072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.04091","last_updated":"2023-05-26T07:06:48Z","snapshot_observed_at":"2026-08-15T11:15:42.066340Z","submitted_at":"2023-05-06T16:34:37Z","title":"Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.04091","snapshot_observed_at":"2026-08-16T05:53:51.790373Z","title":"Offline meta reinforcement learning with in-distribution online adaptation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.790373Z"},"links":{"cited_paper":"/paper/2305.04091","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:2b6dbf5bb85adc78245661e8681abb63a6de42fb840de3edcfc63e8777e0079b","observation_id":"1b0949c7-f089-451e-8559-dabebec686a6","resolution":{"observed_at":"2026-08-16T05:53:51.790373Z","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":"2026-08-17T11:08:48.802438Z","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":"2026-08-16T05:53:51.797997Z","title":"Qwen2 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.797997Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:f9b2cf5171f0e6384eba330f62751bdb3c7c84d6177f9a040d0147a86696a6b4","observation_id":"f82e66aa-83ca-47e3-a024-27fc3785c3ab","resolution":{"observed_at":"2026-08-16T05:53:51.797997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03629","last_updated":"2023-03-10T01:00:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-10-06T01:00:32Z","title":"ReAct: Synergizing Reasoning and Acting in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03629","snapshot_observed_at":"2026-08-16T05:53:51.801618Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.801618Z"},"links":{"cited_paper":"/paper/2210.03629","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:dff54bb5c1ea6744d693f7521ec08978bf4e371400facad8e7d2bdfc5acd5d7f","observation_id":"546ef39a-7d6b-4294-b15d-ffd82a5cadf7","resolution":{"observed_at":"2026-08-16T05:53:51.801618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01825","last_updated":"2023-09-13T03:57:29Z","snapshot_observed_at":"2026-08-16T18:17:11.146813Z","submitted_at":"2023-08-03T15:34:01Z","title":"Scaling Relationship on Learning Mathematical Reasoning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01825","snapshot_observed_at":"2026-08-16T05:53:51.809247Z","title":"Scaling relationship on learning mathematical reasoning with large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.809247Z"},"links":{"cited_paper":"/paper/2308.01825","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:ce037a63bc78e5876c30af4697de0fe1d59e87b2da63a53c96188d6613ebedcb","observation_id":"eae6b90c-700f-43e0-b163-8bfa1e4f4c1c","resolution":{"observed_at":"2026-08-16T05:53:51.809247Z","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":"2026-08-14T11:24:43.688110Z","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":"2026-08-16T05:53:51.812868Z","title":"Quiet-star: Language models can teach themselves to think before speaking","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.812868Z"},"links":{"cited_paper":"/paper/2403.09629","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:9d4ae30b2b1b64a5d11fd447210158e74f73d01c68941d4a13ba07e5e0e52e15","observation_id":"85216d06-567b-449e-aeb1-7fc105fef085","resolution":{"observed_at":"2026-08-16T05:53:51.812868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-08-15T09:37:44.321271Z","submitted_at":"2019-05-19T23:57:23Z","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.07830","snapshot_observed_at":"2026-08-16T05:53:51.816831Z","title":"Hellaswag: Can a ma- chine really finish your sentence? arXiv preprint arXiv:1905.07830,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.816831Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:84e0d36d66d3c98dcfc2487eb00b1f6d72e5022e030cda9726f57d96335a05dd","observation_id":"fca9c069-583a-44eb-bb2e-42d79ef81be5","resolution":{"observed_at":"2026-08-16T05:53:51.816831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-14T10:40:26.323157Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-16T05:53:51.821254Z","title":"Metacure: Meta reinforcement learning with empowerment-driven exploration","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.821254Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:fcc602a09224b8647fbb6975082a79a9305eedac3ddb38243a6dc53a5f55ca25","observation_id":"7287a431-953f-4da7-a8b8-a33671cbae4b","resolution":{"observed_at":"2026-08-16T05:53:51.821254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09797","last_updated":"2024-10-07T04:28:04Z","snapshot_observed_at":"2026-08-16T15:39:13.160910Z","submitted_at":"2023-04-19T16:29:48Z","title":"Progressive-Hint Prompting Improves Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.09797","snapshot_observed_at":"2026-08-16T05:53:51.825376Z","title":"Progressive-hint prompting improves reasoning in large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.825376Z"},"links":{"cited_paper":"/paper/2304.09797","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:b5207e5408f62556054e2e39ab1948b498951b3db15fe0e6a79364a7c78c9440","observation_id":"88999ce9-d762-4956-8171-4525a8390e5f","resolution":{"observed_at":"2026-08-16T05:53:51.825376Z","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":"2026-08-16T05:53:52.155773Z","title":"Different ways of utilizing inference compute","venue":null,"work_id":"108dbdee-e496-40e5-9329-29b049678948","year":2025},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.829307Z"},"links":{"citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:bcd51a516652dbe7b2622effbcdfd551447e63129699552d26d0033a53c186c0","observation_id":"8d61f70e-92b2-4a6c-a948-18a5f2a60f40","resolution":{"observed_at":"2026-08-16T05:53:52.161862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:53:52.142745Z","title":null,"venue":null,"work_id":"099ab103-152a-4305-b0d9-a7f4125e7c90","year":2020},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.833270Z"},"links":{"citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:3d8ee1600312ee8655398c087e970d9ede076a272ad4bc8ff6b89841bb0afa55","observation_id":"8377f31a-d38a-4593-a7c8-0411e43c56ed","resolution":{"observed_at":"2026-08-16T05:53:52.147491Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01574","last_updated":"2024-11-06T02:54:00Z","snapshot_observed_at":"2026-08-17T13:17:07.963143Z","submitted_at":"2024-06-03T17:53:00Z","title":"MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01574","snapshot_observed_at":"2026-08-16T05:53:51.794385Z","title":"Mmlu-pro: A more robust and challenging multi-task language understanding benchmark","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.794385Z"},"links":{"cited_paper":"/paper/2406.01574","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:3661228acc24c88393a0ea379e82638469b25808adef76f32acf73f143c9e715","observation_id":"d3e294ba-301f-4a9b-9aaf-286911abd31f","resolution":{"observed_at":"2026-08-16T05:53:51.794385Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14310","last_updated":"2024-02-22T05:58:03Z","snapshot_observed_at":"2026-08-16T14:16:21.832203Z","submitted_at":"2024-02-22T05:58:03Z","title":"Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14310","snapshot_observed_at":"2026-08-16T05:53:51.742773Z","title":"Hint-before- solving prompting: Guiding llms to effectively utilize encoded knowledge","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.742773Z"},"links":{"cited_paper":"/paper/2402.14310","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:0afc8bcc639b6c44183fd3a3dabb2f9452fc9020fbd0cce714f61611ac3aa80d","observation_id":"c1563b3f-7503-4da5-b4bb-7c45086bfe0a","resolution":{"observed_at":"2026-08-16T05:53:51.742773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-16T05:53:51.774784Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.774784Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:d7a2105168f0ce58f12d25e3a870e3e203449a41ae1fd11bf879208d616258d4","observation_id":"b37a86b8-8b1b-440c-b67e-31eae90f012f","resolution":{"observed_at":"2026-08-16T05:53:51.774784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01495","last_updated":"2025-05-07T05:01:14Z","snapshot_observed_at":"2026-08-16T13:46:39.042200Z","submitted_at":"2024-06-03T16:21:38Z","title":"Re-ReST: Reflection-Reinforced Self-Training for Language Agents","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01495","snapshot_observed_at":"2026-08-16T05:53:51.734199Z","title":"Reflection-reinforced self-training for language agents","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.734199Z"},"links":{"cited_paper":"/paper/2406.01495","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:71e07e82c15e39d6ed51884c7972c4faebc36da9e64733754ca6856f5e5d80af","observation_id":"79890b80-e686-4967-88d3-b5cbb1aab6d7","resolution":{"observed_at":"2026-08-16T05:53:51.734199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10642","last_updated":"2025-01-24T15:21:47Z","snapshot_observed_at":"2026-08-17T08:00:27.051605Z","submitted_at":"2024-04-16T15:16:22Z","title":"Self-playing Adversarial Language Game Enhances LLM Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10642","snapshot_observed_at":"2026-08-16T05:53:51.724793Z","title":"Self-playing adversarial language game enhances llm reasoning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.724793Z"},"links":{"cited_paper":"/paper/2404.10642","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:d4362177cac90038a460c8a9cc3885c214d25d7d29e86a312e19b2884ad8a73d","observation_id":"5571f14e-8568-4e4b-b151-01e6454d84b5","resolution":{"observed_at":"2026-08-16T05:53:51.724793Z","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":"2026-08-16T05:53:52.170114Z","title":"Lumos: Learning agents with unified data, modular design, and open-source llms","venue":null,"work_id":"1a776a7a-805e-48ae-a8fb-73a1b2163157","year":2024},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.805636Z"},"links":{"citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:07e9b0c5213efb955da6386e6526c7da22cb2991e8b0afc7affff686ded46592","observation_id":"28b1e9f6-3319-4957-bff2-6a4f52ded961","resolution":{"observed_at":"2026-08-16T05:53:52.174661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16737","last_updated":"2024-10-07T19:37:10Z","snapshot_observed_at":"2026-08-16T13:22:50.405013Z","submitted_at":"2024-08-29T17:32:35Z","title":"Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16737","snapshot_observed_at":"2026-08-16T05:53:51.720400Z","title":"Smaller, weaker, yet better: Training llm reasoners via compute-optimal sampling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.720400Z"},"links":{"cited_paper":"/paper/2408.16737","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:142721f21ac55f98a459ae543ae2f9ba597bdfaaca33c2df0254eb75f56339a3","observation_id":"e932a855-25f0-4f62-8bb9-5d35fa4b3049","resolution":{"observed_at":"2026-08-16T05:53:51.720400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10044","last_updated":"2019-05-24T05:48:49Z","snapshot_observed_at":"2026-08-17T14:44:42.060038Z","submitted_at":"2019-05-24T05:48:49Z","title":"BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10044","snapshot_observed_at":"2026-08-16T05:53:51.729135Z","title":"Boolq: Exploring the surprising difficulty of natural yes/no questions","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T05:53:51.729135Z"},"links":{"cited_paper":"/paper/1905.10044","citing_paper":"/paper/2505.00031"},"observation_digest":"sha256:d99ad466f25fdcc805b279baad73eac39cecd241cc69621a123418977fdcca25","observation_id":"eb59e3fb-77c8-444b-aa9d-1e0c957ecb48","resolution":{"observed_at":"2026-08-16T05:53:51.729135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.00031","last_updated":"2025-04-28T06:32:58Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T05:48:21.542364Z","submitted_at":"2025-04-28T06:32:58Z","title":"Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":31},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.00031."}