{"as_of":"2026-08-07T16:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:92cf199a868f6573f92c9bc6c46eae9f987891747551212035925a50b9649c72","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-16T10:39:16.997741Z","state":"measured"},{"denominator":132,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":132,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":100,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:44:37.360605Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":235,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2303.04671","last_updated":"2023-03-08T15:50:02Z","snapshot_observed_at":"2026-07-06T15:00:13.368355Z","submitted_at":"2023-03-08T15:50:02Z","title":"Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-13T22:50:24.053411Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2303.04671"},"observation_digest":"sha256:f432d4ebda52b9409ceda35eb4b990a31b289be7b8c2a888a59c95bde508930b","observation_id":"f9cbf5cd-57c8-4020-93be-59669aa174cc","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2303.09014","last_updated":"2023-03-16T01:04:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-16T01:04:45Z","title":"ART: Automatic multi-step reasoning and tool-use for large language models","version":1},"reference_index":145,"source":"arxiv_source","source_observed_at":"2026-05-16T19:03:05.597295Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2303.09014"},"observation_digest":"sha256:038f513a654940c36040913ac85e78c5db0e81095da0bdedf87e899b194a8ae2","observation_id":"5b57ed8e-338e-4a58-bb64-ae8ff099c593","resolution":{"observed_at":"2026-05-16T19:03:06.227091Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2304.06364","last_updated":"2023-09-18T14:23:02Z","snapshot_observed_at":"2026-08-07T12:51:50.861720Z","submitted_at":"2023-04-13T09:39:30Z","title":"AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models","version":2},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-05-16T10:03:58.971585Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2304.06364"},"observation_digest":"sha256:f59ae763f8eac2a12f2abef85b5440cf032ed3b13a73a2bb303dd32c69f67dd5","observation_id":"4c12a26a-adaf-41eb-9701-0e88483c06e6","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2305.19118","last_updated":"2024-10-09T02:41:21Z","snapshot_observed_at":"2026-08-01T16:20:31.337598Z","submitted_at":"2023-05-30T15:25:45Z","title":"Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate","version":4},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-14T00:00:15.372331Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2305.19118"},"observation_digest":"sha256:1807a602a5481cfda39180c9951ccb44d7cb8a8b98c131a898bca6d2bb7e1215","observation_id":"c7ce5eaa-f86d-4e91-b8c9-b79ac263eeb3","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2306.13549","last_updated":"2024-11-29T15:51:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-23T15:21:52Z","title":"A Survey on Multimodal Large Language Models","version":4},"reference_index":186,"source":"pdf_text","source_observed_at":"2026-05-16T02:56:41.658658Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2306.13549"},"observation_digest":"sha256:9ae8050244c92f7f00606f332c2ceb97a77cf690ef4e8db7335ba8ee4d8dc2c5","observation_id":"7b39f218-86e2-4eea-8a8c-89962bc0588e","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2310.03714","last_updated":"2023-10-05T17:37:25Z","snapshot_observed_at":"2026-08-04T05:21:05.846165Z","submitted_at":"2023-10-05T17:37:25Z","title":"DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-11T18:57:46.756656Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2310.03714"},"observation_digest":"sha256:70991b73d85a19ace8f34bf6904606acf57294001aedce8f7cc78c134c1eeb4f","observation_id":"e4fcb313-a2f3-4363-81da-f258341e43e6","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2402.07927","last_updated":"2025-03-16T06:23:34Z","snapshot_observed_at":"2026-08-05T17:55:26.008016Z","submitted_at":"2024-02-05T19:49:13Z","title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-12T21:52:09.938550Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2402.07927"},"observation_digest":"sha256:e6f97eda58c4b77ae23bc58dbad3135ddafec8e7add04bc2c59e699415ce3c81","observation_id":"ccc2a4aa-5706-4364-a1b2-f385a105a980","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2406.04692","last_updated":"2024-06-07T07:04:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-07T07:04:10Z","title":"Mixture-of-Agents Enhances Large Language Model Capabilities","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-16T19:29:34.379712Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2406.04692"},"observation_digest":"sha256:201f764fcc49f11c57eebb2eb88be20c02e47ef44c5c6be37c9b46ab168edc90","observation_id":"21fab816-d9e3-4f4d-8fcc-c7e282428494","resolution":{"observed_at":"2026-05-16T19:29:34.473601Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2409.11022","last_updated":"2026-05-15T09:10:20Z","snapshot_observed_at":"2026-08-01T08:31:26.233271Z","submitted_at":"2024-09-17T09:32:12Z","title":"DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition","version":7},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-23T20:40:09.530799Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2409.11022"},"observation_digest":"sha256:8ca1e727a4509642a076ea54b78aa19fe1fc04a939b04327fdf5236a60d8d67d","observation_id":"6186863a-eb31-4855-8508-a604d7d4e7b5","resolution":{"observed_at":"2026-05-23T20:43:25.466406Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2501.09686","last_updated":"2025-01-23T08:44:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T17:37:58Z","title":"Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models","version":3},"reference_index":191,"source":"pdf_text","source_observed_at":"2026-05-15T21:20:59.128986Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2501.09686"},"observation_digest":"sha256:3ab510e57e80588a7fd877eb24d0628bdcf10ac58ee4ca3ddbf6977c8c2cbaa8","observation_id":"02146419-2eee-47c3-b7e3-016161888cba","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2502.05664","last_updated":"2025-02-08T18:43:59Z","snapshot_observed_at":"2026-07-06T20:33:20.038920Z","submitted_at":"2025-02-08T18:43:59Z","title":"CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-23T03:54:34.321843Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2502.05664"},"observation_digest":"sha256:8650142abe2f044330fdb3821a57c6bc7dc1b370fd7febd41ddc7dad9762e6d5","observation_id":"ad4e2760-7261-40cd-904e-98a79415631e","resolution":{"observed_at":"2026-05-23T03:55:21.831297Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2503.12605","last_updated":"2025-03-23T13:47:43Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-16T18:39:13Z","title":"Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T17:18:52.996467Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2503.12605"},"observation_digest":"sha256:04682008011720a29d3a9ef3cb83ff60547e488c81bd657f77aa5c3128d8fee9","observation_id":"9dba376f-f429-4c10-a96c-0a31ca8663e3","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T15:44:37.360605Z","title":"Zhang, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14015","last_updated":"2025-06-19T10:05:42Z","snapshot_observed_at":"2026-08-07T15:40:14.156850Z","submitted_at":"2025-05-20T07:09:13Z","title":"AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T15:44:37.360605Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.14015"},"observation_digest":"sha256:494157ca4a817d994c8e12acf80d4ad45eb46c577824f416c5da66ae3c22b83f","observation_id":"8942558c-693d-41d1-93c8-aa22b577bdf6","resolution":{"observed_at":"2026-08-07T15:44:37.360605Z","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":"2026-08-07T15:38:50.251756Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14391","last_updated":"2025-05-20T14:12:05Z","snapshot_observed_at":"2026-08-07T15:32:56.082129Z","submitted_at":"2025-05-20T14:12:05Z","title":"Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T15:38:50.251756Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.14391"},"observation_digest":"sha256:15f7022475caf1b3590581e83dee35221122dd6a3c4eddd2af7387644ca79075","observation_id":"630a15a5-4fd0-4948-9489-2af1c8d60d94","resolution":{"observed_at":"2026-08-07T15:38:50.251756Z","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":"2026-08-07T15:36:11.356931Z","title":", Zhang , A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14528","last_updated":"2025-05-29T13:03:01Z","snapshot_observed_at":"2026-08-07T15:30:27.316711Z","submitted_at":"2025-05-20T15:48:34Z","title":"BugRepro: Enhancing Android Bug Reproduction with Domain-Specific Knowledge Integration","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T15:36:11.356931Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.14528"},"observation_digest":"sha256:e1d954b82afc95b967ce259b3bc6583b41567cc4c1d8e5a9ae69876008dc1309","observation_id":"160666e1-b31f-4db5-a33a-17699af5ca37","resolution":{"observed_at":"2026-08-07T15:36:11.356931Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2505.15436","last_updated":"2025-12-05T05:44:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-21T12:18:15Z","title":"Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-17T05:35:13.118221Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.15436"},"observation_digest":"sha256:f91863bf295127a0b4c2214ffbb27f7426f0144ad01f8d51e697d61cad04d706","observation_id":"77c96993-a7e1-40c5-8b6e-ded046456def","resolution":{"observed_at":"2026-05-17T05:35:13.258058Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T15:17:54.160592Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15684","last_updated":"2025-05-23T11:59:22Z","snapshot_observed_at":"2026-08-07T15:10:34.000316Z","submitted_at":"2025-05-21T15:58:16Z","title":"ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T15:17:54.160592Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.15684"},"observation_digest":"sha256:dd0f1d7756c1c5aa70d641e71a5c5a8677da68dac4217347d8f62d78ef7c3449","observation_id":"0cd5687a-49f8-4256-824c-289cafc01116","resolution":{"observed_at":"2026-08-07T15:17:54.160592Z","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":"2026-08-07T14:58:59.552575Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16806","last_updated":"2025-05-22T15:45:29Z","snapshot_observed_at":"2026-08-07T14:52:32.056355Z","submitted_at":"2025-05-22T15:45:29Z","title":"Two-way Evidence self-Alignment based Dual-Gated Reasoning Enhancement","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T14:58:59.552575Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.16806"},"observation_digest":"sha256:68e933307c49238c930a6b8c723a2821a98aba5f5d173b7a1cd5e561eee2c652","observation_id":"1469c00a-373f-49f6-856d-e909c69f0508","resolution":{"observed_at":"2026-08-07T14:58:59.552575Z","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":"2026-08-07T14:09:06.744893Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19956","last_updated":"2025-07-22T08:42:57Z","snapshot_observed_at":"2026-08-07T14:00:34.690209Z","submitted_at":"2025-05-26T13:19:10Z","title":"DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T14:09:06.744893Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.19956"},"observation_digest":"sha256:5f8c1b5acf575625cdea8040f1c607a7d40bc03d18686674d4ab7042fceec785","observation_id":"7ed84954-8652-45db-8936-a61321c88ee1","resolution":{"observed_at":"2026-08-07T14:09:06.744893Z","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":"2026-08-07T13:53:32.834055Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20753","last_updated":"2025-05-27T05:50:25Z","snapshot_observed_at":"2026-08-07T13:45:07.459562Z","submitted_at":"2025-05-27T05:50:25Z","title":"Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T13:53:32.834055Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.20753"},"observation_digest":"sha256:ad54072c9789f5a9442f1c60def5627a6ecabd9ef18d237205dbfb027fb5e9f8","observation_id":"250fc1e9-7210-495a-9cb2-b8a494f739d5","resolution":{"observed_at":"2026-08-07T13:53:32.834055Z","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":"2026-08-07T12:45:45.327386Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.23727","last_updated":"2025-05-29T17:55:49Z","snapshot_observed_at":"2026-08-07T12:36:34.635556Z","submitted_at":"2025-05-29T17:55:49Z","title":"PixelThink: Towards Efficient Chain-of-Pixel Reasoning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:45:45.327386Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.23727"},"observation_digest":"sha256:df915bcf887e3475c56ad3fc7657f22056706dd9c31598e8190532b57fded46b","observation_id":"c06fddbc-7fd3-4fb6-bc7f-1c763812e03a","resolution":{"observed_at":"2026-08-07T12:45:45.327386Z","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":"2026-08-07T12:37:16.334772Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24143","last_updated":"2025-05-30T02:26:05Z","snapshot_observed_at":"2026-08-07T12:30:22.984470Z","submitted_at":"2025-05-30T02:26:05Z","title":"CrossICL: Cross-Task In-Context Learning via Unsupervised Demonstration Transfer","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T12:37:16.334772Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2505.24143"},"observation_digest":"sha256:4ce360dcf86f254b0381ab04033ef4ed32a7cdd5d8ea6de5c2385bab98ac52d4","observation_id":"dff09aa5-5acc-4069-9bc8-deec54559f94","resolution":{"observed_at":"2026-08-07T12:37:16.334772Z","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":"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":"2026-08-07T12:11:13.524958Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00309","last_updated":"2025-06-28T05:54:45Z","snapshot_observed_at":"2026-08-07T12:05:33.056700Z","submitted_at":"2025-05-30T23:37:37Z","title":"Evaluation of LLMs for mathematical problem solving","version":3},"reference_index":137,"source":"pdf_text","source_observed_at":"2026-08-07T12:11:13.524958Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.00309"},"observation_digest":"sha256:5f05188ed968ab07998e02b4be30d69062db6cd9455d00775e84ab9a1e3e0970","observation_id":"57c08818-437c-4e00-a722-cc987b76e5a8","resolution":{"observed_at":"2026-08-07T12:11:13.524958Z","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":"2026-08-07T12:04:31.120330Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00722","last_updated":"2025-05-31T21:43:37Z","snapshot_observed_at":"2026-08-07T11:57:10.512414Z","submitted_at":"2025-05-31T21:43:37Z","title":"Chain-of-Thought Training for Open E2E Spoken Dialogue Systems","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:04:31.120330Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.00722"},"observation_digest":"sha256:4b3392c4ddd7d3cad1280aaa767c09ac040822e7ba2d946973638afa4cfd985d","observation_id":"b0695da3-f98b-49b1-8eb9-cef407085ef6","resolution":{"observed_at":"2026-08-07T12:04:31.120330Z","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":"2026-08-07T11:55:03.190168Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.01096","last_updated":"2025-08-08T08:03:03Z","snapshot_observed_at":"2026-08-07T11:48:21.020886Z","submitted_at":"2025-06-01T17:43:54Z","title":"SuperRL: Reinforcement Learning with Supervision to Boost Language Model Reasoning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:55:03.190168Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.01096"},"observation_digest":"sha256:3fad69cd3cc9db05a1538c86b8f285bfe953f4e55510a1a0efe0d9f2fdad9b25","observation_id":"bcd4c7a1-78ae-496b-8327-50f9e70e0ea2","resolution":{"observed_at":"2026-08-07T11:55:03.190168Z","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":"2026-08-07T11:17:42.323534Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.03100","last_updated":"2025-06-09T10:35:22Z","snapshot_observed_at":"2026-08-07T11:06:27.053756Z","submitted_at":"2025-06-03T17:31:53Z","title":"Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T11:17:42.323534Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.03100"},"observation_digest":"sha256:ce1faed0be9a2ef05674ffb7c5e4e000f04fc247d202e48f29feb36b4a389c24","observation_id":"fd2d55af-4d18-4142-b4f0-cb2d681259d0","resolution":{"observed_at":"2026-08-07T11:17:42.323534Z","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":"2026-08-07T10:42:41.129087Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.04611","last_updated":"2025-06-05T04:02:17Z","snapshot_observed_at":"2026-08-07T13:54:09.003484Z","submitted_at":"2025-06-05T04:02:17Z","title":"Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-07T10:42:41.129087Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.04611"},"observation_digest":"sha256:5eaa85d8f68c84b5682794226be85692cc406e841d631871fe0f555dc4c87f8a","observation_id":"79bef6ae-a8a3-43c1-ada3-8a910bc018d0","resolution":{"observed_at":"2026-08-07T10:42:41.129087Z","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":"2026-08-07T10:28:44.589106Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05213","last_updated":"2025-06-05T16:27:49Z","snapshot_observed_at":"2026-08-07T10:20:08.092949Z","submitted_at":"2025-06-05T16:27:49Z","title":"LLM-First Search: Self-Guided Exploration of the Solution Space","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:28:44.589106Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.05213"},"observation_digest":"sha256:c2b599c3636fcdb21c2e9294312bdf52f35acae7e71ac770ae8e578ca8ac3aec","observation_id":"fb97d6ba-3800-4a31-af00-9351dca19863","resolution":{"observed_at":"2026-08-07T10:28:44.589106Z","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":"2026-08-07T06:09:18.898118Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.06008","last_updated":"2025-06-06T11:53:27Z","snapshot_observed_at":"2026-08-07T10:44:12.390654Z","submitted_at":"2025-06-06T11:53:27Z","title":"Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T06:09:18.898118Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.06008"},"observation_digest":"sha256:90558713d50e80965aa8a84a80b0b0cabb0c619c8363c004e146402180582035","observation_id":"979de358-7b98-48f3-a05c-8e4bb7c463f0","resolution":{"observed_at":"2026-08-07T06:09:18.898118Z","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":"2026-08-07T05:25:30.448415Z","title":"Automatic chain of thought prompting in large language models, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07936","last_updated":"2025-06-09T16:55:32Z","snapshot_observed_at":"2026-08-07T13:27:16.192025Z","submitted_at":"2025-06-09T16:55:32Z","title":"Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:30.448415Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.07936"},"observation_digest":"sha256:f4f5564484516d41bff279e66423abf70c9f59e6f4a87d061433a145fda4c602","observation_id":"3d2ce3f7-cb96-49b9-a637-0ebc28f48b68","resolution":{"observed_at":"2026-08-07T05:25:30.448415Z","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":"2026-08-07T05:19:04.754624Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08243","last_updated":"2025-06-09T21:21:12Z","snapshot_observed_at":"2026-08-07T05:13:08.703639Z","submitted_at":"2025-06-09T21:21:12Z","title":"Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T05:19:04.754624Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.08243"},"observation_digest":"sha256:d6bc1127b49e787d3c9e4ea9e3bd45b76639d7d281114d6d959cff5a4f98c455","observation_id":"eaec8941-bb07-40a9-8436-79f5baaf9479","resolution":{"observed_at":"2026-08-07T05:19:04.754624Z","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":"2026-08-07T05:18:03.510484Z","title":"arXiv preprint arXiv:2210.03493 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08430","last_updated":"2025-06-12T05:41:40Z","snapshot_observed_at":"2026-08-07T06:39:11.801057Z","submitted_at":"2025-06-10T04:05:06Z","title":"CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:18:03.510484Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.08430"},"observation_digest":"sha256:01a42a1e67a716844b5b97993415e05324c330e1093c876e87ca3e6228602bb2","observation_id":"a1bd6d0d-dd2d-4087-b94a-3ce39836d626","resolution":{"observed_at":"2026-08-07T05:18:03.510484Z","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":"2026-08-07T05:09:23.513166Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08691","last_updated":"2025-06-10T11:02:36Z","snapshot_observed_at":"2026-08-07T12:16:31.533543Z","submitted_at":"2025-06-10T11:02:36Z","title":"VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T05:09:23.513166Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.08691"},"observation_digest":"sha256:8627787bfa0374afc07e89f8f5b8490e577ea0b60c4974f062a819d8fa0ac063","observation_id":"ba1fb63a-f1e6-4641-aa47-f9c3ad038dcd","resolution":{"observed_at":"2026-08-07T05:09:23.513166Z","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":"2026-08-07T05:05:52.320226Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08817","last_updated":"2025-06-12T15:51:33Z","snapshot_observed_at":"2026-08-07T04:59:07.065080Z","submitted_at":"2025-06-10T14:08:56Z","title":"Video-CoT: A Comprehensive Dataset for Spatiotemporal Understanding of Videos Based on Chain-of-Thought","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:52.320226Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.08817"},"observation_digest":"sha256:8371150613cafcdbdee8b9db4c473379a5045f4b4d993c0e67abad33df7ae001","observation_id":"f7c99c30-876e-410f-a74d-9c6175633fc5","resolution":{"observed_at":"2026-08-07T05:05:52.320226Z","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":"2026-08-07T15:04:35.091346Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11052","last_updated":"2025-05-22T09:33:55Z","snapshot_observed_at":"2026-08-07T14:58:39.817296Z","submitted_at":"2025-05-22T09:33:55Z","title":"ACCORD: Autoregressive Constraint-satisfying Generation for COmbinatorial Optimization with Routing and Dynamic attention","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:35.091346Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.11052"},"observation_digest":"sha256:6919b75659a45703c03721705a56f91414063487fdbfa567277893ead27b3edf","observation_id":"16d40969-adce-4db9-a86f-1b784c130bde","resolution":{"observed_at":"2026-08-07T15:04:35.091346Z","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":"2026-08-07T04:07:56.846228Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11561","last_updated":"2025-06-13T08:15:45Z","snapshot_observed_at":"2026-08-07T04:02:07.130634Z","submitted_at":"2025-06-13T08:15:45Z","title":"Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T04:07:56.846228Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.11561"},"observation_digest":"sha256:531182264613080c663c3bc7d64e23ded3876d3671105431038fecf943954155","observation_id":"24e6a930-f2a7-4458-82be-7e6b9f38d6aa","resolution":{"observed_at":"2026-08-07T04:07:56.846228Z","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":"2026-08-07T00:42:05.441867Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.13186","last_updated":"2025-06-16T07:52:15Z","snapshot_observed_at":"2026-08-07T15:42:13.842021Z","submitted_at":"2025-06-16T07:52:15Z","title":"Empirical Evaluation of Large Language Models in Automated Program Repair","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T00:42:05.441867Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.13186"},"observation_digest":"sha256:e8449ea82d1ba8ff9c0f79d2ea8a724cb765b2379c50132b3a9e989ba6ee7be0","observation_id":"7e50a810-d84d-4cd0-8eb6-d0b0ab34c358","resolution":{"observed_at":"2026-08-07T00:42:05.441867Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2506.13351","last_updated":"2026-05-07T20:19:13Z","snapshot_observed_at":"2026-07-31T17:48:52.945760Z","submitted_at":"2025-06-16T10:43:38Z","title":"Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-19T09:48:56.990745Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.13351"},"observation_digest":"sha256:1cf576a8b1d58ddebd63f37d46c9eafa26e6880ec052d84e9226362d19b26e5d","observation_id":"74be46e2-347f-41b3-9412-e3c4e10abec1","resolution":{"observed_at":"2026-05-19T09:52:14.178050Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T12:29:35.826782Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.15706","last_updated":"2025-05-30T08:42:14Z","snapshot_observed_at":"2026-08-07T12:22:37.607140Z","submitted_at":"2025-05-30T08:42:14Z","title":"MDPO: Multi-Granularity Direct Preference Optimization for Mathematical Reasoning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:29:35.826782Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.15706"},"observation_digest":"sha256:6a193cd7469e7c29847a5a8ce0e5a035368d9f0a2287acb8b412c008ff65d3d6","observation_id":"f6560daf-019a-4a7a-85c5-15fd3c253c84","resolution":{"observed_at":"2026-08-07T12:29:35.826782Z","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":"2026-08-06T23:28:22.231291Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18183","last_updated":"2025-07-18T02:39:29Z","snapshot_observed_at":"2026-08-07T07:48:56.817946Z","submitted_at":"2025-06-22T21:46:42Z","title":"Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T23:28:22.231291Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.18183"},"observation_digest":"sha256:856fd39b996db83848462da4e299994409347d1d88500465b7bd547e08826209","observation_id":"219c57ad-6ee5-42e7-9f00-11ac9900702c","resolution":{"observed_at":"2026-08-06T23:28:22.231291Z","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":"2026-08-06T23:12:12.682873Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19469","last_updated":"2025-06-24T09:53:10Z","snapshot_observed_at":"2026-08-07T12:44:11.285996Z","submitted_at":"2025-06-24T09:53:10Z","title":"Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:12.682873Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.19469"},"observation_digest":"sha256:6b7875a94c8f9f3147edd84351d52e3c1698fe14850fa16ed50db9819718d2ac","observation_id":"ecbf9516-801e-4646-90d2-b0560d9acc53","resolution":{"observed_at":"2026-08-06T23:12:12.682873Z","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":"2026-08-06T23:12:13.594582Z","title":"(2022, Octo- ber 7).Automatic chain of thought prompting in large lan- guage models.arXiv:2210.03493v1 [cs.CL]","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19599","last_updated":"2025-06-24T13:09:53Z","snapshot_observed_at":"2026-08-06T23:04:21.804814Z","submitted_at":"2025-06-24T13:09:53Z","title":"ECCoT: A Framework for Enhancing Effective Cognition via Chain of Thought in Large Language Model","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:13.594582Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.19599"},"observation_digest":"sha256:77c3ee45c7e4cba030bbdfe435be41b253808cad7a8e0893fee9102793950c9a","observation_id":"9bfe5f84-9b49-420b-a43c-f608644d8528","resolution":{"observed_at":"2026-08-06T23:12:13.594582Z","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":"2026-08-06T21:58:36.595291Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23025","last_updated":"2025-06-28T22:13:43Z","snapshot_observed_at":"2026-08-06T21:49:40.850962Z","submitted_at":"2025-06-28T22:13:43Z","title":"Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-06T21:58:36.595291Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2506.23025"},"observation_digest":"sha256:b8c7ca57292974f367cf1a0167304afe43230a02125fd60c10391dd409e499a0","observation_id":"825f6e34-122b-4d7a-9d80-0aa45d379904","resolution":{"observed_at":"2026-08-06T21:58:36.595291Z","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":"2026-08-06T22:00:08.152957Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00068","last_updated":"2025-06-28T12:12:06Z","snapshot_observed_at":"2026-08-07T14:17:56.782121Z","submitted_at":"2025-06-28T12:12:06Z","title":"MANTA: Cross-Modal Semantic Alignment and Information-Theoretic Optimization for Long-form Multimodal Understanding","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T22:00:08.152957Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.00068"},"observation_digest":"sha256:5e3507b1229a9ab096e40050a801ae4882f41e7a62a031169adf128d8e7f5d38","observation_id":"da8b6c90-b642-43d6-bf39-9afb3f7e333a","resolution":{"observed_at":"2026-08-06T22:00:08.152957Z","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":"2026-08-06T21:38:14.518349Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00092","last_updated":"2025-06-30T09:53:41Z","snapshot_observed_at":"2026-08-06T21:32:11.201568Z","submitted_at":"2025-06-30T09:53:41Z","title":"Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:14.518349Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.00092"},"observation_digest":"sha256:d4c5270afc622ca2e5b34101207fa842354e455e5a7c5691d71d8675229edcb2","observation_id":"4ee23331-b47a-4918-9836-721178f86c37","resolution":{"observed_at":"2026-08-06T21:38:14.518349Z","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":"2026-08-06T20:38:59.268761Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02306","last_updated":"2025-07-03T04:27:16Z","snapshot_observed_at":"2026-08-06T20:30:06.629772Z","submitted_at":"2025-07-03T04:27:16Z","title":"Synthetic Heuristic Evaluation: A Comparison between AI- and Human-Powered Usability Evaluation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T20:38:59.268761Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.02306"},"observation_digest":"sha256:7637165cca61ab04e60ce0011bbfbac17cad8842583b6642fe060747a51ec6e6","observation_id":"a5e04b17-d062-439e-9832-6f1e0bb3c011","resolution":{"observed_at":"2026-08-06T20:38:59.268761Z","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":"2026-08-07T10:18:55.636461Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02870","last_updated":"2025-06-06T10:50:08Z","snapshot_observed_at":"2026-08-07T12:08:16.428168Z","submitted_at":"2025-06-06T10:50:08Z","title":"Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:55.636461Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.02870"},"observation_digest":"sha256:1b1892b6b931df3b550d3daf0f322bc5bd56746d7f5bc5baf11b06efcef08586","observation_id":"0998a2b5-bb49-415e-9ea1-4c5148904727","resolution":{"observed_at":"2026-08-07T10:18:55.636461Z","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":"2026-08-06T19:58:37.787237Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.04142","last_updated":"2025-07-05T19:43:54Z","snapshot_observed_at":"2026-08-06T19:52:02.081681Z","submitted_at":"2025-07-05T19:43:54Z","title":"Dissecting Clinical Reasoning in Language Models: A Comparative Study of Prompts and Model Adaptation Strategies","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T19:58:37.787237Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.04142"},"observation_digest":"sha256:7379fa5500b127e9bfbc3367a47ae760e378c984f17df00ea86f82cccc057f6f","observation_id":"11d09d17-f5dc-4902-82fd-67eb2d12c2e9","resolution":{"observed_at":"2026-08-06T19:58:37.787237Z","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":"2026-08-06T18:40:04.600434Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.07682","last_updated":"2025-07-10T12:02:56Z","snapshot_observed_at":"2026-08-06T18:32:59.808825Z","submitted_at":"2025-07-10T12:02:56Z","title":"Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T18:40:04.600434Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.07682"},"observation_digest":"sha256:0844c74cfe838e5955b942730d0c70def991c1c7c106aece67986d2c737132c4","observation_id":"b63adc93-fe2c-4f9f-8216-7c163e98ff61","resolution":{"observed_at":"2026-08-06T18:40:04.600434Z","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":"2026-08-06T18:19:59.108408Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.08575","last_updated":"2025-07-11T13:23:25Z","snapshot_observed_at":"2026-08-06T18:12:39.414027Z","submitted_at":"2025-07-11T13:23:25Z","title":"Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T18:19:59.108408Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.08575"},"observation_digest":"sha256:f18ba92892c05c160e0ca2e89734a82e0dc38b05c946fb79bf00971d8e41b63f","observation_id":"493f346d-baa6-456f-8d54-81b62e4951eb","resolution":{"observed_at":"2026-08-06T18:19:59.108408Z","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":"2026-08-06T18:04:32.610070Z","title":"arXiv preprint arXiv:2210.03493 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09259","last_updated":"2025-07-12T11:44:41Z","snapshot_observed_at":"2026-08-06T17:57:43.710535Z","submitted_at":"2025-07-12T11:44:41Z","title":"Psychology-Driven Enhancement of Humour Translation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T18:04:32.610070Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.09259"},"observation_digest":"sha256:7a176cf80741c27e884771435d78802f4b5ee19c99537d79e7a5b4ec3b3ad692","observation_id":"67bf7917-b68d-4ba6-ba87-cec9e54e0ff3","resolution":{"observed_at":"2026-08-06T18:04:32.610070Z","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":"2026-08-06T17:51:18.153821Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09850","last_updated":"2025-07-16T17:16:18Z","snapshot_observed_at":"2026-08-06T17:43:33.644403Z","submitted_at":"2025-07-14T01:14:50Z","title":"The Challenge of Teaching Reasoning to LLMs Without RL or Distillation","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:51:18.153821Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.09850"},"observation_digest":"sha256:c2df99561428f848e85503e168bd2424a421a5c0b6f70cd0e7360fbf92ed2712","observation_id":"5bdb560a-8df6-4ae0-950c-3e6bf4abd00d","resolution":{"observed_at":"2026-08-06T17:51:18.153821Z","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":"2026-08-06T16:42:42.837926Z","title":"Zhang, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.12761","last_updated":"2025-07-17T03:33:46Z","snapshot_observed_at":"2026-08-06T16:36:45.445367Z","submitted_at":"2025-07-17T03:33:46Z","title":"Think-Before-Draw: Decomposing Emotion Semantics & Fine-Grained Controllable Expressive Talking Head Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:42.837926Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2507.12761"},"observation_digest":"sha256:3490576bff03d0f7402cf39a48a7921bec1d932c646a4a8bd33e7d9d8b5e5a7a","observation_id":"042e05d8-18b1-4811-87ee-1b78cb02b42d","resolution":{"observed_at":"2026-08-06T16:42:42.837926Z","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":"2026-08-06T00:49:58.718193Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.04216","last_updated":"2025-08-06T08:48:55Z","snapshot_observed_at":"2026-08-07T02:35:26.420682Z","submitted_at":"2025-08-06T08:48:55Z","title":"Causal Reward Adjustment: Mitigating Reward Hacking in External Reasoning via Backdoor Correction","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T00:49:58.718193Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2508.04216"},"observation_digest":"sha256:a766d1d0fe885cbe0fcb5a2e6d9d3a8e3fa1f81475c60d5fcb5df42766783f09","observation_id":"87d5d540-9d51-4cb6-85c3-5b5292754f9a","resolution":{"observed_at":"2026-08-06T00:49:58.718193Z","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":"2026-08-05T16:17:37.987258Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.18772","last_updated":"2025-08-26T07:55:46Z","snapshot_observed_at":"2026-08-07T08:24:33.804357Z","submitted_at":"2025-08-26T07:55:46Z","title":"Beyond the Textual: Generating Coherent Visual Options for MCQs","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-05T16:17:37.987258Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2508.18772"},"observation_digest":"sha256:4845110cd45c259f7e22be99c4f1a6481c7db22b3e2c9453de92249cee5047aa","observation_id":"a0613d84-256d-48ef-9b30-a38b62e6bb55","resolution":{"observed_at":"2026-08-05T16:17:37.987258Z","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":"2026-08-05T12:41:12.247071Z","title":"Automatic chain of thought prompting in large language models, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.01363","last_updated":"2025-09-01T11:04:51Z","snapshot_observed_at":"2026-08-05T12:41:11.360165Z","submitted_at":"2025-09-01T11:04:51Z","title":"Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T12:41:12.247071Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.01363"},"observation_digest":"sha256:486fc7e5543d7059cfbd1713ed23e41ae35cb43746368d6e086b2c3f6f371a71","observation_id":"6c0c152a-d375-4dbe-8945-0ab550f6a49f","resolution":{"observed_at":"2026-08-05T12:41:12.247071Z","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":"2026-08-05T11:48:51.875784Z","title":"arXiv preprint arXiv:2210.03493 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.02241","last_updated":"2025-09-02T12:09:49Z","snapshot_observed_at":"2026-08-07T06:10:37.238530Z","submitted_at":"2025-09-02T12:09:49Z","title":"LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T11:48:51.875784Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.02241"},"observation_digest":"sha256:788acbdd7153908ab1e9c22aa38f0f43c3bb706838d2a30c751ca950533c1451","observation_id":"ef91febe-48de-4b92-9ef7-1d28933c4e3f","resolution":{"observed_at":"2026-08-05T11:48:51.875784Z","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":"2026-08-05T04:50:31.622691Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.05946","last_updated":"2025-09-07T06:46:03Z","snapshot_observed_at":"2026-08-05T14:52:11.492382Z","submitted_at":"2025-09-07T06:46:03Z","title":"Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T04:50:31.622691Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.05946"},"observation_digest":"sha256:7605609d5270fbb9a170e003429024a0964493e6aa07b012645a52e522697cd7","observation_id":"041a3046-0894-49db-9a0f-09f2b8d05544","resolution":{"observed_at":"2026-08-05T04:50:31.622691Z","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":"2026-08-04T22:25:54.979131Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.07339","last_updated":"2025-09-09T02:31:16Z","snapshot_observed_at":"2026-08-06T03:19:26.122470Z","submitted_at":"2025-09-09T02:31:16Z","title":"Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T22:25:54.979131Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.07339"},"observation_digest":"sha256:5fd253eab28e8b18853cc7e7e8ce7cb96f1b8d58783326cee917b05bad517373","observation_id":"f7caad15-2593-4945-84b6-bb035eeeaab2","resolution":{"observed_at":"2026-08-04T22:25:54.979131Z","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":"2026-08-04T19:17:02.231359Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.09342","last_updated":"2025-09-11T10:56:40Z","snapshot_observed_at":"2026-08-04T19:17:01.112030Z","submitted_at":"2025-09-11T10:56:40Z","title":"CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-04T19:17:02.231359Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.09342"},"observation_digest":"sha256:894fb5af4b682605adb8bbf5f4d35dc897b5b4890c786df0523c3c8f77bdf5cf","observation_id":"039f6ed9-b4b1-4bb9-8fd0-5745ffabc9e1","resolution":{"observed_at":"2026-08-04T19:17:02.231359Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2509.11295","last_updated":"2026-04-29T13:18:01Z","snapshot_observed_at":"2026-07-06T22:29:58.066459Z","submitted_at":"2025-09-14T14:39:35Z","title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","version":2},"reference_index":144,"source":"pdf_text","source_observed_at":"2026-05-18T16:39:03.794436Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.11295"},"observation_digest":"sha256:df728d8f5b8810cacc99b6e0f6314bc6475ba181298bba4a9b8e371bca8d397a","observation_id":"4a7c08cd-5ee4-4f1a-9e9c-9b9d90b4a9f8","resolution":{"observed_at":"2026-05-18T16:41:38.102353Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2509.11295","last_updated":"2026-04-29T13:18:01Z","snapshot_observed_at":"2026-07-06T22:29:58.066459Z","submitted_at":"2025-09-14T14:39:35Z","title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","version":2},"reference_index":145,"source":"pdf_text","source_observed_at":"2026-05-18T16:39:03.794436Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.11295"},"observation_digest":"sha256:7cdb5f10a060c329288a3a0aa7bcac479cdbf6e0b353023b157a04df2588bee1","observation_id":"3ae8e806-cc8f-40d2-bdac-7e7ee0d0113d","resolution":{"observed_at":"2026-05-18T16:41:37.558560Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2509.21465","last_updated":"2026-05-15T11:35:18Z","snapshot_observed_at":"2026-07-06T22:30:46.129271Z","submitted_at":"2025-09-25T19:30:39Z","title":"Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-21T22:05:11.146329Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.21465"},"observation_digest":"sha256:43f4d40f07f3b8d2b92e8e02bd58f345961d15701561ae0d5203c7d8c8e7a6e5","observation_id":"8794f1b7-8b5e-4db4-a72a-db5ffb9116e1","resolution":{"observed_at":"2026-05-21T22:05:41.458871Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2509.25699","last_updated":"2026-07-21T17:19:46Z","snapshot_observed_at":"2026-08-04T13:42:58.327870Z","submitted_at":"2025-09-30T02:57:44Z","title":"AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-18T13:30:10.620448Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.25699"},"observation_digest":"sha256:290a3e195b58fc64c9deacde1ecd3333ef6bfde8256ee2fec62b03555b030348","observation_id":"7b3a1492-346a-4ccc-a09e-bda883479324","resolution":{"observed_at":"2026-05-18T13:31:24.850770Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-04T13:43:00.815429Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.25699","last_updated":"2026-07-21T17:19:46Z","snapshot_observed_at":"2026-08-04T13:42:58.327870Z","submitted_at":"2025-09-30T02:57:44Z","title":"AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T13:43:00.815429Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2509.25699"},"observation_digest":"sha256:6f72e92c3868a7b2c8dc4ebec10a971302d442a6d1aa7ab1839a15f66d70738c","observation_id":"3dc591b2-c7f2-4253-b2ae-e6989e6d9b2f","resolution":{"observed_at":"2026-08-04T13:43:00.815429Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2510.08804","last_updated":"2026-05-04T16:48:58Z","snapshot_observed_at":"2026-08-02T08:04:43.071958Z","submitted_at":"2025-10-09T20:35:23Z","title":"MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-18T08:28:59.161675Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2510.08804"},"observation_digest":"sha256:ce3d7ae2f3d429c7ae9b01e7e57c80fb173ea790e1f168d4e49fb2251752f36f","observation_id":"67512a19-106a-44f1-a649-3f290be54ab1","resolution":{"observed_at":"2026-05-18T08:31:06.886490Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2510.15946","last_updated":"2026-04-16T03:01:25Z","snapshot_observed_at":"2026-08-05T21:56:56.130024Z","submitted_at":"2025-10-10T03:08:30Z","title":"Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-18T08:41:27.958069Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2510.15946"},"observation_digest":"sha256:3236f2bb34c5e85fcb17bd427b7e98bde255c70e7ad6b41f1eb3c9f7ccb74447","observation_id":"39ad0f0d-48fc-4484-b852-9a8212a35b74","resolution":{"observed_at":"2026-05-18T08:42:30.095893Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-04T09:18:08.626627Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.16302","last_updated":"2026-06-02T13:42:15Z","snapshot_observed_at":"2026-08-07T14:34:29.258231Z","submitted_at":"2025-10-18T02:19:11Z","title":"DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T09:18:08.626627Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2510.16302"},"observation_digest":"sha256:0152380c1fbb8a26918683412e734669cf1e327ea52202a3de9f6418226a3546","observation_id":"fab63cfe-23f3-455d-9959-19548d86986b","resolution":{"observed_at":"2026-08-04T09:18:08.626627Z","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":"2026-08-04T07:54:08.335496Z","title":"Automatic chain of thought prompting in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.23509","last_updated":"2026-07-18T06:00:54Z","snapshot_observed_at":"2026-08-04T07:54:06.302346Z","submitted_at":"2025-10-27T16:47:15Z","title":"Logic-Guided Socially-aware Robot Navigation World Model","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T07:54:08.335496Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2510.23509"},"observation_digest":"sha256:387d09acbda50d31230ae1b3ee6ea0b03a98fb47af55dac0f596a67265f00f6d","observation_id":"d04d80d8-1bbc-41e9-97cc-52426f5b6723","resolution":{"observed_at":"2026-08-04T07:54:08.335496Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2511.01188","last_updated":"2026-04-25T08:12:03Z","snapshot_observed_at":"2026-07-06T22:34:43.577332Z","submitted_at":"2025-11-03T03:29:42Z","title":"ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-18T01:47:35.232468Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2511.01188"},"observation_digest":"sha256:59f513e942ad3b1075ed8c7b1d2c510c284ddc2beb4f1e815a7126bf56dff830","observation_id":"b998befa-621c-4240-87ef-73b3257ee457","resolution":{"observed_at":"2026-05-18T01:50:38.341567Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-03T20:57:35.195421Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.17731","last_updated":"2026-06-30T21:37:31Z","snapshot_observed_at":"2026-08-03T20:57:27.147869Z","submitted_at":"2025-11-21T19:30:24Z","title":"VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-03T20:57:35.195421Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2511.17731"},"observation_digest":"sha256:43a24e683631ec53e946214d2de053d85ed3b1340c17fc7ec09ed90e3129fcbc","observation_id":"c3991c43-4bab-4a47-bfb5-a70fe4a26d0e","resolution":{"observed_at":"2026-08-03T20:57:35.195421Z","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":"2026-08-04T06:40:28.151683Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.10739","last_updated":"2026-08-03T08:17:50Z","snapshot_observed_at":"2026-08-07T08:51:03.458621Z","submitted_at":"2025-12-11T15:26:28Z","title":"Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T06:40:28.151683Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2512.10739"},"observation_digest":"sha256:1023845be1d754231a2767c75ef778ed7e9f764600e49b3b766bcb896e86550b","observation_id":"8f8665b6-ba14-4e32-bf36-4f742ab48380","resolution":{"observed_at":"2026-08-04T06:40:28.151683Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2512.24329","last_updated":"2025-12-30T16:31:08Z","snapshot_observed_at":"2026-07-06T22:40:23.205898Z","submitted_at":"2025-12-30T16:31:08Z","title":"World model inspired sarcasm reasoning with large language model agents","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T18:54:28.446715Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2512.24329"},"observation_digest":"sha256:25fb4a2b1a3ed8232e1d940ee7ea0333bacb810e910323a650cdb7b89a43a403","observation_id":"5cc169c2-01c8-48b4-8441-0cfce5c98214","resolution":{"observed_at":"2026-05-16T18:58:19.157834Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2601.06993","last_updated":"2026-04-10T19:45:13Z","snapshot_observed_at":"2026-07-06T22:41:24.948774Z","submitted_at":"2026-01-11T17:07:47Z","title":"Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-16T14:51:26.368439Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2601.06993"},"observation_digest":"sha256:2925bda209c378a2f706837938b51452c5b2e33aa12e9c2e9a15f1272322a684","observation_id":"ae5417ea-a1ef-4181-8c00-2bb397d0af9f","resolution":{"observed_at":"2026-05-16T14:53:00.645804Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2603.10477","last_updated":"2026-04-08T07:03:59Z","snapshot_observed_at":"2026-08-03T13:46:05.416163Z","submitted_at":"2026-03-11T07:00:59Z","title":"PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T13:57:41.428695Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2603.10477"},"observation_digest":"sha256:dc0273c507cc99af62a80fe52cc545eae489a44248cfcab933cfd1103b90c6ef","observation_id":"7fbd80a2-4a03-4634-82e4-9aa009af75e5","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2604.03048","last_updated":"2026-04-03T13:56:39Z","snapshot_observed_at":"2026-07-06T22:52:20.577677Z","submitted_at":"2026-04-03T13:56:39Z","title":"Combining Static Code Analysis and Large Language Models Improves Correctness and Performance of Algorithm Recognition","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-13T19:31:51.501833Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2604.03048"},"observation_digest":"sha256:68ac7d462919f1bf42f575d810e58f067ca46fdbd8384546a0b0a190c5251c08","observation_id":"063a36a9-a74f-45f3-a162-6af1eaf86141","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2604.05497","last_updated":"2026-04-07T06:41:05Z","snapshot_observed_at":"2026-07-06T22:54:12.531121Z","submitted_at":"2026-04-07T06:41:05Z","title":"Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T19:06:45.417233Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2604.05497"},"observation_digest":"sha256:34caf69eb0e78a9af274b003ee8288b9a230cbf98160a3dcf12f498a4b7957ad","observation_id":"9c9f890b-c645-4f3f-bb41-1eefe4b57d81","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2604.09741","last_updated":"2026-04-09T23:27:46Z","snapshot_observed_at":"2026-08-03T03:51:17.779334Z","submitted_at":"2026-04-09T23:27:46Z","title":"ExecTune: Effective Steering of Black-Box LLMs with Guide Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-10T16:55:34.091812Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2604.09741"},"observation_digest":"sha256:b5ecf620e800e2964789dba7b9d452692d17faae9882636654157f785f1d57c4","observation_id":"e072ade1-de4d-464c-922f-399e5b3cdf65","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2604.15385","last_updated":"2026-04-16T03:25:28Z","snapshot_observed_at":"2026-07-06T23:02:58.361418Z","submitted_at":"2026-04-16T03:25:28Z","title":"Prompt-Driven Code Summarization: A Systematic Literature Review","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-10T11:35:12.299549Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2604.15385"},"observation_digest":"sha256:c3d7802677943bc5725896b6cc8047bb28525fbe02e3ee1e30a049f555890123","observation_id":"97b54fff-1dd2-4fbc-a01f-3d0ac1876914","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2604.15390","last_updated":"2026-04-27T16:12:19Z","snapshot_observed_at":"2026-07-06T23:02:58.361418Z","submitted_at":"2026-04-16T06:08:07Z","title":"Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T11:23:55.120266Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2604.15390"},"observation_digest":"sha256:9c97c39f73f2b6356efc4a7b9675e9cce0a1e093b9a50f749105782a43ad155e","observation_id":"b137ab0d-25ed-45e4-874b-e5ffcf629bf7","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.01368","last_updated":"2026-05-02T10:25:40Z","snapshot_observed_at":"2026-07-06T23:14:38.379875Z","submitted_at":"2026-05-02T10:25:40Z","title":"Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-09T14:51:10.807902Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.01368"},"observation_digest":"sha256:ef939aa99b46a6fac3f7a873874fc5a9668d355d7806134474cbd8eb19889211","observation_id":"faccf587-eeb4-422b-875c-6f85d4bdcc67","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.07649","last_updated":"2026-05-11T07:04:50Z","snapshot_observed_at":"2026-07-06T23:20:01.856093Z","submitted_at":"2026-05-08T12:17:56Z","title":"Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-11T01:52:46.303378Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.07649"},"observation_digest":"sha256:073dbbca46ca008b65aa944654a69310326567d1c8e5e51bd0fddd5c5c352420","observation_id":"99ca5cb4-75ed-4fda-aeac-a6e30e36e707","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.07649","last_updated":"2026-05-11T07:04:50Z","snapshot_observed_at":"2026-07-06T23:20:01.856093Z","submitted_at":"2026-05-08T12:17:56Z","title":"Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-12T03:35:16.193455Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.07649"},"observation_digest":"sha256:79d43b79289c5516dc646d28b39d5dc9a3836652586d9fed45356aea06a6ef11","observation_id":"3d30b35c-42e3-467b-9c21-8b2b8760648f","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.09492","last_updated":"2026-05-20T15:55:09Z","snapshot_observed_at":"2026-07-06T23:21:35.327066Z","submitted_at":"2026-05-10T11:57:39Z","title":"APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-12T05:22:25.475956Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.09492"},"observation_digest":"sha256:900402b28f938d4c72a7f98056c856c8106aa5858daf8134c0d4dbbac3128408","observation_id":"da78dbf8-e6ef-4b99-a5fc-dac75e6ea907","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.13037","last_updated":"2026-05-13T05:46:29Z","snapshot_observed_at":"2026-08-05T09:07:24.966960Z","submitted_at":"2026-05-13T05:46:29Z","title":"MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-14T19:48:53.213389Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.13037"},"observation_digest":"sha256:8bd649ebfb80c6c155de21f308a1b36aec1655e3346531dd976cf67785e800b7","observation_id":"fcc1b3e2-43d1-46eb-973d-c85b0bc8069e","resolution":{"observed_at":"2026-05-16T10:39:17.100641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.17088","last_updated":"2026-05-16T17:10:13Z","snapshot_observed_at":"2026-08-05T01:36:41.022279Z","submitted_at":"2026-05-16T17:10:13Z","title":"ACIL: Auto Chain of Thoughts for In-Context Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-20T15:25:40.105468Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.17088"},"observation_digest":"sha256:f044c6b1ea60be5a43d5a105b79257d71d125bc39cec0e3e3aacd678037c767b","observation_id":"a115d476-ee1d-4564-83a2-3f71797a76ff","resolution":{"observed_at":"2026-05-20T15:28:25.558016Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.17152","last_updated":"2026-05-16T20:56:15Z","snapshot_observed_at":"2026-08-02T18:07:50.795904Z","submitted_at":"2026-05-16T20:56:15Z","title":"Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-20T14:33:36.100966Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.17152"},"observation_digest":"sha256:7afc570c1fe77a4f2bc2ffd91fd6aefcd838bc644c60cfd94ba7ad536938d735","observation_id":"57cb7e72-0f5a-4913-88fc-f74a20f87c81","resolution":{"observed_at":"2026-05-20T14:38:21.864393Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.21856","last_updated":"2026-05-21T01:06:19Z","snapshot_observed_at":"2026-08-02T10:22:40.169491Z","submitted_at":"2026-05-21T01:06:19Z","title":"The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-22T08:05:42.212459Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.21856"},"observation_digest":"sha256:99e0c6f7ab1dd4f8e8812d719e57d06797b6613c91ff564ca0807ec4848ca3f4","observation_id":"71d9cdc2-77d1-45b9-bd1f-546a35a2f658","resolution":{"observed_at":"2026-05-22T08:06:15.316558Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2605.27959","last_updated":"2026-05-28T03:13:45Z","snapshot_observed_at":"2026-07-06T23:37:36.244456Z","submitted_at":"2026-05-27T04:52:42Z","title":"ROVER: Routing Object-Centric Visual Evidence for Grounded Multi-Image Reasoning","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-29T13:41:44.049230Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2605.27959"},"observation_digest":"sha256:a134ec63f1e7eeafcf9bd479529f59540ac9a1d8a5211a625d10b824376e6d04","observation_id":"9607ac2d-6053-404c-9d81-6ae9ecb5bbc7","resolution":{"observed_at":"2026-06-29T13:43:28.670154Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2606.00875","last_updated":"2026-05-30T20:15:01Z","snapshot_observed_at":"2026-08-02T12:14:21.831646Z","submitted_at":"2026-05-30T20:15:01Z","title":"IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-06-28T18:42:00.845492Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2606.00875"},"observation_digest":"sha256:e59510d5f0d9c668ab1c05a931670e0f931fd79699fff33971abaabcc03e7912","observation_id":"d2962fe9-398f-404e-b55a-3b0efd7894da","resolution":{"observed_at":"2026-06-28T18:42:28.909259Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-02T11:29:26.920670Z","title":"Automatic chain of thought prompting in large language models.arXiv preprint arXiv:2210.03493, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.14502","last_updated":"2026-07-23T02:50:21Z","snapshot_observed_at":"2026-08-07T01:44:45.965983Z","submitted_at":"2026-06-12T14:33:55Z","title":"From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-02T11:29:26.920670Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2606.14502"},"observation_digest":"sha256:f8d499fd4013f28968835d49481cec05570dc1d15a7326b400c027e63965d265","observation_id":"7dd0db96-659d-46d8-bfdb-5ac946f9c137","resolution":{"observed_at":"2026-08-02T11:29:26.920670Z","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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2606.18844","last_updated":"2026-06-17T09:24:19Z","snapshot_observed_at":"2026-08-07T12:46:02.610951Z","submitted_at":"2026-06-17T09:24:19Z","title":"Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T21:30:30.336251Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2606.18844"},"observation_digest":"sha256:739e84582b6b6a4562526adaf206f5ba417ef844534cdb2dfdc3123cb1f22dca","observation_id":"83c9ca52-6b66-4fc9-990e-95b4e067d84b","resolution":{"observed_at":"2026-07-04T00:09:14.130570Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2606.21119","last_updated":"2026-06-19T05:45:48Z","snapshot_observed_at":"2026-07-06T23:56:12.374739Z","submitted_at":"2026-06-19T05:45:48Z","title":"MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T14:41:36.082441Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2606.21119"},"observation_digest":"sha256:113fa0a81cf124250353db63b30f035e8a583e3769811a21ad07c83194ff3d5e","observation_id":"43d48886-af9a-46aa-b2ad-e0260b4c3a96","resolution":{"observed_at":"2026-07-04T06:19:37.468978Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2606.23595","last_updated":"2026-06-22T17:02:09Z","snapshot_observed_at":"2026-08-02T13:00:59.578833Z","submitted_at":"2026-06-22T17:02:09Z","title":"SPIRAL: Learning to Search and Aggregate","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-06-26T08:29:22.303745Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2606.23595"},"observation_digest":"sha256:143cbb5a2a77c5a7985736373e091e9b34eac161ecd497e0ef2a17ae84ee224c","observation_id":"59b29e7b-5db4-4d2a-9604-33fe40506483","resolution":{"observed_at":"2026-07-04T10:49:46.161920Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2210.03493","doi":"10.48550/arxiv.2210.03493","metadata_source":"pith","pith_arxiv_id":"2210.03493","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Automatic Chain of Thought Prompting in Large Language Models","venue":"cs.CL","work_id":"c3739d30-4ba4-47ca-a8c7-41daf3b51e71","year":2022},"citing_paper":{"arxiv_id":"2607.01511","last_updated":"2026-07-01T22:17:39Z","snapshot_observed_at":"2026-08-02T17:13:50.983394Z","submitted_at":"2026-07-01T22:17:39Z","title":"Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-03T20:04:19.110148Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2607.01511"},"observation_digest":"sha256:10f7b792355ce2f2719c13b9ec931a8b947c3fe490361f34776211d6fdac46ed","observation_id":"7f4f432a-1484-433d-9271-447a47d1c6a7","resolution":{"observed_at":"2026-07-03T20:08:54.894026Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-07-12T04:17:40.198357Z","title":"arXiv preprint arXiv:2210.03493 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03184","last_updated":"2026-07-03T10:42:34Z","snapshot_observed_at":"2026-08-05T15:07:18.952856Z","submitted_at":"2026-07-03T10:42:34Z","title":"BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception","version":1},"reference_index":203,"source":"arxiv_source","source_observed_at":"2026-07-12T04:17:40.198357Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2607.03184"},"observation_digest":"sha256:2bd8921f201963119f6dbff6fbcd940e15f23721262cca1b7f4da79eaadb18a0","observation_id":"fe428716-99ba-4bb8-913d-5295f2e1fe94","resolution":{"observed_at":"2026-07-12T04:17:40.198357Z","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":"2026-08-01T12:46:31.056543Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.26593","last_updated":"2026-07-29T08:14:00Z","snapshot_observed_at":"2026-08-06T07:03:38.346418Z","submitted_at":"2026-07-29T08:14:00Z","title":"ASARL: Autonomous Social-Aware Relevance Learning for QQ Search","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T12:46:31.056543Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2607.26593"},"observation_digest":"sha256:aff157e919d4136f5580b72e082dc81a70bc08541fad3e70bebac315aaa0e07a","observation_id":"e5fb3f6d-0709-42e7-9045-25bbcf736d3e","resolution":{"observed_at":"2026-08-01T12:46:31.056543Z","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":"2026-08-04T02:32:06.233650Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00014","last_updated":"2026-06-24T16:14:12Z","snapshot_observed_at":"2026-08-06T23:11:31.183195Z","submitted_at":"2026-06-24T16:14:12Z","title":"CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T02:32:06.233650Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2608.00014"},"observation_digest":"sha256:de2f76ceebc6d72f875cddc4797e47eb59a926264d59fc608fb96fff27a047a4","observation_id":"e0ce3842-d42d-42e6-aa0c-1a503e655d6b","resolution":{"observed_at":"2026-08-04T02:32:06.233650Z","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":"2026-08-05T17:02:19.929652Z","title":"arXiv preprint arXiv:2210.03493 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03550","last_updated":"2026-08-04T12:22:31Z","snapshot_observed_at":"2026-08-07T16:48:33.801610Z","submitted_at":"2026-08-04T12:22:31Z","title":"Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-05T17:02:19.929652Z"},"links":{"cited_paper":"/paper/2210.03493","citing_paper":"/paper/2608.03550"},"observation_digest":"sha256:12c9f2ecabcf3156c9ec597fb73b24313ed30a26cf5e6bf9558a6e4f2a4ac1a0","observation_id":"3f745c18-714c-45c4-a704-e3f248f7a4c6","resolution":{"observed_at":"2026-08-05T17:02:19.929652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2210.03493/citation-record","integrity":"/paper/2210.03493/integrity","json":"/paper/2210.03493/citation-record.json","paper":"/paper/2210.03493"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c8c76fe6-9f18-49cf-b7c5-eb092bf80d60","year":2020},"citing_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},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:27189e16adb0eb133427ce90530f624a5fc54582f7cdad70ef838d8f828e0565","observation_id":"78377240-30c1-4c67-8c0f-8a93afd78215","resolution":{"observed_at":"2026-05-16T10:39:17.099913Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"546635ca-ac36-4775-b6e0-1a7e0066ad49","year":2020},"citing_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},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:3c8b7de61956086d00670640fb637a78317c5d3efeda8690aa4a0a05170f6ab4","observation_id":"61b6134d-5f13-458d-8993-32e66520e3ab","resolution":{"observed_at":"2026-05-16T10:39:17.090143Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08239","last_updated":"2022-02-10T16:30:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-20T15:44:37Z","title":"LaMDA: Language Models for Dialog Applications","version":3},"cited_work":{"arxiv_id":"2201.08239","doi":"10.48550/arxiv.2201.08239","metadata_source":"pith","pith_arxiv_id":"2201.08239","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LaMDA: Language Models for Dialog Applications","venue":"cs.CL","work_id":"1b66d0a5-f6ae-4332-8025-c662dc64b238","year":2022},"citing_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},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2201.08239","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:d86d48ab8da5fc605a58e1bf492e419889e31f7fba85d130db8aed2f171a16b8","observation_id":"a16cecf2-7d00-4e27-b127-f6233eb048df","resolution":{"observed_at":"2026-05-16T10:39:17.073916Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02311","last_updated":"2022-10-05T06:02:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-05T16:11:45Z","title":"PaLM: Scaling Language Modeling with Pathways","version":5},"cited_work":{"arxiv_id":"2204.02311","doi":"10.48550/arxiv.2204.02311","metadata_source":"pith","pith_arxiv_id":"2204.02311","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PaLM: Scaling Language Modeling with Pathways","venue":"cs.CL","work_id":"a94f3ef7-2c49-4445-93fe-6ec16aafd966","year":2022},"citing_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},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2204.02311","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:d69dca8a5c0d080e686757d71c7d65104101aa7f222250a268be88d7f745e518","observation_id":"01544e98-ef5c-4975-80d8-32e7211ad9a3","resolution":{"observed_at":"2026-05-16T10:39:17.079695Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-21T12:25:21.411055+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T12:25:21.411055+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11916","last_updated":"2023-01-29T05:14:17Z","snapshot_observed_at":"2026-08-07T04:02:08.445660Z","submitted_at":"2022-05-24T09:22:26Z","title":"Large Language Models are Zero-Shot Reasoners","version":4},"cited_work":{"arxiv_id":"2205.11916","doi":"10.48550/arxiv.2205.11916","metadata_source":"pith","pith_arxiv_id":"2205.11916","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Large Language Models are Zero-Shot Reasoners","venue":"cs.CL","work_id":"d9b7eb1a-7165-46ff-9f06-d2f0b9d6f95d","year":2022},"citing_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},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2205.11916","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:f3e06363414499601b2b00d7e696fc2025743b2da3c5f4b53afaf95d7fa29f60","observation_id":"6e74ff5b-9170-4aad-a2a3-e4c3a8b57a37","resolution":{"observed_at":"2026-05-16T10:39:17.067719Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/d15-1202","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: 10.18653/v1/D15-1202","venue":null,"work_id":"26ad3887-1b22-4ea0-9e93-c5962e1965fe","year":2015},"citing_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},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:5a2da6f72d9c3b4ebfbdd014753347354e3ef8bd79b4c75a4edee3d30ea09d51","observation_id":"5d6361cd-52d8-47b2-9fb5-6ea44c2f9d2b","resolution":{"observed_at":"2026-05-16T10:39:17.051513Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-20T08:52:34.076337+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T08:52:34.076337+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/n19-1421","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: 10.18653/v1/n19-1421","venue":null,"work_id":"628930d3-897c-43a2-8d6d-589da959e066","year":2019},"citing_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},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:7452b286a0fe8db00160b4e5e988982431a85ae95892e4d361ba47ac9f8107ab","observation_id":"4369558d-fb68-42b4-a557-410bbc1cf88a","resolution":{"observed_at":"2026-05-16T10:39:17.053555Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-01T09:08:04.850966+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T09:08:04.850966+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":"2110.14168","doi":"10.1002/j.1545-","metadata_source":"pith","pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Verifiers to Solve Math Word Problems","venue":"cs.LG","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","year":2021},"citing_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},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:52715682535d7a4327cef7375916ef9cc968a9cc2d0d9ff306c5bcb9f3e5043e","observation_id":"15cba7e9-5cae-41ab-b344-f69c52248ead","resolution":{"observed_at":"2026-05-16T10:39:17.076657Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/p17-1015","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems","venue":null,"work_id":"6acf4e07-6b69-4080-baf3-2a06f8660f6e","year":2017},"citing_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},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:d1fae0d609484c11d8b6fe908e3b810dfb694aa255ba2ade207fd501f6be8223","observation_id":"d5266b88-e3b0-411a-99bc-eda2753d1d41","resolution":{"observed_at":"2026-05-16T10:39:17.055499Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-23T16:25:22.78924+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T16:25:22.78924+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07191","last_updated":"2021-04-15T06:11:12Z","snapshot_observed_at":"2026-08-07T13:11:17.300265Z","submitted_at":"2021-03-12T10:23:47Z","title":"Are NLP Models really able to Solve Simple Math Word Problems?","version":2},"cited_work":{"arxiv_id":"2103.07191","doi":"10.18653/v1/2021.naacl-main.168","metadata_source":"doi_reference","pith_arxiv_id":"2103.07191","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Are NLP Models really able to Solve Simple Math Word Problems?","venue":"cs.CL","work_id":"01c9a44a-5ebf-4209-a628-5f1fd19d2103","year":2021},"citing_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},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2103.07191","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:e1e95804e54d45398d1f826e47e7225bb04aaa7e2705e8775269d966f99480ae","observation_id":"d6d8fdcb-e6b2-46ec-b286-b5501d8f33a3","resolution":{"observed_at":"2026-05-16T17:30:49.973393Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1162/tacl_a_00370","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL https://doi.org/10.1162/tacl_a_00370","venue":"Transactions of the Association for Computational Linguistics","work_id":"8da0da08-938d-4317-bbad-6089fbdca8d9","year":2021},"citing_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},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:f4675bb7b5f88e0f819b24f56e25062dce575c0bd0ab56295746ca62e5023617","observation_id":"55b454aa-f389-4220-b616-958dac852bbe","resolution":{"observed_at":"2026-05-16T10:39:17.026516Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.14465","last_updated":"2022-05-20T13:52:54Z","snapshot_observed_at":"2026-08-05T03:08:42.211484Z","submitted_at":"2022-03-28T03:12:15Z","title":"STaR: Bootstrapping Reasoning With Reasoning","version":2},"cited_work":{"arxiv_id":"2203.14465","doi":"10.48550/arxiv.2203.14465","metadata_source":"pith","pith_arxiv_id":"2203.14465","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Wu, andN.D.Goodman","venue":"cs.LG","work_id":"3339dfe0-b229-41ec-a6e0-8f601080eb97","year":2022},"citing_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},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2203.14465","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:acb28343fdbaca6513f5e8dfa8796b173e69be4c656aef2b31561684fa4b407d","observation_id":"a34c375e-2e54-4c94-b28c-dcb42d934fe0","resolution":{"observed_at":"2026-05-16T10:39:17.064171Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-02T03:08:15.019726+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T03:08:15.019726+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2205.10625","doi":"10.48550/arxiv.2205.10625","metadata_source":"pith","pith_arxiv_id":"2205.10625","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Least-to-Most Prompting Enables Complex Reasoning in Large Language Models","venue":"cs.AI","work_id":"7e58c111-4666-4996-b5ad-1c8efd433083","year":2022},"citing_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},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2205.10625","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:aa1cd7f3b0cefdc476ebec94ce5fb7c5c0c7b02b8fe415d8bb0fc7cabe8494d8","observation_id":"14822204-4402-4ea9-bb25-879d21f35b53","resolution":{"observed_at":"2026-05-16T10:39:17.070960Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Learning to retrieve prompts for in-context learning","venue":null,"work_id":"8adf9cb2-8bf3-4454-8a12-f87eab45b607","year":2022},"citing_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},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:640568b364c86a1340f946c460516e52e897424b7538dc9434b5a69ee4e57143","observation_id":"cad15272-b241-4b75-82d3-ada28f232340","resolution":{"observed_at":"2026-05-16T10:39:17.092258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.naacl-main.191","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URL https://aclanthology.org/2022.naacl-main.191","venue":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","work_id":"296fa0b3-6893-4f3b-91ed-fdc7880ae1ab","year":2022},"citing_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},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:ae79b2ee5139cefd94988777e624633e36563641dfff108876349947aa1c7d2b","observation_id":"16eae670-7b74-42d0-885f-66d9e356d3ba","resolution":{"observed_at":"2026-05-16T10:39:17.029651Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.01975","last_updated":"2022-09-05T14:01:15Z","snapshot_observed_at":"2026-07-06T13:49:00.465047Z","submitted_at":"2022-09-05T14:01:15Z","title":"Selective Annotation Makes Language Models Better Few-Shot Learners","version":1},"cited_work":{"arxiv_id":"2209.01975","doi":"10.48550/arxiv.2209.01975","metadata_source":"arxiv_reference","pith_arxiv_id":"2209.01975","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv:2209.01975","venue":"arXiv (Cornell University)","work_id":"77bc620e-84a7-4177-a57d-33fa8c35601d","year":null},"citing_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},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2209.01975","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:f12e493d3acc75709494d0f50a7edd979c8e8e8f215f815df8292c2a30958ee3","observation_id":"a095a952-1a48-4339-934e-59894a9998a3","resolution":{"observed_at":"2026-05-16T10:39:17.082652Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.acl-long.244","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Cross-Task Generalization via Natural Language Crowdsourcing Instructions","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","work_id":"29367bfd-9b7c-4428-856b-8a8298f8379b","year":2022},"citing_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},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:73797050a5a67cecf167ece9df28c81a1f50af0955a63a40cee1e0e152023fe7","observation_id":"4a80c85a-423c-472e-beea-c560e17db3f8","resolution":{"observed_at":"2026-05-16T10:39:17.032162Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-23T02:23:09.600002+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T02:23:09.600002+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"11 Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer","venue":null,"work_id":"a30a169a-ae10-472a-b585-d12869491973","year":2021},"citing_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},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:6fb269fb9d0528f298a72e8c4aa278d6a3de4adf3b68cebb89063331f2c97612","observation_id":"0ed3aa71-27fa-42e4-a6f1-0194e61f5bce","resolution":{"observed_at":"2026-05-16T10:39:17.094228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2021.emnlp-main.564","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Surface form competition: Why the highest probability answer isn’t always right","venue":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","work_id":"cb353b8c-fb35-4381-b456-54fa644a663c","year":2021},"citing_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},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:84a826f62f801b95c9c36c91829e0944a4583b3b72e3994916414d12ebfbcd35","observation_id":"dce2a4c4-d2e2-4a8d-a330-76d5dbef7e93","resolution":{"observed_at":"2026-05-16T10:39:17.034974Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-25T13:53:55.687634+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T13:53:55.687634+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.acl-long.365","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Noisy channel language model prompting for few-shot text classification","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","work_id":"50e272bc-31d7-4b45-ba5c-18fe69d506dd","year":2022},"citing_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},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:ac6074d66a4db808a665e28038328b33c744de7d72e3bb76b4cb305909e85800","observation_id":"79bc7c2f-bd1e-467f-9e24-4b4a790de141","resolution":{"observed_at":"2026-05-16T10:39:17.037800Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-25T13:53:57.714099+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T13:53:57.714099+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.acl-long.556","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","work_id":"731a7674-9850-4855-995a-4b938348fa34","year":2022},"citing_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},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:cfcac32d15ea232448d2a17616bf943a04305f679f1c3369d83ce4e1b96f2b2e","observation_id":"a864394c-a9c3-401c-a945-43fa44009039","resolution":{"observed_at":"2026-05-16T10:39:17.040311Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-13T09:49:37.708179+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T09:49:37.708179+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2022.naacl-main.167","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Do Prompt-Based Models Really Understand the Meaning of Their Prompts?","venue":"Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","work_id":"e18eb80d-ba0d-4dc8-926e-8b75f80fc433","year":2022},"citing_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},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:ba1b4ef30c7a56e403ff6cc21ace27555230c22d73f3fbf80f5de447c7e9e9a2","observation_id":"3e9995a3-23db-4c06-a4ab-571e91463dac","resolution":{"observed_at":"2026-05-16T10:39:17.042397Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12837","last_updated":"2022-10-20T14:04:10Z","snapshot_observed_at":"2026-08-01T15:23:39.998892Z","submitted_at":"2022-02-25T17:25:19Z","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","version":2},"cited_work":{"arxiv_id":"2202.12837","doi":"10.48550/arxiv.2202.12837","metadata_source":"pith","pith_arxiv_id":"2202.12837","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","venue":"cs.CL","work_id":"d2b537df-825d-4427-9575-fc0664c0962a","year":2022},"citing_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},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2202.12837","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:45c2ef040ce2f046c4b2ab20a1a5d125267ce5995af40b10360c39a6e67c5ae7","observation_id":"649e51d9-3700-481a-b752-1155473322a6","resolution":{"observed_at":"2026-05-16T10:39:17.085777Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-21T18:22:50.486187+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T18:22:50.486187+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/d19-1410","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T01:56:41.012481Z","title":"Sentence- BERT : Sentence Embeddings using S iamese BERT -Networks","venue":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","work_id":"cf07889b-1f35-4d81-9514-4ad3ed223c57","year":2019},"citing_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},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:f8cf9af760b1cf3d07ed7370d156925e865eccac22abc380838d52e18a975163","observation_id":"1bdde2f0-57ee-4624-ad8c-d9776f888496","resolution":{"observed_at":"2026-05-16T10:39:17.044753Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-06T21:38:19.14814+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-06T21:38:19.14814+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3115/v1/d14-1058","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (","venue":null,"work_id":"a8e2452b-41b4-466d-b9d2-9e52b6e375c3","year":2014},"citing_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},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:630c7ef4a44be2652d481307b82427479841bd733e1e490a5a291413728f56f2","observation_id":"4196b5f2-8c50-4327-9047-2f95709cbeb8","resolution":{"observed_at":"2026-05-16T10:39:17.046985Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-20T08:52:26.576517+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T08:52:26.576517+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1162/tacl_a_00160","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URLhttps://aclanthology.org/Q15-1042/","venue":"Transactions of the Association for Computational Linguistics","work_id":"0d9751a6-a76c-430b-bce8-e65836e04f13","year":2015},"citing_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},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:de692be122d71e3d25deea1ace48478a1bea5a45201b55e45c2cf9cce8a45847","observation_id":"d0b8cbc0-241b-4ee9-abf9-d474e6933642","resolution":{"observed_at":"2026-05-16T10:39:17.019842Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.02155","last_updated":"2022-03-04T07:04:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-03-04T07:04:42Z","title":"Training language models to follow instructions with human feedback","version":1},"cited_work":{"arxiv_id":"2203.02155","doi":"10.1007/s00354-022-00198-8","metadata_source":"pith","pith_arxiv_id":"2203.02155","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Training language models to follow instructions with human feedback","venue":"cs.CL","work_id":"52aff42f-4fa9-4fcf-bdb3-1459b9bebf65","year":2022},"citing_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},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2203.02155","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:b7746315451794b058c62d2bb5671419ee00d01ec7c91aa628705c1a11156934","observation_id":"be7e43e4-e61a-4eb1-8740-c55af5758ed2","resolution":{"observed_at":"2026-05-16T10:39:17.058517Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":"2107.03374","doi":"10.48550/arxiv.2107.03374","metadata_source":"pith","pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Evaluating Large Language Models Trained on Code","venue":"cs.LG","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","year":2021},"citing_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},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:d8587d2a946752a534211ee39dd6c1a4d97901510a9a6c1917cc3563e9610bbf","observation_id":"d449c4a6-eda1-407a-a4b3-badd8195d43d","resolution":{"observed_at":"2026-05-16T10:39:17.061258Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/n16-1136","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"MAWPS : A math word problem repository","venue":null,"work_id":"0b604263-7fbc-4c6a-b852-0bbc31623809","year":2016},"citing_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},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:3a8700fe971a8e7a8eafc647ea302d2043d36bfd3ad0f9d2099ec4cf563b1c5e","observation_id":"281a9099-095a-4016-98d0-35184354b4a7","resolution":{"observed_at":"2026-05-16T10:39:17.049379Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-20T08:52:28.026057+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T08:52:28.026057+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"According to the results in Table 5, shuffling questions has the least performance reduction (91.7%→ 73.8%)","venue":null,"work_id":"03af1604-8e91-4281-a57f-407e343b987a","year":2022},"citing_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},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:8405407ad7d1ff042a84d1b67150ba8d673b37aa06bd8876567b357c4b26f171","observation_id":"7d62946d-48f2-4df0-b27d-f47245c364a0","resolution":{"observed_at":"2026-05-16T10:39:17.096169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"(∆ is computed by the difference of largest and smallest values","venue":null,"work_id":"712704e6-0ecb-4063-baf8-bb507902da15","year":2015},"citing_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},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:3645b1203410d909c7c6af544451f8e3f5114c9e0385d4bf3af8229ed8785d1c","observation_id":"9e78c3c5-6f14-42b2-ae3f-057f41686677","resolution":{"observed_at":"2026-05-16T10:39:17.098079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"\\n” for separating the reasoning steps, the rule can be easily implemented by counting the “\\n","venue":null,"work_id":"5be998c9-2530-4736-870b-e27150a794e7","year":2022},"citing_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},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-16T10:39:16.997741Z"},"links":{"citing_paper":"/paper/2210.03493"},"observation_digest":"sha256:970cca968eef6d6f5ba18af4ad1620e859afae319b1efc4127ef40befafef36c","observation_id":"01fe9dd7-7a54-404b-bc8e-ae99c23be9e9","resolution":{"observed_at":"2026-05-16T10:39:17.088156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2210.03493","last_updated":"2022-10-07T12:28:21Z","latest_version":1,"primary_category":"cs.CL","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"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":18,"parse_uncertain":0,"unresolved":2,"verified_exact":7,"verified_fuzzy":5},"total_outbound_references":32},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 100 inbound Pith citation observations for arXiv:2210.03493."}