{"as_of":"2026-08-05T21:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9404f34481eed3d9e905005b184a892138c226e6dc261ba8505568007d6a1d25","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T14:40:21.583101Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T09:56:36.860369Z","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":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"cited_work":{"arxiv_id":"2606.02113","doi":"10.48550/arxiv.2606.02113","metadata_source":"pith","pith_arxiv_id":"2606.02113","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","venue":"cs.CL","work_id":"5bfd3c0c-f238-46ea-a18f-3c1bd02e8077","year":2026},"citing_paper":{"arxiv_id":"2606.03889","last_updated":"2026-06-05T09:38:21Z","snapshot_observed_at":"2026-08-02T13:33:31.496138Z","submitted_at":"2026-06-02T16:51:24Z","title":"RealClawBench: Live OpenClaw Benchmarks from Real Developer-Agent Sessions","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-06-28T09:56:36.860369Z"},"links":{"cited_paper":"/paper/2606.02113","citing_paper":"/paper/2606.03889"},"observation_digest":"sha256:1975d5e8b1962808c96435366cb5f9207877a4238dec70b328ecc384550d2459","observation_id":"7c4c5b44-b8a2-4d3f-9901-babc99db77f9","resolution":{"observed_at":"2026-06-28T10:01:52.590703Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2606.02113/citation-record","integrity":"/paper/2606.02113/integrity","json":"/paper/2606.02113/citation-record.json","paper":"/paper/2606.02113"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2505.13388","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T23:06:20.548395Z","title":"Miranda, Hanyang Zhao, Mohammad Rifqi Farhansyah, Garry Kuwanto, Derry Wijaya, and Genta Indra Winata","venue":null,"work_id":"5392a515-f4de-4825-aaca-5ddece59ac6e","year":2025},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:02f4a5bd1ae6d0e5cbed24b839081239827466885534126dbb620c51c5b1d39c","observation_id":"4ae6c1e7-fad5-4de1-90c8-13d89c3c5446","resolution":{"observed_at":"2026-07-01T23:06:20.551043Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.08606","last_updated":"2025-07-25T16:55:43Z","snapshot_observed_at":"2026-07-06T20:35:30.739339Z","submitted_at":"2025-02-12T17:52:47Z","title":"Distillation Scaling Laws","version":2},"cited_work":{"arxiv_id":"2502.08606","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.08606","snapshot_observed_at":"2026-07-10T10:27:02.464784Z","title":"arXiv preprint arXiv:2502.08606 , year=","venue":"cs.LG","work_id":"a58622e0-207e-45e8-ae59-ce47bcd7c1a8","year":2025},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2502.08606","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:1d46e62644d0fae76c4c688cbbdaa5ded5a8072d730e0821c78d663d721022af","observation_id":"808a10a8-30b8-44f4-979b-5d8fad005d58","resolution":{"observed_at":"2026-07-01T23:06:20.492970Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:aedcdd4f4a6ff05d19a71c164d3b9f612047eae8c19869405060aa5a9c8e0685","observation_id":"9f9e72e2-5555-44cf-8ce9-38574fba3712","resolution":{"observed_at":"2026-07-01T23:06:20.532810Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+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":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-04T15:46:25.710484Z","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":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:e33f6dc13d40babba05ab55d56fe7d5222d63ea6454341379802f2421ec3df73","observation_id":"dd66c078-fe3a-462f-8955-428831c3e560","resolution":{"observed_at":"2026-07-01T23:06:20.537818Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.06070","last_updated":"2023-12-09T05:57:46Z","snapshot_observed_at":"2026-07-06T15:40:50.807376Z","submitted_at":"2023-06-09T17:44:31Z","title":"Mind2Web: Towards a Generalist Agent for the Web","version":3},"cited_work":{"arxiv_id":"2306.06070","doi":"10.48550/arxiv.2306.06070","metadata_source":"pith","pith_arxiv_id":"2306.06070","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mind2Web: Towards a Generalist Agent for the Web","venue":"cs.CL","work_id":"e26f5a00-c007-439d-83f6-7900f5687b6b","year":2023},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2306.06070","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:de799af8b00e999aea4b1f0dde1de74630863a552a0b3904e6960a0698bcbcf9","observation_id":"3f39b1b4-ded4-4070-8a1b-a2fb37d013ba","resolution":{"observed_at":"2026-07-01T23:06:20.541844Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-12T03:19:29.437492+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T03:19:29.437492+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07718","last_updated":"2024-07-23T06:19:28Z","snapshot_observed_at":"2026-08-02T12:09:24.340284Z","submitted_at":"2024-03-12T14:58:45Z","title":"WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?","version":5},"cited_work":{"arxiv_id":"2403.07718","doi":"10.48550/arxiv.2403.07718","metadata_source":"pith","pith_arxiv_id":"2403.07718","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?","venue":"cs.LG","work_id":"5ac27d9e-4522-46f8-985e-0e4f73130803","year":2024},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2403.07718","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:9b7d1af0fab5fd592e89d29ee17eacd3b10a7a3b652651ceac92eb4069c50f38","observation_id":"6b6c7bd1-eb96-4875-9c45-fd7ca5f451fc","resolution":{"observed_at":"2026-07-01T23:06:20.567771Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.17746","last_updated":"2025-10-03T01:55:55Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-23T17:57:55Z","title":"Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains","version":2},"cited_work":{"arxiv_id":"2507.17746","doi":"10.48550/arxiv.2507.17746","metadata_source":"pith","pith_arxiv_id":"2507.17746","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains","venue":"cs.LG","work_id":"805a846c-dae9-4375-abd8-a86dc6934496","year":2025},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2507.17746","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:16f8d8fa4a59bc2528154c22a7b16b89d4d8b0700cf86c8c20f8e55ac3cade49","observation_id":"82041640-7447-4d81-9384-28a5337c6b9f","resolution":{"observed_at":"2026-07-01T23:06:20.512563Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-23T21:53:02.879284+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T21:53:02.879284+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.00846","last_updated":"2026-07-07T15:26:45Z","snapshot_observed_at":"2026-08-05T11:11:40.366021Z","submitted_at":"2026-01-31T18:20:45Z","title":"Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis","version":2},"cited_work":{"arxiv_id":"2602.00846","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2602.00846","snapshot_observed_at":"2026-07-08T02:18:41.255312Z","title":"Omni-rrm: Advancing omni reward modeling via automatic rubric-grounded preference synthesis.CoRR, abs/2602.00846","venue":null,"work_id":"e3cb28d7-b7ce-4c77-ae1e-79546afd1976","year":2021},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2602.00846","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:ecbb221b0a6e356487d760c9d1f85314bf893a46102ceabded123890eea01bdf","observation_id":"56d37dab-4fe5-41a2-8e94-82c19423c538","resolution":{"observed_at":"2026-07-08T02:18:41.255312Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-05T13:11:04.104454Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":"2305.20050","doi":"10.1007/bf00262952","metadata_source":"pith","pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Let's Verify Step by Step","venue":"cs.LG","work_id":"6d05b790-04c5-4fd2-91b2-ba1dfdd5770f","year":2023},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:d221d41376d7e3ce23300323cae06a8a5ae1d170609aa6cbd1dd445f676196dc","observation_id":"67e9b3c8-9058-4b56-9138-3a6d0a996b31","resolution":{"observed_at":"2026-07-01T23:06:20.563872Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05229","last_updated":"2025-08-27T16:24:39Z","snapshot_observed_at":"2026-07-06T19:29:09.714725Z","submitted_at":"2024-10-07T17:36:37Z","title":"GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models","version":2},"cited_work":{"arxiv_id":"2410.05229","doi":"10.48550/arxiv.2410.05229","metadata_source":"pith","pith_arxiv_id":"2410.05229","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models","venue":"cs.LG","work_id":"7d26909d-cb69-48f7-b93e-7d6fcc281415","year":2024},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2410.05229","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:64456c1fdc456bcdb08275d3d95cba244b1a5926e64014234126b509d45a62cb","observation_id":"7d2a08dc-3f14-428a-b890-da61d4791cf8","resolution":{"observed_at":"2026-07-01T23:06:20.502805Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-18T18:51:19.596854+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-18T18:51:19.596854+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.10910","last_updated":"2025-06-12T17:22:37Z","snapshot_observed_at":"2026-08-05T09:00:44.496851Z","submitted_at":"2025-06-12T17:22:37Z","title":"Magistral","version":1},"cited_work":{"arxiv_id":"2506.10910","doi":"10.48550/arxiv.2506.10910","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.10910","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Magistral","venue":"ArXiv.org","work_id":"ae3fa57b-d81e-4022-ab02-51763900103b","year":2025},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2506.10910","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:b9d245e41f6c3768105ecb5089b218f30dcbcb8ce5b2e65536cb46cbf405a361","observation_id":"1b858ce7-4e44-4d77-a08b-8ca5bbb14aa8","resolution":{"observed_at":"2026-07-01T23:06:20.508069Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-23T10:52:47.043971+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T10:52:47.043971+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14573","last_updated":"2025-04-06T20:37:50Z","snapshot_observed_at":"2026-07-06T18:18:39.093732Z","submitted_at":"2024-05-23T13:48:54Z","title":"AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents","version":5},"cited_work":{"arxiv_id":"2405.14573","doi":"10.48550/arxiv.2405.14573","metadata_source":"pith","pith_arxiv_id":"2405.14573","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents","venue":"cs.AI","work_id":"c5116d19-d3d3-40fd-9620-f7489812a9ba","year":2024},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2405.14573","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:f27f89f300490fff4213298084925ad91e9f82c363039abb0dfa65c0e7125fa1","observation_id":"3ef11069-d661-469e-9df3-f117a04d2957","resolution":{"observed_at":"2026-07-01T23:06:20.546412Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18901","last_updated":"2024-07-26T17:55:45Z","snapshot_observed_at":"2026-08-01T20:19:05.798018Z","submitted_at":"2024-07-26T17:55:45Z","title":"AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents","version":1},"cited_work":{"arxiv_id":"2407.18901","doi":"10.48550/arxiv.2407.18901","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.18901","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Trivedi, T","venue":"arXiv (Cornell University)","work_id":"569b9cac-ae96-4656-ab37-26520d82ce78","year":2024},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2407.18901","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:6e731a1df703f791a4b6a13ff912def48201d2eac16b3c605d67a0a17aaf56d9","observation_id":"fa177d23-21bd-4573-af6e-34494a0d7843","resolution":{"observed_at":"2026-07-01T23:06:20.559817Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08935","last_updated":"2024-02-19T14:07:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-14T13:41:54Z","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","version":3},"cited_work":{"arxiv_id":"2312.08935","doi":"10.48550/arxiv.2312.08935","metadata_source":"pith","pith_arxiv_id":"2312.08935","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","venue":"cs.AI","work_id":"fb547990-d48e-4ba3-a047-af1e506e8290","year":2023},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2312.08935","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:bd29f2014eb5e900ba137b228820a1bcc8190e6249e2c242bb53f73845e68f4f","observation_id":"b4e51fb7-0546-453b-bd12-eb062a79c0af","resolution":{"observed_at":"2026-07-01T23:06:20.523341Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2502.13124","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T20:40:08.275804Z","title":"Naturalreasoning: Reasoning in the wild with 2.8 m challenging questions","venue":null,"work_id":"d185ff64-8137-4c75-a12b-852746b6bd58","year":2025},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:ab8b5d02866db7b500de6891e9c52f15ae9dcc4be55c466cf371de47088e29b6","observation_id":"182ff33b-f889-44ee-819c-54eae366d611","resolution":{"observed_at":"2026-07-01T23:06:20.498100Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T14:40:21.583101Z","title":"InThe Tenth In- ternational Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:69e1ee6c4f68ca22747caea9cd81625a243f7a0819fa530828f57d85a8944968","observation_id":"59bda563-1572-4571-b99d-1c27eae965db","resolution":{"observed_at":"2026-06-28T14:40:21.583101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.07624","last_updated":"2021-06-01T05:38:50Z","snapshot_observed_at":"2026-07-06T11:09:57.732606Z","submitted_at":"2021-05-17T06:12:06Z","title":"TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance","version":2},"cited_work":{"arxiv_id":"2105.07624","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2105.07624","snapshot_observed_at":"2026-07-01T23:06:20.514933Z","title":"TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance","venue":null,"work_id":"1c86ee16-bad3-4e2b-9df5-bdbfa9ddb687","year":2021},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2105.07624","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:1228f72680aa03d12fa6cd42dce3ae1c93bfaef0126ecda9137b1a89446902ff","observation_id":"117f0cc5-3636-4ff7-aef3-fa8454790d5f","resolution":{"observed_at":"2026-07-01T23:06:20.518308Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17631","last_updated":"2025-03-01T17:06:43Z","snapshot_observed_at":"2026-07-06T16:39:08.850847Z","submitted_at":"2023-10-26T17:48:58Z","title":"JudgeLM: Fine-tuned Large Language Models are Scalable Judges","version":2},"cited_work":{"arxiv_id":"2310.17631","doi":"10.48550/arxiv.2310.17631","metadata_source":"pith","pith_arxiv_id":"2310.17631","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2310.17631 , year=","venue":"cs.CL","work_id":"f86d2c83-ccb7-4eac-9bae-cc5f835621f1","year":2023},"citing_paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T14:40:21.583101Z"},"links":{"cited_paper":"/paper/2310.17631","citing_paper":"/paper/2606.02113"},"observation_digest":"sha256:af80358020c3e3c4fcc1314d5267b3d18a5da9e7e8b65cd9da881ac5c8516759","observation_id":"335b65e9-9f07-4abe-9224-25d17d13e7e9","resolution":{"observed_at":"2026-07-01T23:06:20.555946Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.02113","last_updated":"2026-06-01T11:45:50Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T23:42:35.063977Z","submitted_at":"2026-06-01T11:45:50Z","title":"A Primer in Post-Training Reasoning Data: What We Know About How It Works"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":15,"parse_uncertain":0,"unresolved":1,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":18},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2606.02113."}