{"as_of":"2026-08-08T17:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a2e8245f6c8475d7f179a8fa5d5f7470251781cc1623601cc95fc0ebc9536473","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T14:27:51.054553Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-22T14:51:42.008449Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-08-08T14:27:51.054553Z","title":"Bonbon alignment for large language models and the sweetness of best-of-n sampling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06781","last_updated":"2025-02-10T18:57:29Z","snapshot_observed_at":"2026-08-08T14:18:42.353169Z","submitted_at":"2025-02-10T18:57:29Z","title":"Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T14:27:51.054553Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2502.06781"},"observation_digest":"sha256:0ab6a08922a19d5541f9588e0b405c951808831bb9d91f7e7698c3443c15b725","observation_id":"9d9880a1-80ff-40c3-bc4f-2f9990694c83","resolution":{"observed_at":"2026-08-08T14:27:51.054553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":"2406.00832","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bonbon alignment for large language models and the sweetness of best-of-n sampling","venue":null,"work_id":"51414037-f077-4292-a9c1-87dd6d272e66","year":2024},"citing_paper":{"arxiv_id":"2505.12741","last_updated":"2026-05-31T07:24:10Z","snapshot_observed_at":"2026-08-07T15:43:27.642974Z","submitted_at":"2025-05-19T05:56:06Z","title":"Language Model Networks: Supervision-Efficient Learning through Dense Communication","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-22T14:50:45.735917Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2505.12741"},"observation_digest":"sha256:14a09a640bd54f71d3ab3a0f98ea132ae3c0aa4043ef348178ed8dda2113e5f4","observation_id":"bfea97b9-bc02-4c6d-95dd-0ad90cae42ec","resolution":{"observed_at":"2026-05-22T14:51:42.011625Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-08-06T22:05:27.168369Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22716","last_updated":"2025-06-28T01:52:50Z","snapshot_observed_at":"2026-08-06T21:57:22.791105Z","submitted_at":"2025-06-28T01:52:50Z","title":"BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T22:05:27.168369Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2506.22716"},"observation_digest":"sha256:930fec61d9af65f426d50bd7ff7e79f839c573484b0cfd9127413d1bfad8680a","observation_id":"759f7deb-c904-489c-9723-b0b1552b1806","resolution":{"observed_at":"2026-08-06T22:05:27.168369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-08-06T19:28:54.801422Z","title":"Bonbon alignment for large language models and the sweetness of best-of-n sampling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05913","last_updated":"2025-07-08T11:59:48Z","snapshot_observed_at":"2026-08-08T15:26:44.713063Z","submitted_at":"2025-07-08T11:59:48Z","title":"Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T19:28:54.801422Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2507.05913"},"observation_digest":"sha256:9d5abfbe0e75f8e1cc9763693beb1c541c5a2a41653dca61952e5c26248e6e0d","observation_id":"ab799296-c1a3-44d0-b8c4-81e837b32367","resolution":{"observed_at":"2026-08-06T19:28:54.801422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-08-06T16:34:25.013301Z","title":"Bonbon alignment for large language models and the sweetness of best-of-n sampling.arXiv preprint arXiv:2406.00832,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.13158","last_updated":"2025-07-17T14:22:24Z","snapshot_observed_at":"2026-08-07T07:16:46.127762Z","submitted_at":"2025-07-17T14:22:24Z","title":"Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:34:25.013301Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2507.13158"},"observation_digest":"sha256:8622270f451fdda343bd395e3aae8c8f35b947fe73e7ce9a6c52663c396fb71b","observation_id":"93ddae34-6421-4d2e-a4a4-6e5dfa256901","resolution":{"observed_at":"2026-08-06T16:34:25.013301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":"2406.00832","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bonbon alignment for large language models and the sweetness of best-of-n sampling","venue":null,"work_id":"51414037-f077-4292-a9c1-87dd6d272e66","year":2024},"citing_paper":{"arxiv_id":"2507.14200","last_updated":"2026-05-15T13:08:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-14T16:17:11Z","title":"A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-21T23:26:38.457193Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2507.14200"},"observation_digest":"sha256:38fd2330ba37362365e2a6e299ca0ac5b0669d9bc51893a8178243cbc7056493","observation_id":"029cafa6-61b0-474c-94af-e58103c25f84","resolution":{"observed_at":"2026-05-21T23:30:46.064326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling","version":3},"cited_work":{"arxiv_id":"2406.00832","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.00832","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bonbon alignment for large language models and the sweetness of best-of-n sampling","venue":null,"work_id":"51414037-f077-4292-a9c1-87dd6d272e66","year":2024},"citing_paper":{"arxiv_id":"2511.01008","last_updated":"2026-05-10T03:37:31Z","snapshot_observed_at":"2026-07-06T22:34:43.577332Z","submitted_at":"2025-11-02T16:55:30Z","title":"MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-18T01:31:40.920567Z"},"links":{"cited_paper":"/paper/2406.00832","citing_paper":"/paper/2511.01008"},"observation_digest":"sha256:e3a4cede53915d2e8548d48eb6d0f6285ff7253cc52d5aa11236c7f7c79108b1","observation_id":"33c0561a-5738-4944-9f00-cccbbf46acb6","resolution":{"observed_at":"2026-05-18T01:32:17.408062Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.00832/citation-record","integrity":"/paper/2406.00832/integrity","json":"/paper/2406.00832/citation-record.json","paper":"/paper/2406.00832"},"outbound":[],"paper":{"arxiv_id":"2406.00832","last_updated":"2024-11-01T20:02:32Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T16:20:42.997639Z","submitted_at":"2024-06-02T18:42:57Z","title":"BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.00832."}