{"as_of":"2026-08-15T15:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:60e50135c9104a5f9f826ce016b06a14b72d3c01ac0a220d4d018849f6c64145","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:49:04.493890Z","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":"2026-07-03T21:08:57.762683Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-12T00:11:14.618718Z","title":"Variational best-of-n alignment.arXiv:2407.06057,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01951","last_updated":"2024-12-04T14:20:21Z","snapshot_observed_at":"2026-08-14T11:20:12.488890Z","submitted_at":"2024-12-02T20:24:17Z","title":"Self-Improvement in Language Models: The Sharpening Mechanism","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T00:11:14.618718Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2412.01951"},"observation_digest":"sha256:a3d7a51fb22c50d33c53bbbc427abc15c416ddae7f2dcd2dbab0f5db519b4d8d","observation_id":"d7948727-c316-488a-b7bc-9d176cc9fae7","resolution":{"observed_at":"2026-08-12T00:11:14.618718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-11T20:53:36.438121Z","title":"Variational best-of-n alignment.arXiv preprint arXiv:2407.06057, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05232","last_updated":"2025-07-03T18:06:35Z","snapshot_observed_at":"2026-08-14T13:58:04.180134Z","submitted_at":"2024-12-06T18:02:59Z","title":"LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T20:53:36.438121Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2412.05232"},"observation_digest":"sha256:b9da98e666c44ceffcd2dd51f44dffe67f5e08a390e29e3044f88c08b52b8637","observation_id":"3c141f7c-eca5-4b07-8e2a-47c3ddbd406c","resolution":{"observed_at":"2026-08-11T20:53:36.438121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-11T12:30:38.322139Z","title":"URL https://doi.org/10.48550/arXiv.2407","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.14135","last_updated":"2024-12-18T18:24:47Z","snapshot_observed_at":"2026-08-14T12:37:01.715676Z","submitted_at":"2024-12-18T18:24:47Z","title":"Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T12:30:38.322139Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2412.14135"},"observation_digest":"sha256:23d91dc20f72f7d305c2cd42b607cba29f3d22cdbb577da617163a0781e0ee30","observation_id":"eefbb980-1aa2-43f7-a3f1-d9d71915a7d2","resolution":{"observed_at":"2026-08-11T12:30:38.322139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-10T23:57:54.187833Z","title":"Variational best-of-n alignment, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19792","last_updated":"2025-08-21T16:32:06Z","snapshot_observed_at":"2026-08-15T03:17:04.958017Z","submitted_at":"2024-12-27T18:45:36Z","title":"InfAlign: Inference-aware language model alignment","version":5},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T23:57:54.187833Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2412.19792"},"observation_digest":"sha256:30fe0cb110d7449154adb5374b5ff99047d281c0fe53e9deb5fdb617f3e5942a","observation_id":"090ce3b9-2b3f-47b4-b82f-fa4b758d7d52","resolution":{"observed_at":"2026-08-10T23:57:54.187833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-05T14:33:17.864511Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.21228","last_updated":"2025-08-28T21:39:53Z","snapshot_observed_at":"2026-08-13T18:17:05.835571Z","submitted_at":"2025-08-28T21:39:53Z","title":"Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-05T14:33:17.864511Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2508.21228"},"observation_digest":"sha256:4c2adc7e544595bc29e506140223e04b25f54e80cf203af35a31a0255ce6c13e","observation_id":"a428ab09-1ed7-42e8-ad59-866f87bb3181","resolution":{"observed_at":"2026-08-05T14:33:17.864511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":"2407.06057","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-07-03T21:08:57.762683Z","title":"Variational best-of-n alignment","venue":null,"work_id":"c93d35c4-1e13-47d8-ab10-ec77bf69ee2d","year":2024},"citing_paper":{"arxiv_id":"2605.04559","last_updated":"2026-05-06T07:02:57Z","snapshot_observed_at":"2026-08-15T05:32:55.813536Z","submitted_at":"2026-05-06T07:02:57Z","title":"Beyond Static Best-of-N: Bayesian List-wise Alignment for LLM-based Recommendation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-08T17:06:18.050592Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2605.04559"},"observation_digest":"sha256:bebc49f4d58d55ed0c060b3ec9a3ff12af6fe3d148abc4358e659d04115db125","observation_id":"1d0d7f82-1d1e-442e-a68a-d7a69d6823be","resolution":{"observed_at":"2026-05-11T17:51:06.582478Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":"2407.06057","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-07-03T21:08:57.762683Z","title":"Variational best-of-n alignment","venue":null,"work_id":"c93d35c4-1e13-47d8-ab10-ec77bf69ee2d","year":2024},"citing_paper":{"arxiv_id":"2607.01490","last_updated":"2026-07-01T21:39:19Z","snapshot_observed_at":"2026-08-13T03:52:17.032614Z","submitted_at":"2026-07-01T21:39:19Z","title":"Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-07-03T20:59:57.539909Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2607.01490"},"observation_digest":"sha256:8dd4cb3be4ce386108a26fb1de333af8fc7c58bf3a5261c0644eba0d058294c2","observation_id":"dde0da81-2429-43e1-a61a-979dfb172a92","resolution":{"observed_at":"2026-07-03T21:08:57.763943Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-07-14T06:44:16.198117Z","title":"Variational best-of- N alignment","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11146","last_updated":"2026-07-13T06:37:42Z","snapshot_observed_at":"2026-08-14T23:38:52.756466Z","submitted_at":"2026-07-13T06:37:42Z","title":"Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-07-14T06:44:16.198117Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2607.11146"},"observation_digest":"sha256:a4485505b30d41547cbc3ecb0dd26470a28fac549e62dbfe2a912cabf30ad515","observation_id":"00553ecb-2788-4879-a3e5-1eddc551446c","resolution":{"observed_at":"2026-07-14T06:44:16.198117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-15T14:49:04.493890Z","title":"2025 , eprint =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.04001","last_updated":"2026-08-04T17:57:20Z","snapshot_observed_at":"2026-08-15T14:42:02.369461Z","submitted_at":"2026-08-04T17:57:20Z","title":"Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-15T14:49:04.493890Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2608.04001"},"observation_digest":"sha256:caf4c7dc9d4109c2a91075b061a10f256ac391a66b074cfdf67ae06b647ea528","observation_id":"18fdb6ed-7ca2-453d-a9a1-399b5c03c232","resolution":{"observed_at":"2026-08-15T14:49:04.493890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06057","snapshot_observed_at":"2026-08-12T14:10:45.364327Z","title":"arXiv preprint arXiv:2407.06057 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.10928","last_updated":"2026-08-11T13:58:07Z","snapshot_observed_at":"2026-08-14T23:12:19.265618Z","submitted_at":"2026-08-11T13:58:07Z","title":"ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-08-12T14:10:45.364327Z"},"links":{"cited_paper":"/paper/2407.06057","citing_paper":"/paper/2608.10928"},"observation_digest":"sha256:b9181559fe184e41cbac169a3a49015cbb091243fba7b2ee4995251743d524bc","observation_id":"694eba5f-4a72-4c8d-a550-63c27a45cbb9","resolution":{"observed_at":"2026-08-12T14:10:45.364327Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.06057/citation-record","integrity":"/paper/2407.06057/integrity","json":"/paper/2407.06057/citation-record.json","paper":"/paper/2407.06057"},"outbound":[],"paper":{"arxiv_id":"2407.06057","last_updated":"2025-03-04T14:33:50Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T23:26:38.161586Z","submitted_at":"2024-07-08T15:59:44Z","title":"Variational Best-of-N Alignment"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2407.06057."}