{"as_of":"2026-08-13T01:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e0255335e6aa0ae276904b114f3869ea01c3a51673c7bc7f64f2b4a30dfdb3a5","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:10:53.694502Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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":"2026-08-06T17:10:01.813523Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T17:10:04.458833Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"cited_work":{"arxiv_id":"2412.06033","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06033","snapshot_observed_at":"2026-08-06T17:10:04.458833Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","venue":"stat.ML","work_id":"3ce90321-892c-4bf7-8b73-fbfd0ebd2d9e","year":2024},"citing_paper":{"arxiv_id":"2507.11768","last_updated":"2026-06-23T03:31:31Z","snapshot_observed_at":"2026-08-08T05:47:27.231450Z","submitted_at":"2025-07-15T22:20:11Z","title":"LLMs are Bayesian, In Expectation, Not in Realization","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:10:01.813523Z"},"links":{"cited_paper":"/paper/2412.06033","citing_paper":"/paper/2507.11768"},"observation_digest":"sha256:fbfae677f0648c375a382800ea3c7d858f222c559d98ad0f61d03db0e4d80607","observation_id":"0f267321-c566-41e5-ba69-8e09b064217c","resolution":{"observed_at":"2026-08-06T17:10:04.510089Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.06033/citation-record","integrity":"/paper/2412.06033/integrity","json":"/paper/2412.06033/citation-record.json","paper":"/paper/2412.06033"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:10:53.874189Z","title":"∼.” It means “sampled according to","venue":null,"work_id":"4d30d374-ceb6-4b8f-a9fa-461f6fee4e47","year":2024},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.689929Z"},"links":{"citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:72e9851b780664ced5ac19891145795af2c9de0bfa84beafc1e4b52f17567b34","observation_id":"df76fb40-48b7-44d9-8c41-184c47e7b2ab","resolution":{"observed_at":"2026-08-11T20:10:53.877956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:10:53.860343Z","title":"Let F ∼ Pθ and X1, X2,","venue":null,"work_id":"a30f5e42-9515-479f-a958-99fbeeba39a3","year":2018},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.694502Z"},"links":{"citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:3dc1aca3aaeb013c4631d5be88386a83ef7ca713c857f3a3ec7284f05287a4c5","observation_id":"3fa7fc8b-9978-4093-a9c5-657682076da2","resolution":{"observed_at":"2026-08-11T20:10:53.865769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00234","last_updated":"2024-10-05T11:47:02Z","snapshot_observed_at":"2026-07-06T14:36:25.690733Z","submitted_at":"2022-12-31T15:57:09Z","title":"A Survey on In-context Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00234","snapshot_observed_at":"2026-08-11T20:10:53.633581Z","title":"A survey on in-context learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.633581Z"},"links":{"cited_paper":"/paper/2301.00234","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:e3dc3ac8f68fc51c331ad54093d1cd0d3eb1f1844b762c234bad9e4cf8902cf5","observation_id":"36a5099a-1845-45b0-b7ac-a82cf49d6cea","resolution":{"observed_at":"2026-08-11T20:10:53.633581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00793","last_updated":"2024-06-02T16:20:30Z","snapshot_observed_at":"2026-08-12T23:52:09.282494Z","submitted_at":"2024-06-02T16:20:30Z","title":"Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00793","snapshot_observed_at":"2026-08-11T20:10:53.642612Z","title":"Is in-context learning in large language models bayesian? a martingale perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.642612Z"},"links":{"cited_paper":"/paper/2406.00793","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:420c4a7fc5f6e948fda972bed2f53d768265766d39fb2e0ab559bbc276cb48ce","observation_id":"c95af48a-f17b-4400-adf8-8e2473aafc31","resolution":{"observed_at":"2026-08-11T20:10:53.642612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.06085","last_updated":"2023-06-09T17:48:54Z","snapshot_observed_at":"2026-08-10T10:54:48.705061Z","submitted_at":"2023-06-09T17:48:54Z","title":"Trapping LLM Hallucinations Using Tagged Context Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.06085","snapshot_observed_at":"2026-08-11T20:10:53.646609Z","title":"Trapping LLM hallucinations using tagged context prompts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.646609Z"},"links":{"cited_paper":"/paper/2306.06085","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:bcfcb52f90fa82bc74c95f7f4483bb7f4f614344396b915441eeea776a450c57","observation_id":"2e940a73-1e8b-49b1-a730-f0f7a9429856","resolution":{"observed_at":"2026-08-11T20:10:53.646609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05221","last_updated":"2022-11-21T16:38:35Z","snapshot_observed_at":"2026-08-12T15:27:25.451283Z","submitted_at":"2022-07-11T22:59:39Z","title":"Language Models (Mostly) Know What They Know","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05221","snapshot_observed_at":"2026-08-11T20:10:53.651237Z","title":"Language models (mostly) know what they know","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.651237Z"},"links":{"cited_paper":"/paper/2207.05221","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:fc12d729fb5d2d0791668f4bfd8ba4a494d6e90c0ac361d7fbdf468546683a81","observation_id":"8b6cea9e-3bc4-43ee-b728-a6703e40d079","resolution":{"observed_at":"2026-08-11T20:10:53.651237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.03122","last_updated":"2018-01-09T19:58:41Z","snapshot_observed_at":"2026-07-06T06:17:56.038551Z","submitted_at":"2018-01-09T19:58:41Z","title":"A detailed treatment of Doob's theorem","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.03122","snapshot_observed_at":"2026-08-11T20:10:53.659009Z","title":"A detailed treatment of doob’s theorem","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.659009Z"},"links":{"cited_paper":"/paper/1801.03122","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:cd112a1be9cbee36ad79498a673ae83e1dccb66cd31869bc8a0cc57b163d82e8","observation_id":"13633014-b693-47e7-bef7-a1a6a54e4bd4","resolution":{"observed_at":"2026-08-11T20:10:53.659009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12813","last_updated":"2023-03-08T23:41:49Z","snapshot_observed_at":"2026-08-09T08:08:35.780192Z","submitted_at":"2023-02-24T18:48:43Z","title":"Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12813","snapshot_observed_at":"2026-08-11T20:10:53.665287Z","title":"Check your facts and try again: Improving large language models with external knowledge and automated feedback","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.665287Z"},"links":{"cited_paper":"/paper/2302.12813","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:bd3958ba0624ee7acb2104a73290508a39a9ed95e72d3152cfb4898b73164523","observation_id":"3db7b092-fcbc-40f8-9a01-db12d13b276e","resolution":{"observed_at":"2026-08-11T20:10:53.665287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06681","last_updated":"2024-07-05T15:30:45Z","snapshot_observed_at":"2026-08-07T14:28:06.805424Z","submitted_at":"2023-12-09T04:40:46Z","title":"Steering Llama 2 via Contrastive Activation Addition","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06681","snapshot_observed_at":"2026-08-11T20:10:53.668432Z","title":"Steering Llama 2 via contrastive activation addition","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.668432Z"},"links":{"cited_paper":"/paper/2312.06681","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:f0d28b21588af0fbd790a98fd06a6bdc63d74e1b17a01b0ee22a50fabe86ba32","observation_id":"18dcd7c4-7d5f-45f3-a090-1e8952386d51","resolution":{"observed_at":"2026-08-11T20:10:53.668432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-11T20:10:53.675184Z","title":"Llama 2: Open foundation and fine-tuned chat models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.675184Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:14568939dc0847fd16d651b637ead9dfc071e2a0eca158488c092efce28ad91e","observation_id":"ecfc4592-ed19-4eab-a14c-9271b23c2702","resolution":{"observed_at":"2026-08-11T20:10:53.675184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03987","last_updated":"2023-08-12T14:57:37Z","snapshot_observed_at":"2026-08-10T16:32:50.837312Z","submitted_at":"2023-07-08T14:25:57Z","title":"A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03987","snapshot_observed_at":"2026-08-11T20:10:53.678380Z","title":"A stitch in time saves nine: Detecting and mitigating hallucinations of LLMs by validating low-confidence generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.678380Z"},"links":{"cited_paper":"/paper/2307.03987","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:ea961be6fd4835b368309b3fd950a43a0e0de9c2bec59a2302110b3d147ec5a0","observation_id":"1142fc7c-15eb-4982-9a63-3f09f4cebc05","resolution":{"observed_at":"2026-08-11T20:10:53.678380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03307","last_updated":"2024-11-28T17:48:55Z","snapshot_observed_at":"2026-08-12T23:07:41.137115Z","submitted_at":"2024-08-06T17:16:10Z","title":"Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03307","snapshot_observed_at":"2026-08-11T20:10:53.681473Z","title":"Pre-training and in-context learning is bayesian inference a la de finetti","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.681473Z"},"links":{"cited_paper":"/paper/2408.03307","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:0cd5afba9dca1c212d0d7d19f39f386d273bae13ca92ae16e8f5921712f15f31","observation_id":"f45a331b-1e17-49a6-a265-638908d3bc85","resolution":{"observed_at":"2026-08-11T20:10:53.681473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13669","last_updated":"2024-06-13T03:44:03Z","snapshot_observed_at":"2026-08-05T08:47:31.549852Z","submitted_at":"2023-05-23T04:22:50Z","title":"The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13669","snapshot_observed_at":"2026-08-11T20:10:53.686505Z","title":"Mitigating language model hallucination with interactive question-knowledge alignment","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.686505Z"},"links":{"cited_paper":"/paper/2305.13669","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:eae87056746527812606cb96d3d8abbbea0007680539043fb226507eb91d8bcf","observation_id":"adcf29d0-9123-40ec-a144-794010f963ae","resolution":{"observed_at":"2026-08-11T20:10:53.686505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:10:53.886067Z","title":"Trusting your evidence: Hallucinate less with context-aware decoding","venue":null,"work_id":"f6a84545-b224-4736-96dd-451680694822","year":2024},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":1984,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.671598Z"},"links":{"citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:79ce9bbdadf18c514fb50c0b056fff727d48d46c3f2295ba0326583f277b6d88","observation_id":"53d4d0b2-8d90-4e15-ae3d-b1625590ad10","resolution":{"observed_at":"2026-08-11T20:10:53.892033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:10:53.916584Z","title":"ISBN 978-3-540-33428-6","venue":null,"work_id":"2086d0f5-e304-4a99-b997-bc01185545f7","year":2024},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.629541Z"},"links":{"citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:c898e079f9b106bd8b734e7cd0d639cba4cefa8ab134e821aa8b2b48a19a3d89","observation_id":"f13f502e-1af6-4cfc-9c86-b97c0724445a","resolution":{"observed_at":"2026-08-11T20:10:53.920325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:10:53.902639Z","title":"Holdout predictive checks for [b]ayesian model criticism","venue":null,"work_id":"a2ad2970-03ad-423b-91e7-2e14f8fc66c3","year":2023},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.662131Z"},"links":{"citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:1da0e7c37c93a1133a4331ad9439e3632a6108911b8efb98487bcb79c3987a89","observation_id":"7c446083-8348-4126-bac1-c4b8bbbf68c8","resolution":{"observed_at":"2026-08-11T20:10:53.907395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11764","last_updated":"2023-09-13T18:01:36Z","snapshot_observed_at":"2026-08-12T08:09:00.136672Z","submitted_at":"2023-08-22T20:12:49Z","title":"Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11764","snapshot_observed_at":"2026-08-11T20:10:53.637979Z","title":"Halo: Estimation and reduction of hallucinations in open-source weak large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.637979Z"},"links":{"cited_paper":"/paper/2308.11764","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:d92c71c3c1019dfbb83351692283d2e1193c7425b2ea6860c21226b2a839e510","observation_id":"912d4a32-08a5-4b8a-9815-006fb0967ffe","resolution":{"observed_at":"2026-08-11T20:10:53.637979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.15927","last_updated":"2024-06-22T19:46:06Z","snapshot_observed_at":"2026-07-30T04:59:24.269176Z","submitted_at":"2024-06-22T19:46:06Z","title":"Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.15927","snapshot_observed_at":"2026-08-11T20:10:53.655136Z","title":"Semantic entropy probes: Robust and cheap hallucination detection in llms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.655136Z"},"links":{"cited_paper":"/paper/2406.15927","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:cc2587f65445cbe91f93272a83eafe05c61fbc8e8b4ef2a4d7d019772a732f97","observation_id":"21992463-d560-438f-976c-ff5a4fb9e3b1","resolution":{"observed_at":"2026-08-11T20:10:53.655136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00474","last_updated":"2024-06-04T22:39:58Z","snapshot_observed_at":"2026-08-13T00:40:49.394574Z","submitted_at":"2024-03-30T20:47:55Z","title":"Linguistic Calibration of Long-Form Generations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00474","snapshot_observed_at":"2026-08-11T20:10:53.620826Z","title":"Linguistic calibration of language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.620826Z"},"links":{"cited_paper":"/paper/2404.00474","citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:95dd58051844af539a1a7c5e72081313f8740ef59adc58412a73c48c882bef38","observation_id":"2850642d-7ce1-4cbf-9004-f436f7e259b8","resolution":{"observed_at":"2026-08-11T20:10:53.620826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:10:53.625528Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T20:10:53.625528Z"},"links":{"citing_paper":"/paper/2412.06033"},"observation_digest":"sha256:1fc39386357ed0556c7f2545444df66d4324c0b8e7d99e8fd1f4e97b404ece1a","observation_id":"77ec781f-4635-4f2c-9abd-e3fab0152fc8","resolution":{"observed_at":"2026-08-11T20:10:53.625528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.06033","last_updated":"2024-12-08T19:03:21Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-12T11:14:38.435349Z","submitted_at":"2024-12-08T19:03:21Z","title":"Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":20},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2412.06033."}