{"as_of":"2026-08-10T00:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c143685949780bfc595a15be8c2ee2a5bdc1157e66fdbd70704db12d050a19c","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":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":22,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T16:33:53.575510Z","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":"2026-07-03T14:58:32.551572Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-08-08T16:33:53.575510Z","title":"Taming overconfidence in LLMs: Reward calibration in RLHF,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06173","last_updated":"2025-08-14T18:03:36Z","snapshot_observed_at":"2026-08-09T10:05:25.715996Z","submitted_at":"2025-02-10T05:54:36Z","title":"Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T16:33:53.575510Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2502.06173"},"observation_digest":"sha256:08ccd8d0cf867ebb157732d8b3d2b93aefba79eec4efda86029c9317ab2e7159","observation_id":"a2f0ea1d-8596-401a-a1cd-3f5abadff337","resolution":{"observed_at":"2026-08-08T16:33:53.575510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-08-07T13:05:45.303527Z","title":"Taming overconfidence in llms: Reward calibration in rlhf","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22787","last_updated":"2025-05-28T18:58:09Z","snapshot_observed_at":"2026-08-08T06:11:29.144134Z","submitted_at":"2025-05-28T18:58:09Z","title":"Can Large Language Models Match the Conclusions of Systematic Reviews?","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T13:05:45.303527Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2505.22787"},"observation_digest":"sha256:ea40b6bae47341f6a57fa51597d36d1e16858f491ab98cbce4d8e3920c1cdf18","observation_id":"607a5fc8-c3fc-456a-bcae-c39cd7a68ee4","resolution":{"observed_at":"2026-08-07T13:05:45.303527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-08-05T17:03:43.739393Z","title":"Taming overconfidence in LLMs : Reward calibration in RLHF , 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.17182","last_updated":"2025-08-31T21:27:41Z","snapshot_observed_at":"2026-08-06T14:14:29.845548Z","submitted_at":"2025-08-24T01:43:48Z","title":"LLM Assertiveness can be Mechanistically Decomposed into Emotional and Logical Components","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T17:03:43.739393Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2508.17182"},"observation_digest":"sha256:e9e613eaf598d968309a29094ef4001a9b4c4ed4f4aeeacafd64a8702ab4ee1f","observation_id":"07571656-1408-4162-8c00-f3f3e04a398e","resolution":{"observed_at":"2026-08-05T17:03:43.739393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-08-04T23:12:10.111260Z","title":"Taming overconfidence in llms: Reward calibration in rlhf, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.06770","last_updated":"2025-09-13T00:41:46Z","snapshot_observed_at":"2026-08-09T06:19:53.068339Z","submitted_at":"2025-09-08T14:54:31Z","title":"Another Turn, Better Output? A Turn-Wise Analysis of Iterative LLM Prompting","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T23:12:10.111260Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2509.06770"},"observation_digest":"sha256:f75dbde09b2e5151c9b77d1bed00a9ec6f9e6793657f02cc56cd786d13547d4d","observation_id":"d63f4a44-b50d-472e-9c78-0193e0958724","resolution":{"observed_at":"2026-08-04T23:12:10.111260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2509.21882","last_updated":"2026-05-25T20:11:55Z","snapshot_observed_at":"2026-08-04T14:57:15.491600Z","submitted_at":"2025-09-26T05:06:25Z","title":"Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-18T14:24:48.666197Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2509.21882"},"observation_digest":"sha256:5be07107ec423f7633bc49c06bcf5515b55159b611af25c662ac9d21d1046e48","observation_id":"aa5ca8e9-d994-4036-a946-90eeb1c1b26b","resolution":{"observed_at":"2026-05-18T14:26:28.302447Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2512.05929","last_updated":"2026-06-29T18:18:24Z","snapshot_observed_at":"2026-08-03T18:19:06.255338Z","submitted_at":"2025-12-05T18:12:21Z","title":"LLM Harms: A Taxonomy and Discussion","version":2},"reference_index":105,"source":"pdf_text","source_observed_at":"2026-05-17T00:29:07.951709Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2512.05929"},"observation_digest":"sha256:dba1f0db30c3fb8f25128a256c96262fa388e0717461bc9043b9b39d5aca728c","observation_id":"ae922221-cd9d-496b-ab84-7596603f6658","resolution":{"observed_at":"2026-05-17T00:31:24.649728Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-08-03T18:19:20.047860Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.05929","last_updated":"2026-06-29T18:18:24Z","snapshot_observed_at":"2026-08-03T18:19:06.255338Z","submitted_at":"2025-12-05T18:12:21Z","title":"LLM Harms: A Taxonomy and Discussion","version":4},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-03T18:19:20.047860Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2512.05929"},"observation_digest":"sha256:d0f23589bc49345481b283fb7388d6c49603d00460e86be198a381c9a2be3112","observation_id":"c9846613-28dd-4d21-be79-665e7a2c4e19","resolution":{"observed_at":"2026-08-03T18:19:20.047860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2603.09117","last_updated":"2026-05-27T02:49:05Z","snapshot_observed_at":"2026-07-15T12:13:14.577374Z","submitted_at":"2026-03-10T02:47:59Z","title":"Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T13:49:11.758336Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2603.09117"},"observation_digest":"sha256:dbd7c9755a923add7382fdef1e88faf578722fbbb5cd616796572435bf8d64a6","observation_id":"b4bdbee7-1597-4e3b-b460-9447ab13a233","resolution":{"observed_at":"2026-05-15T13:50:02.381436Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2604.08974","last_updated":"2026-04-10T05:27:35Z","snapshot_observed_at":"2026-07-06T22:57:59.555972Z","submitted_at":"2026-04-10T05:27:35Z","title":"Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-10T17:38:53.434978Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2604.08974"},"observation_digest":"sha256:490bf68b2ae11bcfed5e4f30b66c8a6f17e4d907d479f5b4f25cc876c3f13d42","observation_id":"218a908e-983f-4cde-9b98-01a87c69ee99","resolution":{"observed_at":"2026-05-11T06:25:58.763809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2604.14651","last_updated":"2026-04-23T19:52:27Z","snapshot_observed_at":"2026-07-06T23:02:22.790426Z","submitted_at":"2026-04-16T05:58:37Z","title":"CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T11:58:02.994749Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2604.14651"},"observation_digest":"sha256:678bd72a59b33b15de852352d9255ac28b1732c7b76a5469ff5b46b307171006","observation_id":"94b34015-9813-4510-b10f-a458b81c53fb","resolution":{"observed_at":"2026-05-10T12:00:22.057592Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2604.15602","last_updated":"2026-04-17T00:56:59Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-17T00:56:59Z","title":"GroupDPO: Memory efficient Group-wise Direct Preference Optimization","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-10T09:43:18.432084Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2604.15602"},"observation_digest":"sha256:d42b67d93305fd212dc55a8459ad0300c3eac8b5fe8fca035e1600af1620df75","observation_id":"d0f3526d-4537-4ff0-bfdb-4acb56c7bd27","resolution":{"observed_at":"2026-05-10T09:43:48.691920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2604.19444","last_updated":"2026-04-21T13:25:25Z","snapshot_observed_at":"2026-08-08T12:04:02.806543Z","submitted_at":"2026-04-21T13:25:25Z","title":"Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation","version":1},"reference_index":174,"source":"arxiv_source","source_observed_at":"2026-05-10T03:13:35.541936Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2604.19444"},"observation_digest":"sha256:124f367fca4b6e2b0caaa3f3b5b7fce51a85e62ad8e52e8c9276a7d87962aef0","observation_id":"ccc54c08-c8cd-408c-9b6c-31040d33de1c","resolution":{"observed_at":"2026-05-10T03:14:07.759719Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2604.20500","last_updated":"2026-04-22T12:42:03Z","snapshot_observed_at":"2026-08-03T00:39:38.077432Z","submitted_at":"2026-04-22T12:42:03Z","title":"Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T01:03:06.917872Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2604.20500"},"observation_digest":"sha256:cc002818c6b93038fe3f7b65f6ff9ace65c59133492b670f89b9fe63ec34d61c","observation_id":"b1aa62a7-45ab-4a78-9e96-61442b57a5b7","resolution":{"observed_at":"2026-05-10T01:04:50.002848Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2605.11436","last_updated":"2026-05-12T02:37:04Z","snapshot_observed_at":"2026-07-06T23:23:16.461539Z","submitted_at":"2026-05-12T02:37:04Z","title":"Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-13T02:33:58.262664Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2605.11436"},"observation_digest":"sha256:096c3d3db8f389d9ceea41f5ae7f80c569497af2c3fbd7f84e84402cd4eb4075","observation_id":"e198e2d6-1265-45f0-a543-dbbebc498ac0","resolution":{"observed_at":"2026-05-13T02:37:07.994548Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2605.12718","last_updated":"2026-05-12T20:26:41Z","snapshot_observed_at":"2026-08-02T04:56:08.043773Z","submitted_at":"2026-05-12T20:26:41Z","title":"CHAL: Council of Hierarchical Agentic Language","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-05-14T19:59:13.378797Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2605.12718"},"observation_digest":"sha256:81109d0d44514136a1093a787af1a91fa9a5174e46affb59dc59f58b37b393a6","observation_id":"bb52a3f5-3bd1-4b7c-a761-dd7236d994b5","resolution":{"observed_at":"2026-05-14T19:59:25.854790Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-13T13:23:25.998584Z","title":"ArXiv:2410.09724 [cs]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.23909","last_updated":"2026-04-03T19:43:24Z","snapshot_observed_at":"2026-08-08T07:54:06.703604Z","submitted_at":"2026-04-03T19:43:24Z","title":"Confidence Calibration in Large Language Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T13:23:25.998584Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2605.23909"},"observation_digest":"sha256:e3ac789e015275d8004f6210bb826be7aa0d89a03bbad3c707b9f38dfc42e657","observation_id":"c77dc202-54d1-4fe5-b38e-618c19b8e9aa","resolution":{"observed_at":"2026-07-13T13:23:25.998584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2606.05436","last_updated":"2026-06-03T20:58:43Z","snapshot_observed_at":"2026-07-06T23:45:28.379468Z","submitted_at":"2026-06-03T20:58:43Z","title":"Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison","version":1},"reference_index":127,"source":"arxiv_source","source_observed_at":"2026-06-28T06:03:59.798126Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2606.05436"},"observation_digest":"sha256:05884fc982167d4c6e4e9fd899a02fe056ded63d2c18633bb52fdadcfc5bf95f","observation_id":"9a28bca9-603e-4d6b-bc27-859e3a70ba3a","resolution":{"observed_at":"2026-07-02T08:26:48.121851Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2606.31371","last_updated":"2026-06-30T09:03:24Z","snapshot_observed_at":"2026-08-07T03:44:52.820601Z","submitted_at":"2026-06-30T09:03:24Z","title":"Calibrating the Evaluator: Does Probability Calibration Mitigate Preference Coupling in LLM Agent Feedback Loops?","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-07-01T06:04:09.068738Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2606.31371"},"observation_digest":"sha256:e55f9458b8b12d63810fcb5697db6a09d9d672a498d1a562b815f2d3661d0d27","observation_id":"9ad256af-5a97-4db0-afec-8497b0108f85","resolution":{"observed_at":"2026-07-01T09:55:40.896183Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2606.32032","last_updated":"2026-06-30T17:56:01Z","snapshot_observed_at":"2026-07-07T00:05:41.920778Z","submitted_at":"2026-06-30T17:56:01Z","title":"Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-01T05:22:38.232552Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2606.32032"},"observation_digest":"sha256:4b5d479bc7aa8ec79e7e1ce1b7669c0e3fb921413fbcd99cf3b38fb0eddc8946","observation_id":"25b1d762-bb7c-499c-a5b7-51cc77b8c286","resolution":{"observed_at":"2026-07-01T10:35:42.132345Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2607.00164","last_updated":"2026-06-30T20:42:45Z","snapshot_observed_at":"2026-08-04T14:45:57.113312Z","submitted_at":"2026-06-30T20:42:45Z","title":"Verifiable Rewards for Calibrated Probabilistic Forecasting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-02T19:45:23.721172Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2607.00164"},"observation_digest":"sha256:9cb335911175a7c7f2b2518bcbfcfab5cb7d32709c10050acd5ce0ee54e5ee0e","observation_id":"fa365aa6-dfbd-4c15-b6db-49a604551323","resolution":{"observed_at":"2026-07-02T19:47:18.749700Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":"2410.09724","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-07-03T14:58:32.551572Z","title":"Hashimoto","venue":null,"work_id":"d6c09da4-579a-4599-96e5-91cb9776a962","year":2024},"citing_paper":{"arxiv_id":"2607.01612","last_updated":"2026-07-02T02:29:33Z","snapshot_observed_at":"2026-08-07T21:17:52.584061Z","submitted_at":"2026-07-02T02:29:33Z","title":"Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-07-03T14:49:33.364596Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2607.01612"},"observation_digest":"sha256:b9bd54c688ec3f5978c9690d2c250851a58f2a3bf3479b461521304881e41858","observation_id":"979babe3-935f-4385-8633-22075dd91c85","resolution":{"observed_at":"2026-07-03T14:58:32.552966Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09724","snapshot_observed_at":"2026-08-01T11:40:00.632550Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.19847","last_updated":"2026-07-22T07:32:09Z","snapshot_observed_at":"2026-08-08T08:57:16.341809Z","submitted_at":"2026-07-22T07:32:09Z","title":"Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T11:40:00.632550Z"},"links":{"cited_paper":"/paper/2410.09724","citing_paper":"/paper/2607.19847"},"observation_digest":"sha256:c6501a1283a52d4ab9e97dbf335b57a639255b1e2f3151fa6bff0efef185bc4d","observation_id":"1bb14b89-c902-41b2-aa21-1684b3a5f897","resolution":{"observed_at":"2026-08-01T11:40:00.632550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2410.09724/citation-record","integrity":"/paper/2410.09724/integrity","json":"/paper/2410.09724/citation-record.json","paper":"/paper/2410.09724"},"outbound":[],"paper":{"arxiv_id":"2410.09724","last_updated":"2025-02-28T23:36:40Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T19:32:28.772012Z","submitted_at":"2024-10-13T04:48:40Z","title":"Taming Overconfidence in LLMs: Reward Calibration in RLHF"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2410.09724."}