{"as_of":"2026-08-09T13:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:220a2e604663513289d37d915ed1cc4f9041e09719ac7a3aaaa9626fb399c297","coverage":[{"denominator":61,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:02:26.048992Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:15:42.129693Z","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":"2026-07-02T07:46:45.680966Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.20196","snapshot_observed_at":"2026-08-07T15:15:42.129693Z","title":"Temporal sampling for forgotten reasoning in llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.15804","last_updated":"2025-07-10T17:36:35Z","snapshot_observed_at":"2026-08-07T17:07:40.040298Z","submitted_at":"2025-05-21T17:57:38Z","title":"STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:15:42.129693Z"},"links":{"cited_paper":"/paper/2505.20196","citing_paper":"/paper/2505.15804"},"observation_digest":"sha256:d07091f14906715f54177732bdec13d7eff8227239c76b8bba02e4ed3024134e","observation_id":"caddb07a-2955-4f0f-b28c-415281230c70","resolution":{"observed_at":"2026-08-07T15:15:42.129693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.20196","snapshot_observed_at":"2026-08-06T18:53:12.730408Z","title":"Temporal sampling for forgotten reasoning in llms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.07017","last_updated":"2025-07-09T16:45:48Z","snapshot_observed_at":"2026-08-07T21:24:46.836836Z","submitted_at":"2025-07-09T16:45:48Z","title":"First Return, Entropy-Eliciting Explore","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:53:12.730408Z"},"links":{"cited_paper":"/paper/2505.20196","citing_paper":"/paper/2507.07017"},"observation_digest":"sha256:36dd0282ee3bc4b4407195ca3375ae8f01e869a9ca8f86a84d2502b782201a1a","observation_id":"89d42913-2226-4634-b56e-05c1653ec6c8","resolution":{"observed_at":"2026-08-06T18:53:12.730408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":"2505.20196","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.20196","snapshot_observed_at":"2026-07-02T07:46:45.680966Z","title":"Temporal sampling for forgotten reasoning in llms","venue":null,"work_id":"6595157b-c27c-47d5-a380-df2c750137c3","year":2025},"citing_paper":{"arxiv_id":"2509.08827","last_updated":"2025-10-09T17:08:52Z","snapshot_observed_at":"2026-08-06T15:38:05.011922Z","submitted_at":"2025-09-10T17:59:43Z","title":"A Survey of Reinforcement Learning for Large Reasoning Models","version":3},"reference_index":293,"source":"arxiv_source","source_observed_at":"2026-05-18T00:02:24.352947Z"},"links":{"cited_paper":"/paper/2505.20196","citing_paper":"/paper/2509.08827"},"observation_digest":"sha256:e3798e7fe6617c455fe00a4bba3edf70df59f61fb059b6e06322759a0c6f6dd7","observation_id":"ae2bbc0d-99d1-493b-9b4e-fc01be575a6d","resolution":{"observed_at":"2026-05-18T00:02:24.794099Z","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":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":"2505.20196","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.20196","snapshot_observed_at":"2026-07-02T07:46:45.680966Z","title":"Temporal sampling for forgotten reasoning in llms","venue":null,"work_id":"6595157b-c27c-47d5-a380-df2c750137c3","year":2025},"citing_paper":{"arxiv_id":"2606.05145","last_updated":"2026-06-03T17:50:26Z","snapshot_observed_at":"2026-08-05T18:21:33.114138Z","submitted_at":"2026-06-03T17:50:26Z","title":"Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-28T06:48:28.788806Z"},"links":{"cited_paper":"/paper/2505.20196","citing_paper":"/paper/2606.05145"},"observation_digest":"sha256:f531084f0af60e4ca0ff7c51b7c3d57c1658c539a7d78b27a91833c7c5fbd847","observation_id":"338ed308-9a1a-4c99-b8ab-634b9f73a7b8","resolution":{"observed_at":"2026-07-02T07:46:45.682602Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2505.20196/citation-record","integrity":"/paper/2505.20196/integrity","json":"/paper/2505.20196/citation-record.json","paper":"/paper/2505.20196"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:30.625785Z","title":"Integer partitions","venue":null,"work_id":"f83b683b-18c9-4c32-b74f-cbc0936d9983","year":2004},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.033707Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:d69bb993f5461dc2721aae8942e69e6aa3a435787e8b327c9289d18379034bcc","observation_id":"70c7d326-6ed9-42ec-8b8a-8e847eef9652","resolution":{"observed_at":"2026-08-07T14:02:30.715168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:30.461007Z","title":"Training data attribution via ap- proximate unrolled differentiation, 2024","venue":null,"work_id":"805f045e-5ea4-42e7-8090-42458bad11b3","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.212127Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:57bfb15a9e99f9155dc9d0ef22b0fef6b7e9ffaf58940fa0bdd37437d273c86a","observation_id":"b58ca41b-2f50-4d3f-9051-996ee7f081f4","resolution":{"observed_at":"2026-08-07T14:02:30.550097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:30.255416Z","title":"Scaling test-time compute with open models, 2024","venue":null,"work_id":"9022ae72-af65-49d8-998d-fd23e6fa1893","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.225463Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:2c5fc846cecf8b518555f375d77000c1d7106837d79d05fa861c147761c2dc57","observation_id":"f1d73099-f6cb-4f40-bdda-3fbace0b557a","resolution":{"observed_at":"2026-08-07T14:02:30.356784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-07T14:02:23.236891Z","title":"Evaluating large language models trained on code","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.236891Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:6723a068f0bfe44773deb70012ceda656913102c849b3f01e848cad92f63b18f","observation_id":"81dbd395-e89d-4511-98d7-c0b38b1f18a9","resolution":{"observed_at":"2026-08-07T14:02:23.236891Z","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":"2026-08-07T14:02:23.255182Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.255182Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:8788bbe89a5aeec7b49da01d36a3934a5b9f7e82f7d93fc608cd138cefecdbf0","observation_id":"3f4d909d-ed34-41c0-9570-218f75d6e845","resolution":{"observed_at":"2026-08-07T14:02:23.255182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T14:02:23.278184Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.278184Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:60da945f88d539a4a23cfe1a92bbc8f2a7caa258201c3132dec3f4bb3696f3f1","observation_id":"6cbad9c1-eb11-40ca-889d-78304facee2d","resolution":{"observed_at":"2026-08-07T14:02:23.278184Z","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":"2026-08-07T14:02:23.326108Z","title":"Raft: Reward ranked finetuning for generative foundation model alignment, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.326108Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:20b94e74a9c21be2670ad7b5d10ec9cad4496feb64684e6125f441e874e8e77b","observation_id":"33929f6c-0c68-4ef9-90b7-e98e1714adb1","resolution":{"observed_at":"2026-08-07T14:02:23.326108Z","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":"2026-08-07T14:02:23.348367Z","title":"Open r1: A fully open reproduction of deepseek-r1, January 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.348367Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:3cdcfc53c979f9211ae672a9112aa364fb7c209ac02820e6e5b29bb107d533be","observation_id":"993a767d-68e2-4c37-a1b7-6fda07f280b8","resolution":{"observed_at":"2026-08-07T14:02:23.348367Z","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":"2026-08-07T14:02:30.055346Z","title":"Alphazero-like tree-search can guide large language model decoding and train- ing, 2023","venue":null,"work_id":"ac5cd5e0-f5a6-4a9a-9473-12429926d709","year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.354897Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:736f1415ebb0c50b24c0f8d06d2f491d620bd9babc53e674f8dbc3d850714207","observation_id":"1aa825a2-6d8e-4e56-acd4-544464a942b3","resolution":{"observed_at":"2026-08-07T14:02:30.139456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:23.407754Z","title":"Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.407754Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:2c9d4175e424a0342bb9066b2b1dd5aa4c6d1dec1578b39d0ff5b1c66f03a389","observation_id":"a87407a6-5a1b-47d8-90b2-5c4e5ede8085","resolution":{"observed_at":"2026-08-07T14:02:23.407754Z","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":"2026-08-07T14:02:29.816604Z","title":"Measuring mathematical problem solving with the math dataset","venue":null,"work_id":"54593f7d-feaa-4cb7-af1f-02f278fb4d0e","year":2021},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.462345Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:bbc26c4d9e73759672d56b1a62ff2ca19359c4ee4371a6d0561bdefa2da30a41","observation_id":"b3289dfd-8af4-467b-9f42-3d59977b2626","resolution":{"observed_at":"2026-08-07T14:02:29.947899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:23.533322Z","title":"Measuring mathematical problem solving with the math dataset, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.533322Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:263f68a919dc7c765c8684cc0bdf1f64ca44c1a77b2a94242f184b3122cda3d1","observation_id":"a1bf7707-3401-47dc-9471-91ed35b4bde4","resolution":{"observed_at":"2026-08-07T14:02:23.533322Z","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":"2026-08-07T14:02:23.578889Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.578889Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:a1ab972b3546f18428ff8c63a62ce9923eec032b25c2df29435932eb4ed2bece","observation_id":"ef3c62b9-eb3c-4942-a79a-974e89f82677","resolution":{"observed_at":"2026-08-07T14:02:23.578889Z","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":"2026-08-07T14:02:29.625992Z","title":"Putting rl back in rlhf","venue":null,"work_id":"39f2b71d-fe71-4d83-9c7f-c700f638e55d","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.647588Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:45eaa66a03f0791778b37b6467513bc13257902ba09b41311ef9a7592d48c5bd","observation_id":"a2e60e65-9036-4a98-9c22-06a46cf996f4","resolution":{"observed_at":"2026-08-07T14:02:29.719136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:29.421995Z","title":"Ii-thought : A large-scale, high-quality reasoning dataset, 2025","venue":null,"work_id":"71605fab-bc85-4519-b81a-da9f2307dadb","year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.700807Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:c23a8c721c90df05189f2c018d1cb5a995d633f0ff6a31335170e8b59ce42638","observation_id":"472600c0-b3eb-48a1-bd36-5029bd3c87da","resolution":{"observed_at":"2026-08-07T14:02:29.525687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:29.252856Z","title":"Disentangling memory and reasoning ability in large language models, 2025","venue":null,"work_id":"3835e006-c7c8-4edf-9aef-f9210b237b5f","year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.734235Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:c28fd2647516ac3c2c8caf6a1701e2c10eac7d623c993a6380032f9943ac1848","observation_id":"acc95381-6fa2-4f7a-9d6f-56fb86a2f5a3","resolution":{"observed_at":"2026-08-07T14:02:29.329756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2405.16265","last_updated":"2024-06-26T14:01:15Z","snapshot_observed_at":"2026-08-01T16:15:24.164308Z","submitted_at":"2024-05-25T15:07:33Z","title":"MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16265","snapshot_observed_at":"2026-08-07T14:02:23.778999Z","title":"MindStar: Enhancing math reasoning in pre-trained llms at inference time","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.778999Z"},"links":{"cited_paper":"/paper/2405.16265","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:fb1b2afa6cc29f2f862697e0a45c46e8005688063100b4f54ac8ef781528b80d","observation_id":"1107fcfd-8811-4be0-ad73-2cdcc62c0af3","resolution":{"observed_at":"2026-08-07T14:02:23.778999Z","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":"2026-08-07T14:02:29.078057Z","title":"Gunawi, Cody Hammock, Joe Mambretti, Alexander Barnes, Franc ¸ois Halbach, Alex Rocha, and Joe Stubbs","venue":null,"work_id":"2554487a-3408-40e4-a09b-eab85095a4cd","year":2020},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.790451Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:ccfdf4deca4ebe7ec65b8a9fab4447916ad1a074155113cacc2ce8615092bb11","observation_id":"972f01bc-f229-4add-80e5-48c92d350f2b","resolution":{"observed_at":"2026-08-07T14:02:29.162314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:28.912659Z","title":"ARGS: Alignment as reward-guided search","venue":null,"work_id":"ab2dbae0-04cf-4cfe-a059-a02e9d4f7c99","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.805177Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:2cbe88a0d6550179b29070a725db67ba3465b1996d43327dc17e4f4d8e5d088d","observation_id":"104c28fe-9ff2-4000-a969-a137f8e40e68","resolution":{"observed_at":"2026-08-07T14:02:28.983441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:23.808887Z","title":"Kimi k1.5: Scaling reinforcement learning with llms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.808887Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:aa8600e08c454724bb8c541a9d39b7e4285de6213f1cfa6a8e196cf5a181f16d","observation_id":"8f256095-4e90-40b5-841c-3c6d37bdc0e7","resolution":{"observed_at":"2026-08-07T14:02:23.808887Z","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":"2026-08-07T14:02:28.717382Z","title":"Fine-tuning can distort pretrained features and underperform out-of-distribution, 2022","venue":null,"work_id":"ab254f8d-43c9-445c-9dbe-1e629b6db205","year":2022},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.812114Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:165f46c87024e9c9acda910c4b328a94c7f84d15985b67e640c497bc7136ff80","observation_id":"13670720-9bab-4117-a955-facc318172a6","resolution":{"observed_at":"2026-08-07T14:02:28.794799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:28.540768Z","title":"Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models, 2025","venue":null,"work_id":"e3b91abf-f7ea-4e9a-83d4-d91d01002066","year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.817008Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:9bc217ffbde126d82539fec8cded8893d5e4b4db2e1eb639bb026749382889e8","observation_id":"9aa6ccff-3aa4-4725-a96d-c2c9504629ea","resolution":{"observed_at":"2026-08-07T14:02:28.618669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:28.359669Z","title":"Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D","venue":null,"work_id":"ad5917bb-f6f5-4fea-aacf-efaf8222a3c3","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.822902Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:22bf437c3a05f473b4f7478967ea362b0780faed6e8179f260391ca5ad50b953","observation_id":"12f2dc94-0fc4-498c-89a5-c05aed516231","resolution":{"observed_at":"2026-08-07T14:02:28.455123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:23.875586Z","title":"Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.875586Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:da064de40e6afbf0aaa91ac6c4973b828205a716162f1efe45535a45ba13d48d","observation_id":"72ccfa28-6238-4a10-ae52-8c334caa6385","resolution":{"observed_at":"2026-08-07T14:02:23.875586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08747","last_updated":"2025-01-05T04:09:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-17T02:53:23Z","title":"An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08747","snapshot_observed_at":"2026-08-07T14:02:23.918480Z","title":"An empirical study of catastrophic forgetting in large language models during continual fine-tuning.arXiv preprint arXiv:2308.08747, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.918480Z"},"links":{"cited_paper":"/paper/2308.08747","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:c7d788829b97bd9bf355c6a61d11a8f1980c4294d7339bd8c90cb7323601ac9f","observation_id":"b6d77920-7be7-466a-832b-afb1dcb2f8fb","resolution":{"observed_at":"2026-08-07T14:02:23.918480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02725","last_updated":"2024-10-03T17:47:29Z","snapshot_observed_at":"2026-08-07T06:52:26.132351Z","submitted_at":"2024-10-03T17:47:29Z","title":"Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02725","snapshot_observed_at":"2026-08-07T14:02:23.966532Z","title":"Adaptive inference-time compute: Llms can predict if they can do better, even mid-generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:23.966532Z"},"links":{"cited_paper":"/paper/2410.02725","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:1db2ab1a43c7fd5c95c66ebbaf60b88937a30dfd437a1d239d6f52da1385b692","observation_id":"d2aaa8f3-3bff-438d-87e5-5815c8ca0e76","resolution":{"observed_at":"2026-08-07T14:02:23.966532Z","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":"2026-08-07T14:02:28.173679Z","title":"s1: Simple test-time scaling, 2025","venue":null,"work_id":"5d9e28a4-b441-49f5-bc6c-82aefacab572","year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.037222Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:d932d44ac85afa24f6f9760d2710661509ed0b0f5ecfcd11f256061a160dbf3b","observation_id":"1905c551-d608-4113-8cd8-7790aebe0100","resolution":{"observed_at":"2026-08-07T14:02:28.276013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:28.033409Z","title":"Sky-T1: Train your own o1 preview model within $450, 2025","venue":null,"work_id":"3f4bbce6-41c2-4c02-8b27-fd3508f3f398","year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.112267Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:183f11261454a013e7a01a999a6b3f196c8dd38f9611d1925bb07d947796d14e","observation_id":"2d0e710d-69c8-41cf-9fda-4ad4d47beb80","resolution":{"observed_at":"2026-08-07T14:02:28.063624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:24.201176Z","title":"Learning to reason with llms, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.201176Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:523eae77792630c50bc0277ba07ddf4c359b73ae9d9674a0415f6a4c581d2fda","observation_id":"b9bc2fbe-f3e7-4304-b74b-fff0dc7b390f","resolution":{"observed_at":"2026-08-07T14:02:24.201176Z","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":"2026-08-07T14:02:24.260758Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.260758Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:1e3f1b74f3a321f11141b8cd86435f05271196e128870faabad9c51fabb46d5d","observation_id":"2f9da60b-9f49-4816-a93c-775a31a05115","resolution":{"observed_at":"2026-08-07T14:02:24.260758Z","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":"2026-08-07T14:02:24.325870Z","title":"Qwq: Reflect deeply on the boundaries of the unknown, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.325870Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:536646645365641a9d11d4ac5f19e335d5aec827f9ecece5d5c904abafccaa32","observation_id":"af592150-0df5-4edf-820e-c8bc1f23ce90","resolution":{"observed_at":"2026-08-07T14:02:24.325870Z","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":"2026-08-07T14:02:24.399877Z","title":"Manning, and Chelsea Finn","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.399877Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:3df30aa513cfc6648bee0009cead6ae4e7330eb419ec8a54f4f888ee1eccbfd0","observation_id":"d4c740ae-2096-464f-8864-134a332b86bc","resolution":{"observed_at":"2026-08-07T14:02:24.399877Z","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":"2026-08-07T14:02:24.459177Z","title":"Gpqa: A graduate-level google-proof q&a benchmark","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.459177Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:ae5b89d87c1336ef6cf1928b160e731f1cf9b2c53081afe316bb5c5ee2935618","observation_id":"f501025a-142a-42a5-8eff-922a8d511722","resolution":{"observed_at":"2026-08-07T14:02:24.459177Z","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":"2026-08-07T14:02:27.850819Z","title":"Sutherland","venue":null,"work_id":"bccbe4d0-a32b-4501-a251-15cdf9f35ada","year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.529300Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:b5831df4401df61f4266cfe035f3294315c8be3f75fe14385f52db7b1441ec53","observation_id":"28b803aa-4298-4f9d-9fe7-4fb4d18550d3","resolution":{"observed_at":"2026-08-07T14:02:27.925285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:24.572749Z","title":"Sutherland","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.572749Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:331ed9863f9aa0c7fe54219554dd1146961ba2532df3052e3b9d8ff7d2cfbb7a","observation_id":"8d41def4-9b7e-4cbc-bb7a-ac537e376c65","resolution":{"observed_at":"2026-08-07T14:02:24.572749Z","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":"2026-08-07T14:02:27.678550Z","title":"Beyond chinchilla- optimal: Accounting for inference in language model scaling laws","venue":null,"work_id":"70bde9af-08a6-4818-ad91-046a23e8f266","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.606601Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:12967eeef99206a15268cd6ac039ae791ec2571b25f3ff612d22dc628831f9f5","observation_id":"70f60fbc-e06d-4e32-b444-53936710c085","resolution":{"observed_at":"2026-08-07T14:02:27.757600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:24.624085Z","title":"Proximal policy optimization algorithms, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.624085Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:585cc34277de6456b0234c42c477ee7519c6b36b3e58800203edf7bae72bd714","observation_id":"c9918e5a-a6c4-4331-99d6-5391077ce826","resolution":{"observed_at":"2026-08-07T14:02:24.624085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-07T14:02:24.655253Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.655253Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:ac963f323e784f085947d3b4a52c0894389a317db2c2507c4d716f876fa05669","observation_id":"19b5c516-05ad-4d02-8e36-d01d5821bb9d","resolution":{"observed_at":"2026-08-07T14:02:24.655253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-08-07T14:02:24.661998Z","title":"Hybridflow: A flexible and efficient rlhf framework","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.661998Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:46c30a4288d5cc8e1e7e2d5a0ecec9319c125fd1dbebc8ff6ab002a1cbb3a263","observation_id":"4752f1da-0dcc-4cc6-9c09-fb0659d481a2","resolution":{"observed_at":"2026-08-07T14:02:24.661998Z","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":"2026-08-07T14:02:24.703763Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.703763Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:0a1a3462eeed0b7027cd031de0e4a81dc33c7336b36a09a260963fe6c9d21e7f","observation_id":"5916b830-5d57-41d5-9431-091656f01f16","resolution":{"observed_at":"2026-08-07T14:02:24.703763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-07T14:02:24.741475Z","title":"Scaling llm test-time compute opti- mally can be more effective than scaling model parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.741475Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:32b88e4b596c5c6eb0e39c2657ae0601d40378b0998fc9c2c6d76badb9a1c969","observation_id":"4074a463-0208-4cc8-a4e3-287ed4a7c4d6","resolution":{"observed_at":"2026-08-07T14:02:24.741475Z","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":"2026-08-07T14:02:24.795246Z","title":"Open Thoughts","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.795246Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:c6b55455aa89566f578ea5c9ca2e3efd0545354fa18877780d6152e077b0bab4","observation_id":"ce6846c5-ebcd-4d34-b4fe-f91e0e862b42","resolution":{"observed_at":"2026-08-07T14:02:24.795246Z","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":"2026-08-07T14:02:24.880272Z","title":"Still-3-1.5b-preview: Enhancing slow thinking abilities of small models through reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.880272Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:1a47a15230e56af51c05c84e2472babc5e49e37dd32cee74079c1b7eaa0fe728","observation_id":"2be7d0f9-9174-43a8-944c-ed5d1bedd583","resolution":{"observed_at":"2026-08-07T14:02:24.880272Z","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":"2026-08-07T14:02:27.490488Z","title":"AlphaZero-like tree-search can guide large language model decoding and train- ing","venue":null,"work_id":"55cdaf69-2076-4b44-a7d6-abd424d847ff","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.927393Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:607ced3c052df8c632f30f1a051ef5da585a4e870c0c91048a2a6659adf12e64","observation_id":"29fcc7dd-b9d0-411a-aa90-4bcfc9ae0e7c","resolution":{"observed_at":"2026-08-07T14:02:27.559076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:27.318984Z","title":"Self-consistency improves chain of thought reasoning in lan- guage models, 2023","venue":null,"work_id":"b0fa6307-26b1-446d-990c-36f7e219ba19","year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:24.991813Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:ff722782186be9bacecbc140739c0fd47346debf1d6eef5c68709dac4a2ad794","observation_id":"7add9d1d-1ae8-4a14-ad90-374c80687777","resolution":{"observed_at":"2026-08-07T14:02:27.393384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:27.135545Z","title":"Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou","venue":null,"work_id":"3201e79d-fd49-4265-b6e4-98bdbe53aed6","year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.038301Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:994fc36b731131f836162408b380c80f6e9f3b25f0a876cd658900eaeb53cc2d","observation_id":"36a8499f-50dc-45b3-8831-2af3aa08b01f","resolution":{"observed_at":"2026-08-07T14:02:27.208708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:25.093221Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.093221Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:93c9ef0f5bddf89806234aa35dd684eb218d2c2d647ba35cdd882ff8bf3471bb","observation_id":"c226b0a3-6021-4a39-af91-fc2e1a6689cb","resolution":{"observed_at":"2026-08-07T14:02:25.093221Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18449","last_updated":"2025-12-01T00:16:59Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-25T18:45:04Z","title":"SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18449","snapshot_observed_at":"2026-08-07T14:02:25.163164Z","title":"Swe-rl: Advancing llm reason- ing via reinforcement learning on open software evolution","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.163164Z"},"links":{"cited_paper":"/paper/2502.18449","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:e7b6c6cef0598baa6e1321ad58a43c2af2d514e10b837ffada9198161255f210","observation_id":"6d71cfd6-9692-45af-afb2-a02bee00383c","resolution":{"observed_at":"2026-08-07T14:02:25.163164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00724","last_updated":"2025-03-03T07:53:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-01T17:16:04Z","title":"Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00724","snapshot_observed_at":"2026-08-07T14:02:25.245487Z","title":"Inference scaling laws: An empirical analysis of compute-optimal inference for problem-solving with language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.245487Z"},"links":{"cited_paper":"/paper/2408.00724","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:74adcfc9954ad59c34a854bd031e8498d4be680ae4e12098b2320481f080ee4c","observation_id":"da457912-d863-4163-89c3-00325bf822e2","resolution":{"observed_at":"2026-08-07T14:02:25.245487Z","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":"2026-08-07T14:02:26.950426Z","title":"Self-evaluation guided beam search for reasoning","venue":null,"work_id":"14b7b155-4fa5-4a3b-8d24-ceca8e5d8ff0","year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.291233Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:0fbfd5387f76fda1b58d8df40489de9b672f9189c8c926a5f19a53b852b79f13","observation_id":"b9b67ac5-083d-4e64-8dd4-c2e862a332bd","resolution":{"observed_at":"2026-08-07T14:02:27.034480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2405.14333","last_updated":"2024-05-23T09:03:42Z","snapshot_observed_at":"2026-07-06T18:18:25.746022Z","submitted_at":"2024-05-23T09:03:42Z","title":"DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14333","snapshot_observed_at":"2026-08-07T14:02:25.377247Z","title":"Deepseek-prover: Advancing theorem proving in llms through large- scale synthetic data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.377247Z"},"links":{"cited_paper":"/paper/2405.14333","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:82285ed6e13e00ae761a821ed19a62923afd9c31d75084bb3a10621fc874b3c7","observation_id":"6dd1189b-63c8-4412-9700-dbe5dd2b0ffd","resolution":{"observed_at":"2026-08-07T14:02:25.377247Z","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":"2026-08-07T14:02:25.436004Z","title":"A minimalist approach to llm reasoning: from rejection sampling to reinforce, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.436004Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:323e65ebcd0681398447ebafbb01fa1baca4dd77b332330d32921db710ff2365","observation_id":"c0775373-3000-4331-8586-bd6ebe95b757","resolution":{"observed_at":"2026-08-07T14:02:25.436004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-07T14:02:25.503406Z","title":"Qwen2.5 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.503406Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:ca9dca01e8f15a1e53d9c36770401b60e9a6e3704639711e76c83445f387b3b2","observation_id":"abe6ad84-34cf-4ad5-8c2d-901d2f4ef843","resolution":{"observed_at":"2026-08-07T14:02:25.503406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12122","last_updated":"2024-09-18T16:45:37Z","snapshot_observed_at":"2026-07-06T19:17:41.512834Z","submitted_at":"2024-09-18T16:45:37Z","title":"Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12122","snapshot_observed_at":"2026-08-07T14:02:25.574892Z","title":"Qwen2.5-math technical report: Toward mathematical expert model via self-improvement","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.574892Z"},"links":{"cited_paper":"/paper/2409.12122","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:931ca287bf50c95928a53edc9ba4ed42c404d3674e6de33b1b4cf9e22a6afd7b","observation_id":"4c3c6752-3b80-4d13-8e8e-cb8b16671f30","resolution":{"observed_at":"2026-08-07T14:02:25.574892Z","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":"2026-08-07T14:02:26.773837Z","title":"Tree of thoughts: Deliberate problem solving with large language models","venue":null,"work_id":"8cac8f2b-f054-4fea-8048-2024f0a6f9d4","year":2023},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.648741Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:930c22ffb4482066b4ec671e07f3c0c63142cebefbad86fad85d2e10eb881e7e","observation_id":"8114a1f2-929e-4e86-ba06-6ff428eded02","resolution":{"observed_at":"2026-08-07T14:02:26.852870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:26.608556Z","title":"Dapo: An open-source llm reinforcement learning system at scale, 2025","venue":null,"work_id":"3c708acd-7d82-4dc2-8ddd-1ef16c15390f","year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.707333Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:b597bf777c4beeb4d4259b552c2d88068053c5276c6ef4a2c0a435c4c295a613","observation_id":"94040b31-9160-44cf-b9f3-6c68bff61ae4","resolution":{"observed_at":"2026-08-07T14:02:26.693321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2504.13837","last_updated":"2025-11-24T06:11:04Z","snapshot_observed_at":"2026-07-06T21:11:34.701779Z","submitted_at":"2025-04-18T17:59:56Z","title":"Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13837","snapshot_observed_at":"2026-08-07T14:02:25.765449Z","title":"Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? arXiv preprint arXiv:2504.13837, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.765449Z"},"links":{"cited_paper":"/paper/2504.13837","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:21e8c9a2728bb62e9b91ea9703221496595799bca392c7b76a214fd491018c8f","observation_id":"1a42dcdf-bd3f-4522-b9c7-dd5234da1f4b","resolution":{"observed_at":"2026-08-07T14:02:25.765449Z","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":"2026-08-07T14:02:25.839489Z","title":"7b model and 8k examples: Emerging reasoning with reinforcement learning is both effective and efficient","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.839489Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:023ffd37edd4a038e7881c97d34b371e3633df4afc3d3070485aee44036b36d8","observation_id":"91b6be52-fabf-4aaa-b28b-d0dd78e3528c","resolution":{"observed_at":"2026-08-07T14:02:25.839489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07301","last_updated":"2025-06-05T16:34:24Z","snapshot_observed_at":"2026-08-03T11:11:25.359494Z","submitted_at":"2025-01-13T13:10:16Z","title":"The Lessons of Developing Process Reward Models in Mathematical Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.07301","snapshot_observed_at":"2026-08-07T14:02:25.907173Z","title":"The lessons of developing process reward models in mathematical reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.907173Z"},"links":{"cited_paper":"/paper/2501.07301","citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:23e453ea0b6a17e42fd92b56f68b352ecba3607fff571748fe4022efb4fc3213","observation_id":"f4edee92-115a-49ab-b259-e47944d6d4f6","resolution":{"observed_at":"2026-08-07T14:02:25.907173Z","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":"2026-08-07T14:02:26.431906Z","title":"aha moments","venue":null,"work_id":"1cb5260b-bf24-4ccf-9fcc-bfc4248755e0","year":2024},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:25.976745Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:644820ef8da271926765aeae4a86474d1f8bbac96dcbd4bc3f8059f1387876db","observation_id":"7edbab37-0923-45ff-bce5-125e33bfcb93","resolution":{"observed_at":"2026-08-07T14:02:26.472973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:02:26.283771Z","title":"N −Ci,j kj N kj # = (1− ri,j)kj Since Ci,j follows a binomial distribution B(N, ri,j), we have: E","venue":null,"work_id":"46b2e6c6-b638-4257-8996-a4b6b6cd0ca0","year":null},"citing_paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T14:02:26.048992Z"},"links":{"citing_paper":"/paper/2505.20196"},"observation_digest":"sha256:98b5e7f8120c1341a757b70806688223d9389d5a517359f04b2a15aac62df469","observation_id":"7c3de85b-1c67-4e9b-bcce-648d4b99e51e","resolution":{"observed_at":"2026-08-07T14:02:26.350525Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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"}}],"paper":{"arxiv_id":"2505.20196","last_updated":"2025-05-26T16:39:52Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T13:55:18.345337Z","submitted_at":"2025-05-26T16:39:52Z","title":"Temporal Sampling for Forgotten Reasoning in LLMs"},"reference_resolution":{"displayed":61,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":0,"verified_fuzzy":24},"total_outbound_references":61},"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 9 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 4 inbound Pith citation observations for arXiv:2505.20196."}