{"as_of":"2026-08-24T03:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70503ff5b3af3d98f8a8e13ae3dc1c7ee3b0d3ace2a50bdb2c22ddbbfd91a08e","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:43:55.305136Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:40:07.437399Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-15T13:55:53.301034Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.19898","snapshot_observed_at":"2026-08-07T12:40:07.437399Z","title":"Gencls++: Pushing the boundaries of generative classification in llms through comprehensive sft and rl studies across diverse datasets.arXiv preprint arXiv:2504.19898, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24105","last_updated":"2025-05-30T01:13:22Z","snapshot_observed_at":"2026-08-09T13:46:57.143129Z","submitted_at":"2025-05-30T01:13:22Z","title":"Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:07.437399Z"},"links":{"cited_paper":"/paper/2504.19898","citing_paper":"/paper/2505.24105"},"observation_digest":"sha256:1337177765016f97a600561fae42c71ba0671e7ed1520bf0cd4b7a7377f8f597","observation_id":"f99164e9-49d7-417a-800b-205e0852b455","resolution":{"observed_at":"2026-08-07T12:40:07.437399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.19898","snapshot_observed_at":"2026-08-07T12:27:48.666190Z","title":"Gencls++: Pushing the boundaries of generative classification in llms through comprehensive sft and rl studies across diverse datasets","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24500","last_updated":"2025-05-30T12:01:06Z","snapshot_observed_at":"2026-08-14T22:51:03.191116Z","submitted_at":"2025-05-30T12:01:06Z","title":"TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligence","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T12:27:48.666190Z"},"links":{"cited_paper":"/paper/2504.19898","citing_paper":"/paper/2505.24500"},"observation_digest":"sha256:c841eb5d7f28cd2308b7fb82f0f005a6aacf24d71f2b01ecd4e016a958192729","observation_id":"6e6e6052-e199-4952-ad4f-924d4dfd415b","resolution":{"observed_at":"2026-08-07T12:27:48.666190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"cited_work":{"arxiv_id":"2504.19898","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.19898","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gencls++: Pushing the boundaries of generative classification in llms through comprehensive sft and rl studies across diverse datasets","venue":null,"work_id":"c597a6af-aaa6-4db9-8c00-2eda8b792313","year":2025},"citing_paper":{"arxiv_id":"2604.00013","last_updated":"2026-04-12T03:30:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-03-10T12:48:41Z","title":"C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-15T13:51:40.334057Z"},"links":{"cited_paper":"/paper/2504.19898","citing_paper":"/paper/2604.00013"},"observation_digest":"sha256:d04000a500b71cdcaf5028d55f3394ce1e5a4e88dd027f7b771e0d4ec89a3d98","observation_id":"41712406-ee9d-4d6c-ba92-227d157e2bf3","resolution":{"observed_at":"2026-05-15T13:55:53.304156Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.19898/citation-record","integrity":"/paper/2504.19898/integrity","json":"/paper/2504.19898/citation-record.json","paper":"/paper/2504.19898"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:56.022783Z","title":"Claude 3.5 sonnet","venue":null,"work_id":"e6f0921c-e479-4e1f-ab1a-fb09b6991f15","year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.130009Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:0988c73e631e541c04d8f867282f00a3e0dc74e4803af5fbcd5d8e3f3c5ac420","observation_id":"5aa926c3-18f9-4fa2-b7e6-7a7574f15015","resolution":{"observed_at":"2026-08-16T05:43:56.028031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-16T05:43:55.135352Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.135352Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:b43e26498a4b331646d2e2f21cfddfd87aedf51ebb185355debcefdc4ef3278f","observation_id":"b66b3898-0de0-4f1d-8759-d2a7c3b71f51","resolution":{"observed_at":"2026-08-16T05:43:55.135352Z","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":"2026-08-16T05:43:56.006741Z","title":"Our next-generation model: Gemini 1.5","venue":null,"work_id":"74f06f99-ec1e-46a8-9815-a0e9dd599c97","year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.140832Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:772c4f05e7db09d49dee3d6b3a7955fd41c5e6a879eb19052fdafe28a9912fb9","observation_id":"c063cd36-aee4-4250-9f53-dea2c816ea3c","resolution":{"observed_at":"2026-08-16T05:43:56.012217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","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":"2026-08-16T05:43:55.146074Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.146074Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:2f241bf4a2b58b03ff22681a2b335266cadfa5da2ff40ef88a0b250fb9793ef7","observation_id":"081da6fd-256f-47b3-997a-d5dd5d570a9b","resolution":{"observed_at":"2026-08-16T05:43:55.146074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14992","last_updated":"2023-10-23T07:24:28Z","snapshot_observed_at":"2026-08-15T11:39:07.998070Z","submitted_at":"2023-05-24T10:28:28Z","title":"Reasoning with Language Model is Planning with World Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14992","snapshot_observed_at":"2026-08-16T05:43:55.151322Z","title":"J., Wang, Z., Wang, D","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.151322Z"},"links":{"cited_paper":"/paper/2305.14992","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:98deae4437e09ab43592dd5262f0dc52d473017902f63b941a7bb558226a9f9e","observation_id":"546bc826-f173-40d8-afb7-b96333f77cf9","resolution":{"observed_at":"2026-08-16T05:43:55.151322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00390","last_updated":"2024-06-29T10:09:49Z","snapshot_observed_at":"2026-08-20T11:11:46.142437Z","submitted_at":"2024-06-29T10:09:49Z","title":"Advancing Process Verification for Large Language Models via Tree-Based Preference Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00390","snapshot_observed_at":"2026-08-16T05:43:55.156433Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.156433Z"},"links":{"cited_paper":"/paper/2407.00390","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:83df7b0fca23a4b7e0ec914305712a388128c6e1785864b463302863d27e03ef","observation_id":"ecea5e92-c61d-439e-b4f0-9adeae4641be","resolution":{"observed_at":"2026-08-16T05:43:55.156433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07954","last_updated":"2024-03-23T13:54:44Z","snapshot_observed_at":"2026-08-18T23:53:31.744599Z","submitted_at":"2023-11-14T07:13:10Z","title":"A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.07954","snapshot_observed_at":"2026-08-16T05:43:55.162103Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.162103Z"},"links":{"cited_paper":"/paper/2311.07954","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:8eb1296d5d946cc19900329adb1a9261a42b5ad4637f0559a9215cbdd065b58a","observation_id":"0f8acb76-1fec-45ac-9de1-72444056c921","resolution":{"observed_at":"2026-08-16T05:43:55.162103Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.03262","last_updated":"2025-11-10T15:11:13Z","snapshot_observed_at":"2026-08-16T04:57:30.418363Z","submitted_at":"2025-01-04T02:08:06Z","title":"REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.03262","snapshot_observed_at":"2026-08-16T05:43:55.167991Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.167991Z"},"links":{"cited_paper":"/paper/2501.03262","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:66bd9097d5716fc5987d3cf26fd7900dca84bbf9335b9bee40500da64221c1b7","observation_id":"44b7ddf7-ff7f-48e1-8494-dac5c842035b","resolution":{"observed_at":"2026-08-16T05:43:55.167991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01798","last_updated":"2024-03-14T04:27:52Z","snapshot_observed_at":"2026-08-13T03:11:04.678829Z","submitted_at":"2023-10-03T04:56:12Z","title":"Large Language Models Cannot Self-Correct Reasoning Yet","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01798","snapshot_observed_at":"2026-08-16T05:43:55.173133Z","title":"S., Yu, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.173133Z"},"links":{"cited_paper":"/paper/2310.01798","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:fd84a0731b6c73049bf0e9877c94cbfb1f6f01716cc3f86cc1353944d2189355","observation_id":"2fc34dc6-f141-4d91-8aa5-e8e644290388","resolution":{"observed_at":"2026-08-16T05:43:55.173133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.09621","last_updated":"2025-02-13T18:59:46Z","snapshot_observed_at":"2026-08-17T11:53:37.450198Z","submitted_at":"2025-02-13T18:59:46Z","title":"MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09621","snapshot_observed_at":"2026-08-16T05:43:55.178439Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.178439Z"},"links":{"cited_paper":"/paper/2502.09621","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:df7f59dbf731a17461b599fcf587f9a7ca0f838c08133c22fcf705255ce0a01a","observation_id":"43395219-3aa2-4dea-8dbb-8e7c711bb315","resolution":{"observed_at":"2026-08-16T05:43:55.178439Z","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":"2026-08-16T05:43:55.183542Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.183542Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:90ba4bb1cb97432a938787ddc0282cdbb921644e4a40ae147ae3f0e67e659ae7","observation_id":"814e3594-bc20-42d3-a0c3-bd3d1faa31f1","resolution":{"observed_at":"2026-08-16T05:43:55.183542Z","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":"2026-08-16T05:43:55.188205Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.188205Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:254da98945560c043837ea129266d39e001dce0e96c147abdc4be69f1bbc2b03","observation_id":"49230c4f-9fee-422b-9a0e-ec815d93755d","resolution":{"observed_at":"2026-08-16T05:43:55.188205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.10954","last_updated":"2023-12-06T03:34:00Z","snapshot_observed_at":"2026-08-20T11:11:38.782689Z","submitted_at":"2023-09-19T22:41:44Z","title":"In-Context Learning for Text Classification with Many Labels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.10954","snapshot_observed_at":"2026-08-16T05:43:55.192925Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.192925Z"},"links":{"cited_paper":"/paper/2309.10954","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:393f52b9aec68deb358039b8113410ee3306e7532da841f57f2a0b148a6b60c7","observation_id":"f30f6a5c-287e-4ec6-89d2-7749818b0ea6","resolution":{"observed_at":"2026-08-16T05:43:55.192925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07316","last_updated":"2023-03-19T13:37:01Z","snapshot_observed_at":"2026-08-17T06:54:11.300249Z","submitted_at":"2022-10-13T19:42:08Z","title":"MTEB: Massive Text Embedding Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07316","snapshot_observed_at":"2026-08-16T05:43:55.198173Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.198173Z"},"links":{"cited_paper":"/paper/2210.07316","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:c4297dca252833d9b07113a84f5dff7b8fcb834d380301f71f3ce473a3ae2114","observation_id":"cb3f36fb-2fc6-43c8-96cb-2af382c0ea4a","resolution":{"observed_at":"2026-08-16T05:43:55.198173Z","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":"2026-08-16T05:43:55.991730Z","title":"Hello gpt-4o","venue":null,"work_id":"021bef84-3119-41b7-8506-d607f7003524","year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.203026Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:8bb9c28034f4a5fb1f58196b3db2b76dc6c4d53e9b2a78a9fcfc6bc626b2b476","observation_id":"db2c3021-5e18-4a9a-8f4d-1c233a5c5e11","resolution":{"observed_at":"2026-08-16T05:43:55.996429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T05:43:55.207496Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.207496Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:03ef646df6fc140f591ceb1da4c765cb658783af7da7589acb8e06971676ab96","observation_id":"cbcc62fb-59f8-40d2-b85f-58db336930ba","resolution":{"observed_at":"2026-08-16T05:43:55.207496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07157","last_updated":"2023-05-11T22:07:27Z","snapshot_observed_at":"2026-08-21T00:18:38.563720Z","submitted_at":"2023-05-11T22:07:27Z","title":"Exploring Zero and Few-shot Techniques for Intent Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07157","snapshot_observed_at":"2026-08-16T05:43:55.212254Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.212254Z"},"links":{"cited_paper":"/paper/2305.07157","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:5e1621761d11d22c5886d2b62a55dc0d88afecf3ee0af3798c8cc2b71792e0eb","observation_id":"e8f7cd1b-c9c9-4cb5-b6a8-64b38c19d3eb","resolution":{"observed_at":"2026-08-16T05:43:55.212254Z","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":"2026-08-16T05:43:55.966693Z","title":null,"venue":null,"work_id":"c9270143-fc63-465c-b8a2-e8646b0066b5","year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.217207Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:55d6b129df8515a6b2e3c2ee5ac483cd9c5bce6b36e3cbfcc40e7e139625ec18","observation_id":"8046cbd0-8df5-420f-b045-d39e503f1f9d","resolution":{"observed_at":"2026-08-16T05:43:55.971487Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.06476","last_updated":"2023-11-19T15:43:58Z","snapshot_observed_at":"2026-08-19T22:26:56.125157Z","submitted_at":"2023-02-08T09:44:51Z","title":"Is ChatGPT a General-Purpose Natural Language Processing Task Solver?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.06476","snapshot_observed_at":"2026-08-16T05:43:55.223083Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.223083Z"},"links":{"cited_paper":"/paper/2302.06476","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:f8095c5de2a185cba8910fbc0e3a9e7cd9f34581cd06e27ca583381146f837ad","observation_id":"a29fa7fb-aecc-4bf4-a21f-f9e278f3f673","resolution":{"observed_at":"2026-08-16T05:43:55.223083Z","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":"2026-08-16T05:43:55.951116Z","title":"D., Ermon, S., and Finn, C","venue":null,"work_id":"c0ba45c9-ec23-4aaf-8699-44fc88859c16","year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.228259Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:68559049959cdceafb5ec34428d473323fbffea5a89d5f480930d426c9161790","observation_id":"44935a20-69fd-4bb9-8b7b-59c3dcde572e","resolution":{"observed_at":"2026-08-16T05:43:55.956032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-16T05:43:55.935000Z","title":"J., and Lakshminarayanan, B","venue":null,"work_id":"d64af59b-86f7-4b33-bc68-31e198156e46","year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.233153Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:6d61074fcbbf430ac8207e8bcb7923fa1fd3a376bbc3fb3ab5d1bc9fd9da20f1","observation_id":"46465cd3-ddaf-4c08-8cb3-5d81248f21d6","resolution":{"observed_at":"2026-08-16T05:43:55.940313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12114","last_updated":"2024-06-17T21:45:48Z","snapshot_observed_at":"2026-08-19T11:37:44.421876Z","submitted_at":"2024-06-17T21:45:48Z","title":"Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12114","snapshot_observed_at":"2026-08-16T05:43:55.237972Z","title":"and Makrehchi, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.237972Z"},"links":{"cited_paper":"/paper/2406.12114","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:0aa10c0e9bbbaf3b2e5c20ac2081e3b56f56d7e79c38870c18342541818cb5d7","observation_id":"e989a0aa-0f5a-4a00-b0e1-d1f188a54c70","resolution":{"observed_at":"2026-08-16T05:43:55.237972Z","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":"2410.02028","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:43:55.581974Z","title":null,"venue":null,"work_id":"7118d3fa-d137-4522-9d5f-4215b8c09bd6","year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.243020Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:a5beb1c0f8f11c4cfe06b4bb3f1b084dfb9361f8a6d9273c7eef0cc66d403158","observation_id":"b0aefdb6-376e-4474-87a7-375f0a3c430f","resolution":{"observed_at":"2026-08-16T05:43:55.592752Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-20T07:04:06.309989Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-16T05:43:55.247579Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.247579Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:05e7974ac9e28e6fe56e73fc25db0b264791a68a69cac82510985641e2259bd3","observation_id":"ebbbb1e7-a882-43cd-a396-5eee507c8800","resolution":{"observed_at":"2026-08-16T05:43:55.247579Z","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":"2026-08-16T05:43:55.919242Z","title":null,"venue":null,"work_id":"175b115b-69a8-441c-9508-e3cd6c4f18aa","year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.252482Z"},"links":{"citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:485b740f28bc4f9c864e0836ff4764e61a5d748edfc6eaeba3855adc1ef3a450","observation_id":"c0b65536-95ec-407e-b80f-54a72a7f7d92","resolution":{"observed_at":"2026-08-16T05:43:55.924099Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-16T05:43:55.256948Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.256948Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:0a17bea10410dead4978ec23307a24d99beb92058cd3fea56f5ef3f94072a658","observation_id":"761cecdf-626b-40e8-b7b6-4573c7d85ac8","resolution":{"observed_at":"2026-08-16T05:43:55.256948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12183","last_updated":"2025-05-07T18:00:45Z","snapshot_observed_at":"2026-08-19T12:27:53.107321Z","submitted_at":"2024-09-18T17:55:00Z","title":"To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12183","snapshot_observed_at":"2026-08-16T05:43:55.262029Z","title":"D., Jiang, D., Wadhwa, M., Singhal, P., Zhao, X., Ye, X., Mahowald, K., and Durrett, G","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.262029Z"},"links":{"cited_paper":"/paper/2409.12183","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:5832d2c271f0d7ba3712b6f585fb0e6aa9b387320adae0c261a05d6ba09952d0","observation_id":"d5f03c67-be7a-4d52-a8c6-9d0bf7d4c1a1","resolution":{"observed_at":"2026-08-16T05:43:55.262029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.16419","last_updated":"2025-08-21T19:14:40Z","snapshot_observed_at":"2026-08-11T13:10:23.709172Z","submitted_at":"2025-03-20T17:59:38Z","title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.16419","snapshot_observed_at":"2026-08-16T05:43:55.266754Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.266754Z"},"links":{"cited_paper":"/paper/2503.16419","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:a37754fe56cde790e0724389f5cbd8a7e977a73965939d07c5e6d1072559aac6","observation_id":"1d7afb9f-ad77-453b-b633-bb9684442ca5","resolution":{"observed_at":"2026-08-16T05:43:55.266754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08377","last_updated":"2023-10-09T15:52:30Z","snapshot_observed_at":"2026-08-16T15:33:09.890111Z","submitted_at":"2023-05-15T06:24:45Z","title":"Text Classification via Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08377","snapshot_observed_at":"2026-08-16T05:43:55.271515Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.271515Z"},"links":{"cited_paper":"/paper/2305.08377","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:d8d3a471f793f59bfa8cbd86d56b21ae1a463e86e6f1ff4fba8581c795bb8559","observation_id":"87ff4ac3-5293-4353-9ca5-e731628e32a2","resolution":{"observed_at":"2026-08-16T05:43:55.271515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05986","last_updated":"2020-11-05T14:46:45Z","snapshot_observed_at":"2026-08-15T13:01:59.880479Z","submitted_at":"2020-04-13T15:02:29Z","title":"CLUE: A Chinese Language Understanding Evaluation Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05986","snapshot_observed_at":"2026-08-16T05:43:55.276400Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.276400Z"},"links":{"cited_paper":"/paper/2004.05986","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:1c30d996ecd687dd3d05be939ed13fe1e2d10d6d7e5712174e48d434261bc90b","observation_id":"5288a7da-aec2-4c4f-b562-f967101611ad","resolution":{"observed_at":"2026-08-16T05:43:55.276400Z","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":"2026-08-17T18:50:07.059564Z","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":"2026-08-16T05:43:55.281104Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.281104Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:4a833d202ab1f765be883e60f8b73aa0875fb4c6bced44b8267ed5a139ce8d2e","observation_id":"b37c8dcd-463d-4e22-ba8e-40bb1f61e746","resolution":{"observed_at":"2026-08-16T05:43:55.281104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03742","last_updated":"2024-10-13T10:21:29Z","snapshot_observed_at":"2026-08-20T11:11:37.273722Z","submitted_at":"2024-10-01T07:38:58Z","title":"Beyond Scalar Reward Model: Learning Generative Judge from Preference Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03742","snapshot_observed_at":"2026-08-16T05:43:55.285882Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.285882Z"},"links":{"cited_paper":"/paper/2410.03742","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:757725d250abc953b7afeb44529ba14b2470abb8deb515f5c854ef2196b7a2d5","observation_id":"b8354a36-06f4-4d5c-8578-c388c2c30710","resolution":{"observed_at":"2026-08-16T05:43:55.285882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09724","last_updated":"2024-04-01T13:50:51Z","snapshot_observed_at":"2026-08-18T03:01:28.009772Z","submitted_at":"2023-11-16T09:56:28Z","title":"OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09724","snapshot_observed_at":"2026-08-16T05:43:55.290892Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.290892Z"},"links":{"cited_paper":"/paper/2311.09724","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:4a0364e1a92dfe07ba8687e5da2d93a684f7fea3dd5e8e526eb585112554785b","observation_id":"dcd42fc2-1575-4491-bb80-c4755f88492f","resolution":{"observed_at":"2026-08-16T05:43:55.290892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14476","last_updated":"2025-05-20T01:37:34Z","snapshot_observed_at":"2026-08-18T05:01:20.543826Z","submitted_at":"2025-03-18T17:49:06Z","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14476","snapshot_observed_at":"2026-08-16T05:43:55.295517Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.295517Z"},"links":{"cited_paper":"/paper/2503.14476","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:462f493206b3e48a380f36fc3d618f5d514ab1c655e770b99926e91adf9398ce","observation_id":"d67a948d-5064-4295-9ccc-0952448898ca","resolution":{"observed_at":"2026-08-16T05:43:55.295517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15240","last_updated":"2025-02-22T10:21:46Z","snapshot_observed_at":"2026-08-20T17:13:38.857103Z","submitted_at":"2024-08-27T17:57:45Z","title":"Generative Verifiers: Reward Modeling as Next-Token Prediction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15240","snapshot_observed_at":"2026-08-16T05:43:55.300535Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.300535Z"},"links":{"cited_paper":"/paper/2408.15240","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:cbb8dbc008089c79dde4cd2383a129712bcfefda27843cfb34b33147c7d7b8df","observation_id":"0a29e391-360e-4539-be4e-fe40b7646368","resolution":{"observed_at":"2026-08-16T05:43:55.300535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08593","last_updated":"2020-01-08T23:02:36Z","snapshot_observed_at":"2026-08-21T16:54:14.450108Z","submitted_at":"2019-09-18T17:33:39Z","title":"Fine-Tuning Language Models from Human Preferences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.08593","snapshot_observed_at":"2026-08-16T05:43:55.305136Z","title":"M., Stiennon, N., Wu, J., Brown, T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-16T05:43:55.305136Z"},"links":{"cited_paper":"/paper/1909.08593","citing_paper":"/paper/2504.19898"},"observation_digest":"sha256:5e3c1586b5d869babbda134b94623276d78f98fc9049143542d38c95241490da","observation_id":"7442b44f-ebd3-453e-aefa-02bb27792c32","resolution":{"observed_at":"2026-08-16T05:43:55.305136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.19898","last_updated":"2025-04-28T15:30:58Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-17T08:05:28.071014Z","submitted_at":"2025-04-28T15:30:58Z","title":"GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasets"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":1,"verified_fuzzy":5},"total_outbound_references":36},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 24 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 3 inbound Pith citation observations for arXiv:2504.19898."}