{"as_of":"2026-08-04T16:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff4bd0f6ea2410d0d688437db7343dba764f84b378ad3271390cf46e5af15c17","coverage":[{"denominator":64,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":64,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-17T03:40:25.706499Z","state":"measured"},{"denominator":112,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":112,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":48,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T00:48:28.952743Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T12:15:01.137692Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2504.12501","last_updated":"2026-08-03T01:47:58Z","snapshot_observed_at":"2026-08-04T16:49:20.085151Z","submitted_at":"2025-04-16T21:36:46Z","title":"Reinforcement Learning from Human Feedback","version":9},"reference_index":170,"source":"pdf_text","source_observed_at":"2026-05-22T19:27:40.991325Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2504.12501"},"observation_digest":"sha256:2caa1ec2accfc46e5dc9e3eda61dc95e3d04076433e283e0cd3a488ecd3257d2","observation_id":"df745f2f-0866-4f33-b7f1-cbe7fbe5e9fa","resolution":{"observed_at":"2026-05-22T19:32:00.977531Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2505.23281","last_updated":"2026-01-14T21:39:58Z","snapshot_observed_at":"2026-08-02T10:05:44.330694Z","submitted_at":"2025-05-29T09:28:06Z","title":"MathArena: Evaluating LLMs on Uncontaminated Math Competitions","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T00:10:14.812539Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2505.23281"},"observation_digest":"sha256:863a0aba1ae480d680de362af52d8d3e412df87371878a1829ac3f1f20316be2","observation_id":"3f78d8d7-b2a3-4632-9083-f15a89340d50","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2507.04023","last_updated":"2026-04-23T08:06:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-05T12:31:17Z","title":"Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-19T06:25:12.799097Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2507.04023"},"observation_digest":"sha256:1c792de9e5bbaa9ad1cfa1e24992b8ebb7df901aae6d6a3c9649441d5dc819cf","observation_id":"8a857573-5dcc-4e4a-8842-ec8372391689","resolution":{"observed_at":"2026-05-19T06:27:07.269810Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2507.21433","last_updated":"2026-05-14T02:27:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-07-29T02:05:51Z","title":"ReasonCache: Accelerating Large Reasoning Model Serving through KV Cache Sharing","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-19T03:14:05.109011Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2507.21433"},"observation_digest":"sha256:cbe7cefcf6fb91d3999e95d28acd4300cff9e0de6d273aad30325e738ad27e72","observation_id":"ca875656-cd14-4e80-81b1-8a0755f4523c","resolution":{"observed_at":"2026-05-19T03:17:00.830862Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2509.08827","last_updated":"2025-10-09T17:08:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-10T17:59:43Z","title":"A Survey of Reinforcement Learning for Large Reasoning Models","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-18T00:02:24.352947Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2509.08827"},"observation_digest":"sha256:c7d5f41a01380122191ee9374d67c4241df9ceb150b679e84a08544e330629e2","observation_id":"a4710964-eb33-481d-ab96-754f1a9cb7f5","resolution":{"observed_at":"2026-05-18T00:02:24.461462Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2509.16343","last_updated":"2026-04-20T22:59:13Z","snapshot_observed_at":"2026-07-06T22:30:21.881075Z","submitted_at":"2025-09-19T18:34:08Z","title":"Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-18T15:19:59.478032Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2509.16343"},"observation_digest":"sha256:76899961439745802bbfc364fffecb3cc09d522ffccb87a250674ec22f9d790e","observation_id":"4c1e6eb3-6977-47c4-9c41-5057cd7f5a15","resolution":{"observed_at":"2026-05-18T15:21:32.839473Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2510.04265","last_updated":"2026-05-12T01:55:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-05T16:14:03Z","title":"Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation","version":4},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-05-18T10:04:39.223895Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2510.04265"},"observation_digest":"sha256:9e799895fdf5561bb086810bc1680c960bece6c590922571669b5ed71ecd8c8b","observation_id":"8a051f55-631c-4fe2-ba5a-ca5b49fa7303","resolution":{"observed_at":"2026-05-18T10:06:13.858270Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-08-03T09:37:05.721179Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.13433","last_updated":"2026-05-29T00:45:48Z","snapshot_observed_at":"2026-08-03T09:37:04.326965Z","submitted_at":"2026-01-19T22:37:30Z","title":"Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-03T09:37:05.721179Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2601.13433"},"observation_digest":"sha256:3a44ef48e7a9a59a48f2cbf31a8ec09f40804e99e326b9a0d751c69f5c9e81d4","observation_id":"5d21b7cb-1dac-4395-ba9a-d8826d8c2876","resolution":{"observed_at":"2026-08-03T09:37:05.721179Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2601.14249","last_updated":"2026-05-25T10:44:31Z","snapshot_observed_at":"2026-08-03T09:22:41.086646Z","submitted_at":"2026-01-20T18:58:10Z","title":"Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T12:23:56.318846Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2601.14249"},"observation_digest":"sha256:ea3831c583733be622d348ab4acffa4467fa39d4981bd102e631b4647de52b50","observation_id":"43239800-29bb-4948-ab42-335f3500ecd2","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-08-03T09:22:42.385847Z","title":"Phi-4-reasoning technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.14249","last_updated":"2026-05-25T10:44:31Z","snapshot_observed_at":"2026-08-03T09:22:41.086646Z","submitted_at":"2026-01-20T18:58:10Z","title":"Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment","version":5},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T09:22:42.385847Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2601.14249"},"observation_digest":"sha256:4c1d5671419f1482923a512000eae834a6626592cbc85ee9cd355802dedf35a0","observation_id":"99465e2b-03fe-4e2a-8ef5-ac45a23b13bb","resolution":{"observed_at":"2026-08-03T09:22:42.385847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2601.18832","last_updated":"2026-05-09T13:17:24Z","snapshot_observed_at":"2026-07-06T22:43:07.053570Z","submitted_at":"2026-01-25T18:16:17Z","title":"The Geometric Reasoner: Manifold-Informed Latent Foresight Search for Long-Context Reasoning","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T10:42:33.579501Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2601.18832"},"observation_digest":"sha256:75e154ea5ac12998f02fa85ef0d2268937598cf830945e13c5e8dcaeaeeed54e","observation_id":"62bd258a-3b18-4a7d-9287-0db06f442bc8","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2601.21684","last_updated":"2026-05-05T06:17:11Z","snapshot_observed_at":"2026-08-02T07:26:05.475014Z","submitted_at":"2026-01-29T13:18:36Z","title":"Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T09:53:14.045466Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2601.21684"},"observation_digest":"sha256:25e625ab62d7327af4c78e993a9a03b528639c8c4c8fe5ce2f7d776ee068aec7","observation_id":"1e350365-2b6d-4b50-8e89-17be021f38e5","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2602.09782","last_updated":"2026-05-08T07:55:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-02-10T13:42:12Z","title":"Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T02:34:43.248634Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2602.09782"},"observation_digest":"sha256:1849092630f44739b60fa1b1c74a9306e29c61f70c686e45f5f1da515fba429a","observation_id":"aad44ffe-f341-491e-aca1-c56bf64094cf","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-08-03T01:18:01.894936Z","title":"Muhammad Adnan, Akhil Arunkumar, Gaurav Jain, Prashant J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.10238","last_updated":"2026-06-26T10:01:58Z","snapshot_observed_at":"2026-08-03T01:17:29.085975Z","submitted_at":"2026-02-10T19:34:15Z","title":"Learning to Evict from Key-Value Cache","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T01:18:01.894936Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2602.10238"},"observation_digest":"sha256:92df4d21b108129527a1ec703f31a192b36867212038c0f0bfd273884ef68095","observation_id":"313f32af-5eab-45e7-b000-c5137af0bc82","resolution":{"observed_at":"2026-08-03T01:18:01.894936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-08-03T09:42:58.204848Z","title":"Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.11173","last_updated":"2026-05-23T16:54:44Z","snapshot_observed_at":"2026-08-03T09:42:45.963684Z","submitted_at":"2026-01-19T14:07:10Z","title":"Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T09:42:58.204848Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2602.11173"},"observation_digest":"sha256:f3b59335478e3092cc05c26302259033f31764d2277ec740a129e73a2f89d8a5","observation_id":"ba6c202a-3788-4db8-9695-0408dfbc8dca","resolution":{"observed_at":"2026-08-03T09:42:58.204848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2603.10960","last_updated":"2026-03-11T16:47:41Z","snapshot_observed_at":"2026-08-02T16:20:05.305021Z","submitted_at":"2026-03-11T16:47:41Z","title":"Ranking Reasoning LLMs under Test-Time Scaling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T13:33:18.767999Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2603.10960"},"observation_digest":"sha256:f0cc0e034c933d4b6c33dc7c3c3702527740c00b98e3312e5b52064bb9eca901","observation_id":"a55d5afc-cbfb-46a5-be08-5fe0e1c730f4","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2603.17305","last_updated":"2026-05-19T16:22:05Z","snapshot_observed_at":"2026-07-06T22:49:28.866886Z","submitted_at":"2026-03-18T03:00:42Z","title":"Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-21T11:23:26.852673Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2603.17305"},"observation_digest":"sha256:0cde14083a5d33788180a5566ac2783c7dc5960809a13407a8a01d14717db847","observation_id":"c2f5d83b-9b0d-4ab0-a196-c9f7d925ff05","resolution":{"observed_at":"2026-05-21T11:24:08.468108Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.03231","last_updated":"2026-04-03T17:59:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-03T17:59:51Z","title":"CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-13T20:28:30.864143Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.03231"},"observation_digest":"sha256:fe72307c920deda0464fb0778cb2089f297ed3eafc17396da4009e8398bf1eb9","observation_id":"17b61778-41a9-4fb5-bb1b-cfbda779c868","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.07035","last_updated":"2026-05-18T19:31:30Z","snapshot_observed_at":"2026-07-06T22:55:24.459130Z","submitted_at":"2026-04-08T12:50:52Z","title":"Unified Deployment-Aware Evaluation of Open Reasoning Language Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T17:49:52.594972Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.07035"},"observation_digest":"sha256:c4f798aee6e98f961d70da1a8e1b1922ee70fb6b3356668f819bada8d2233d7e","observation_id":"7c3d0251-d398-4903-ad22-b42f4609d85d","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.07035","last_updated":"2026-05-18T19:31:30Z","snapshot_observed_at":"2026-07-06T22:55:24.459130Z","submitted_at":"2026-04-08T12:50:52Z","title":"Unified Deployment-Aware Evaluation of Open Reasoning Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-21T09:42:33.144129Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.07035"},"observation_digest":"sha256:ffc0edae8dac79ed1ad052f3eba3392656f9d0e45da898e3000d4306d059d9fe","observation_id":"6c6f73d2-f14d-4bed-b812-4956cf574142","resolution":{"observed_at":"2026-05-21T09:44:05.752285Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.07864","last_updated":"2026-06-28T14:24:43Z","snapshot_observed_at":"2026-07-13T00:12:30.852396Z","submitted_at":"2026-04-09T06:24:54Z","title":"ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T18:36:23.127141Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.07864"},"observation_digest":"sha256:68d519c73d81581d4f39beb37be2125774d7502fd99146f1f066f067b6709419","observation_id":"31225b90-44ea-47b4-bbf4-f7091b96764d","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.08299","last_updated":"2026-04-19T12:06:32Z","snapshot_observed_at":"2026-07-06T22:57:26.678728Z","submitted_at":"2026-04-09T14:32:07Z","title":"SeLaR: Selective Latent Reasoning in Large Language Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-10T18:27:36.132030Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.08299"},"observation_digest":"sha256:5f48d8dd3a11a8964e64364827978a607df8393664c5e94d33f740eb32e2e6ce","observation_id":"373706ed-23ef-412c-a036-4250ad0f5a2b","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.17940","last_updated":"2026-04-20T08:20:53Z","snapshot_observed_at":"2026-07-06T23:04:55.190916Z","submitted_at":"2026-04-20T08:20:53Z","title":"When AI Models Become Dependencies: Studying the Evolution of Pre-Trained Model Reuse in Downstream Software Systems","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T04:26:31.894139Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.17940"},"observation_digest":"sha256:9af63b6fa14f3083d2deb3a681f81e0735afcc3f3e4deb830c4c65bee1d1a904","observation_id":"b5991c9b-da4e-4930-94de-065ab2571ab5","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2604.25235","last_updated":"2026-04-29T07:06:15Z","snapshot_observed_at":"2026-07-06T23:11:07.254460Z","submitted_at":"2026-04-28T05:30:18Z","title":"VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-07T16:46:01.749101Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2604.25235"},"observation_digest":"sha256:e5519393b5f1bcb65be1961bc8c2c861f88e6e2d4e797651caf73a440e6419a1","observation_id":"9b53877c-5c07-4ecd-95b9-a1dc1add3399","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.00072","last_updated":"2026-04-30T11:50:32Z","snapshot_observed_at":"2026-07-06T23:13:33.799847Z","submitted_at":"2026-04-30T11:50:32Z","title":"XekRung Technical Report","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-09T20:55:10.400291Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.00072"},"observation_digest":"sha256:dc65ecfaae498ec5948eaa42f0c272f3c4737cebac93c0e25f4cbbc5852fb1da","observation_id":"53a9fc33-c0a5-4561-aaa9-d6953f4530d4","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.00674","last_updated":"2026-05-15T17:10:37Z","snapshot_observed_at":"2026-07-06T23:14:01.852096Z","submitted_at":"2026-05-01T13:56:34Z","title":"Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-05-09T19:18:42.039311Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.00674"},"observation_digest":"sha256:1df781809cbc1ffdac3fbed538e0e6d420181680b970e104c2902c97f8bafad5","observation_id":"2e4d2d91-0987-4f84-b766-cc5cca0c8ffb","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.00674","last_updated":"2026-05-15T17:10:37Z","snapshot_observed_at":"2026-07-06T23:14:01.852096Z","submitted_at":"2026-05-01T13:56:34Z","title":"Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-05-19T18:19:01.759230Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.00674"},"observation_digest":"sha256:35aef48d54761b6a0ab85dc784b9aac42e9cc4d96f0024a2d8c0b586da5cb07c","observation_id":"ef6863f1-78cd-43e5-ade2-5797199b0003","resolution":{"observed_at":"2026-05-19T18:22:43.544420Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.02290","last_updated":"2026-05-04T07:26:41Z","snapshot_observed_at":"2026-08-02T10:20:40.458806Z","submitted_at":"2026-05-04T07:26:41Z","title":"Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-09T16:29:05.186607Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.02290"},"observation_digest":"sha256:8d3390a8f1c6025f208dc1715490fdd21ab01245a97f8fa9fc9339abdd2b3fc4","observation_id":"9c0f06a0-e1fe-49c9-9cc7-5f7779f47031","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.09260","last_updated":"2026-05-10T02:11:52Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-10T02:11:52Z","title":"Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-12T04:52:07.910558Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.09260"},"observation_digest":"sha256:2ca7c9994f8500f89608e1f1d97415101e0a6a66e8670efff670210c3f7762c9","observation_id":"79a9a8d6-b714-494b-bb18-01b84c5dda6c","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.11553","last_updated":"2026-05-12T05:35:00Z","snapshot_observed_at":"2026-08-04T08:21:56.364054Z","submitted_at":"2026-05-12T05:35:00Z","title":"TwiSTAR:Think Fast, Think Slow, Then Act,Generative Recommendation with Adaptive Reasoning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-13T01:40:04.856714Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.11553"},"observation_digest":"sha256:2d491ce2aeca4e6fc7e3293e5c812bed877e93ae98f5e0b36c867143c8035034","observation_id":"6acf9e8c-0c6a-46a8-87b0-0b2e76b51ec4","resolution":{"observed_at":"2026-05-17T03:40:25.972991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.17228","last_updated":"2026-05-17T02:28:25Z","snapshot_observed_at":"2026-07-06T23:28:16.927009Z","submitted_at":"2026-05-17T02:28:25Z","title":"Artificial Intolerance: Stigmatizing Language in Clinical Documentation Skews Large Language Model Decision-Making","version":1},"reference_index":107,"source":"arxiv_source","source_observed_at":"2026-05-20T14:48:55.203993Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.17228"},"observation_digest":"sha256:24b4c97999348e083ed7ee663b13d794096cd19d6cbb1c6d26b894ed72ad89b7","observation_id":"b38ec3d3-e27d-4ceb-9e5d-0d87f900f57f","resolution":{"observed_at":"2026-05-20T14:53:23.430395Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.18163","last_updated":"2026-05-18T10:08:09Z","snapshot_observed_at":"2026-08-03T02:20:00.851119Z","submitted_at":"2026-05-18T10:08:09Z","title":"TRACE: Trajectory Correction from Cross-layer Evidence for Hallucination Reduction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-20T10:28:03.944764Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.18163"},"observation_digest":"sha256:8ef61a275a1def673490a22bb82f76c667b21bf74ace90077d6be9407e3828b3","observation_id":"26af29a6-5d40-4cc0-8069-809003dc0b7c","resolution":{"observed_at":"2026-05-20T10:28:11.826210Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.19228","last_updated":"2026-06-07T18:34:11Z","snapshot_observed_at":"2026-07-31T16:56:03.737552Z","submitted_at":"2026-05-19T00:57:51Z","title":"Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-20T06:43:23.870127Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.19228"},"observation_digest":"sha256:64309975072591c0d0fb8c55bc53bad7864f96d70830f43d2dd5404aa66268e3","observation_id":"1f7a6f04-65d1-4fd2-b627-b0c4b1db699a","resolution":{"observed_at":"2026-05-20T06:48:05.949143Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.20075","last_updated":"2026-05-19T16:28:53Z","snapshot_observed_at":"2026-08-01T16:11:19.969726Z","submitted_at":"2026-05-19T16:28:53Z","title":"CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-20T05:25:51.655208Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.20075"},"observation_digest":"sha256:315576011b1d2a7d4f627d7a645e597e30caff8a6968a5d4215038735aa6fa0e","observation_id":"fd29940f-c883-473c-98cf-fbdb501a3432","resolution":{"observed_at":"2026-05-20T05:28:04.988837Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.21851","last_updated":"2026-05-22T00:42:32Z","snapshot_observed_at":"2026-07-06T23:32:15.891169Z","submitted_at":"2026-05-21T00:55:13Z","title":"OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-22T08:10:55.720464Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.21851"},"observation_digest":"sha256:41adb85d332b70cb8c22059a9a0a290d855711155ee9511436b1ced5eba34fba","observation_id":"9f0cc0ec-4644-405c-ae32-c1e8124165df","resolution":{"observed_at":"2026-05-22T08:11:17.051924Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2605.21851","last_updated":"2026-05-22T00:42:32Z","snapshot_observed_at":"2026-07-06T23:32:15.891169Z","submitted_at":"2026-05-21T00:55:13Z","title":"OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T05:47:33.925413Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2605.21851"},"observation_digest":"sha256:8ecb715d15baf115eadd5d83a0695450d73c038fea30a806d6e63e1d4d944d78","observation_id":"3c9db0b1-dd96-4e22-bbf4-4e04e3685f9f","resolution":{"observed_at":"2026-05-25T05:50:24.328443Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.01249","last_updated":"2026-06-17T04:44:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-31T14:04:51Z","title":"Trust Region On-Policy Distillation","version":3},"reference_index":235,"source":"arxiv_source","source_observed_at":"2026-06-28T17:38:50.313305Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.01249"},"observation_digest":"sha256:534da5bcfa7eed77d6f83e4e1e049126e155997ce47dab6a832cd0fb6d3bf34c","observation_id":"7dbd29ac-5ccb-466c-8c22-9e5bdf90b7a5","resolution":{"observed_at":"2026-07-01T20:56:13.454774Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.03057","last_updated":"2026-06-02T02:45:41Z","snapshot_observed_at":"2026-08-02T19:11:37.406781Z","submitted_at":"2026-06-02T02:45:41Z","title":"Rethinking Molecular Text Representations for LLMs: An Empirical Study","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-06-28T11:18:47.561764Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.03057"},"observation_digest":"sha256:6277037bff74ba8bc6014e9c49bc2d4131725ad631514abd7bb65c513be5c1c8","observation_id":"0dab8cde-a0a2-4e65-ba22-f98d44567788","resolution":{"observed_at":"2026-07-02T02:06:26.456584Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.06745","last_updated":"2026-06-04T21:57:27Z","snapshot_observed_at":"2026-07-06T23:46:28.942186Z","submitted_at":"2026-06-04T21:57:27Z","title":"When to Think Deeply: Inhibitory Deliberation for LLM Reasoning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-28T01:07:47.737574Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.06745"},"observation_digest":"sha256:35e5b2d4ac776b6df7505ee2a4f62b869ed03c87047826dd6393506f9cd0093f","observation_id":"731654d1-56e4-40de-9631-570d2e7fbbf6","resolution":{"observed_at":"2026-07-02T13:36:59.359240Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.10254","last_updated":"2026-06-08T23:40:34Z","snapshot_observed_at":"2026-07-31T10:24:53.794624Z","submitted_at":"2026-06-08T23:40:34Z","title":"RealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-27T16:02:04.749003Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.10254"},"observation_digest":"sha256:dc4967d430e959e61465cb6f160bc9d140ab5aea789842bc42cf8d01309aba03","observation_id":"aa0a2c45-257b-469e-9752-70b6c9fd9a24","resolution":{"observed_at":"2026-07-03T02:17:34.947472Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.11052","last_updated":"2026-06-09T16:17:19Z","snapshot_observed_at":"2026-07-06T23:50:11.160843Z","submitted_at":"2026-06-09T16:17:19Z","title":"Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-27T13:08:57.218711Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.11052"},"observation_digest":"sha256:ff091007dfa7beace4b260aedb239244e479ee9deb9b18d52f6a7fb702cb57a6","observation_id":"fc87d099-da85-4bec-8d63-b1f8c266294b","resolution":{"observed_at":"2026-06-27T13:10:55.926358Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.11232","last_updated":"2026-05-29T02:36:10Z","snapshot_observed_at":"2026-07-29T22:58:13.464216Z","submitted_at":"2026-05-29T02:36:10Z","title":"Every Act Has Its Price: Compressed Moral Composition in Frontier LLMs","version":1},"reference_index":109,"source":"arxiv_source","source_observed_at":"2026-06-28T22:49:03.124658Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.11232"},"observation_digest":"sha256:063f8ca5cd9264dc969c1b23d6fd1a6b3a43db38916d4d4f8d9263fcae611a8c","observation_id":"7d1d8372-8a79-4d8e-9e06-6e1433bf4fa3","resolution":{"observed_at":"2026-06-28T22:52:45.529339Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.17682","last_updated":"2026-06-16T08:48:58Z","snapshot_observed_at":"2026-08-01T05:53:08.805029Z","submitted_at":"2026-06-16T08:48:58Z","title":"From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-27T00:59:50.038405Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.17682"},"observation_digest":"sha256:ec4694853672810c5a12670a26aa53e7f04512152bc65b65e7084fbb1649c123","observation_id":"23584aa2-013b-4936-9d59-1e74d3f2c407","resolution":{"observed_at":"2026-06-27T01:00:19.860001Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.29067","last_updated":"2026-06-27T19:56:55Z","snapshot_observed_at":"2026-07-07T00:03:07.870153Z","submitted_at":"2026-06-27T19:56:55Z","title":"ThinkProbe: Beyond Accuracy -- Structural Profiling of Open-Ended LLM Reasoning Traces via Non-Generative Thought Graphs","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-06-30T09:23:26.386399Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.29067"},"observation_digest":"sha256:e63050d03b06e6e62757a929aac143f6c7e5eb821c7e1a789c77b851e59a161f","observation_id":"ab31bb19-93ae-4bcc-a5bc-98c156ae0781","resolution":{"observed_at":"2026-06-30T09:24:32.205313Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":"2504.21318","doi":"10.48550/arxiv.2504.21318","metadata_source":"pith","pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Phi-4-reasoning Technical Report","venue":"cs.AI","work_id":"e3e80b6c-c1bb-4244-b349-91cdcc0dd9a3","year":2025},"citing_paper":{"arxiv_id":"2606.31308","last_updated":"2026-06-30T08:18:45Z","snapshot_observed_at":"2026-07-30T01:03:38.628473Z","submitted_at":"2026-06-30T08:18:45Z","title":"Benchmarking Large Language Models on Floating-Point Error Classification","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-01T05:39:01.180338Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2606.31308"},"observation_digest":"sha256:aba333e554e2daf8392b4d41815115625773bd12992a20a8d3885b4a0ec5a198","observation_id":"71834db7-0c7d-4d5c-b9db-c7e3ed0bd1ab","resolution":{"observed_at":"2026-07-01T10:15:44.918943Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-08-01T10:26:08.486717Z","title":"arXiv preprint arXiv:2504.21318 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20241","last_updated":"2026-07-22T15:01:10Z","snapshot_observed_at":"2026-08-01T10:25:59.503767Z","submitted_at":"2026-07-22T15:01:10Z","title":"On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T10:26:08.486717Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2607.20241"},"observation_digest":"sha256:f9d0379f75bf863301a0f0ba56b6bf1ea8da8e7b78fcbb36313a83823200e707","observation_id":"a40a5c98-377b-44d0-ad5a-56ecf542cd22","resolution":{"observed_at":"2026-08-01T10:26:08.486717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-07-31T16:21:12.065335Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28150","last_updated":"2026-07-30T12:56:05Z","snapshot_observed_at":"2026-08-03T00:05:06.522190Z","submitted_at":"2026-07-30T12:56:05Z","title":"SmartGen: Seamless Disaggregated LLM Inference with Selective KV Cache Transfer","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-31T16:21:12.065335Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2607.28150"},"observation_digest":"sha256:b9430271b7c22e18603fa2da1e4f845aac3983a3cbd700c5b805303ee27f704e","observation_id":"5a1b21e5-e5f4-4521-aeb6-f6de3eb6b26f","resolution":{"observed_at":"2026-07-31T16:21:12.065335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21318","snapshot_observed_at":"2026-08-04T00:48:28.952743Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00326","last_updated":"2026-07-31T22:26:45Z","snapshot_observed_at":"2026-08-04T16:41:15.064562Z","submitted_at":"2026-07-31T22:26:45Z","title":"Learning to Coordinate Symbolic Tools: LLM Agents for Verified Sum-of-Squares Certificates","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T00:48:28.952743Z"},"links":{"cited_paper":"/paper/2504.21318","citing_paper":"/paper/2608.00326"},"observation_digest":"sha256:4dbfc15bd8decf6e63b0fa30170de3ab7c71f5ae3cf2eca4d118cabc65c690dd","observation_id":"35da46c7-9b11-403c-b1cb-b2cdd374ecdf","resolution":{"observed_at":"2026-08-04T00:48:28.952743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.21318/citation-record","integrity":"/paper/2504.21318/integrity","json":"/paper/2504.21318/citation-record.json","paper":"/paper/2504.21318"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.14219","last_updated":"2024-08-30T21:17:17Z","snapshot_observed_at":"2026-07-06T18:03:47.096406Z","submitted_at":"2024-04-22T14:32:33Z","title":"Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone","version":4},"cited_work":{"arxiv_id":"2404.14219","doi":"10.48550/arxiv.2404.14219","metadata_source":"pith","pith_arxiv_id":"2404.14219","snapshot_observed_at":"2026-07-10T13:57:06.708632Z","title":"Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone","venue":"cs.CL","work_id":"feef9556-a016-493c-abd2-0c97a23a7ebf","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2404.14219","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:686682ee3b169d3e3018938341652271eb5b40b3ddc98adcfce690fa402b4ea5","observation_id":"884eb5c4-2c6f-4fcb-8561-c79b42985f6b","resolution":{"observed_at":"2026-05-17T03:40:25.821543Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.08905","last_updated":"2024-12-12T03:37:41Z","snapshot_observed_at":"2026-08-02T08:41:10.128841Z","submitted_at":"2024-12-12T03:37:41Z","title":"Phi-4 Technical Report","version":1},"cited_work":{"arxiv_id":"2412.08905","doi":"10.48550/arxiv.2412.08905","metadata_source":"pith","pith_arxiv_id":"2412.08905","snapshot_observed_at":"2026-07-11T00:47:43.055842Z","title":"Phi-4 Technical Report","venue":"cs.CL","work_id":"b6274271-7af9-4ee8-993b-ba1ba4205ba8","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2412.08905","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:0c9dc43e3aa053256b7859af06249c0e00253c5c02147870dba06c1608c3bd13","observation_id":"70f4f4a0-2245-4405-8cbe-5b2ae0de7fa4","resolution":{"observed_at":"2026-05-17T03:40:25.743418Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-02T03:08:11.323957+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T03:08:11.323957+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"KITAB: evaluating llms on constraint satisfaction for information retrieval","venue":null,"work_id":"aaa04fb3-c48c-4dd7-af3a-fdd9d02aab90","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:eb39d87c4f589ef96feb8d35223cb19bc36dae8a3468a743e45a146de4bc0bba","observation_id":"0c522f1b-f36d-4fed-8c6f-7a155b72c24a","resolution":{"observed_at":"2026-05-17T03:40:25.894984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Aime 83-24","venue":null,"work_id":"4b96ed08-bbb1-4ae7-9952-1a0850c4901e","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:65b4dfe1a0fe63305da441e10bc8372cdda3910396f4bc2ccf5e735984488992","observation_id":"405b4788-a173-499f-9cfe-f84552267446","resolution":{"observed_at":"2026-05-17T03:40:25.898840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Aime 2025","venue":null,"work_id":"7b05bc8a-1b03-4a13-9850-a3c06a16b3b4","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:334db3d643d0644c5da01d081d06c68e679c43d957825cc70cf34299d6f00f78","observation_id":"b3c289d0-e7da-4350-b714-d80962887c12","resolution":{"observed_at":"2026-05-17T03:40:25.902043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06565","last_updated":"2016-07-25T17:23:29Z","snapshot_observed_at":"2026-07-06T05:00:46.434335Z","submitted_at":"2016-06-21T13:37:05Z","title":"Concrete Problems in AI Safety","version":2},"cited_work":{"arxiv_id":"1606.06565","doi":"10.48550/arxiv.1606.06565","metadata_source":"pith","pith_arxiv_id":"1606.06565","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Concrete Problems in AI Safety","venue":"cs.AI","work_id":"c8d14fbe-6eab-464a-95b3-778aabd82fa3","year":2016},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/1606.06565","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:8a812b9b54f88ebb40bd542c2cd4ac3ff4a30b042a741eb032bba2014c624304","observation_id":"1902c78c-a273-469b-a505-0c7b317d526d","resolution":{"observed_at":"2026-05-17T03:40:25.866001Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-13T23:49:52.215761+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T23:49:52.215761+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Claude 3.7 sonnet.https://www.anthropic.com/news/claude-3-7-sonnet","venue":null,"work_id":"7b5105bb-ec46-490d-a999-dee68913c4b2","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:6bf9b0f8855489c77fe0978c48724dccb31e61dddda592ca6d97826294a2550b","observation_id":"f20ae402-9939-4ef4-8cab-0cfeeb0f4147","resolution":{"observed_at":"2026-05-17T03:40:25.909069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.08679","last_updated":"2026-06-16T17:36:22Z","snapshot_observed_at":"2026-07-06T20:50:51.469809Z","submitted_at":"2025-03-11T17:56:30Z","title":"Chain-of-Thought Reasoning In The Wild Is Not Always Faithful","version":6},"cited_work":{"arxiv_id":"2503.08679","doi":"10.48550/arxiv.2503.08679","metadata_source":"pith","pith_arxiv_id":"2503.08679","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-thought reasoning in the wild is not always faithful.arXiv preprint:2503.08679","venue":"cs.AI","work_id":"221c289d-ba9c-41b9-a1d6-7ea026fdcc9b","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2503.08679","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:44847b4b863052650d5f61d8901e7e6ec6eb089ebc49a3bf9824da440343e9a9","observation_id":"9194b23c-8816-44fa-a0e8-38533ac977b9","resolution":{"observed_at":"2026-06-01T03:02:49.618587Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10566","last_updated":"2024-09-13T18:01:49Z","snapshot_observed_at":"2026-07-06T19:16:14.638445Z","submitted_at":"2024-09-13T18:01:49Z","title":"Eureka: Evaluating and Understanding Large Foundation Models","version":1},"cited_work":{"arxiv_id":"2409.10566","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.10566","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Eureka: Evaluating and understanding large foundation models","venue":null,"work_id":"ff2b4604-378c-441d-8c92-805e6852b3dc","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2409.10566","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:d638673ceef15f87726b94ca6214ac614cc1e296d37bd89499630f416b5c00d7","observation_id":"14ac81e6-d67c-4614-990e-1fb3a65762d9","resolution":{"observed_at":"2026-05-17T03:40:25.874331Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.00294","last_updated":"2025-03-31T23:40:28Z","snapshot_observed_at":"2026-08-04T13:19:45.872454Z","submitted_at":"2025-03-31T23:40:28Z","title":"Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead","version":1},"cited_work":{"arxiv_id":"2504.00294","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.00294","snapshot_observed_at":"2026-07-01T09:05:37.594479Z","title":"Inference-time scaling for complex tasks: Where we stand and what lies ahead","venue":null,"work_id":"f3ff70b3-3801-4176-9fd9-a2e3efa65daf","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2504.00294","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:1b6754750dab58f797f7d0fcf4671bef83d2360af0fd2d4070086941881e2f1d","observation_id":"59c63ade-c704-4bfb-a7bb-4bcd342082f9","resolution":{"observed_at":"2026-05-17T03:40:25.879473Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-08T00:54:21.409492Z","title":"Matharena: Evaluating llms on uncontaminated math competitions, February 2025","venue":null,"work_id":"3130a6c6-5158-45d2-858b-1029c80a84d2","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:23cc73f0fe7882f7f0002938cbc2999a7eb1c0eac7a6b2c46376d33efd7de52d","observation_id":"5e85f7d8-de6a-40db-82a5-23f435a81586","resolution":{"observed_at":"2026-05-17T03:40:25.921108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Designing disaggregated evaluations of ai systems: Choices, considera- tions, and tradeoffs","venue":null,"work_id":"e4e9b9a5-7d79-416b-92cb-8ac3d720c8b8","year":2021},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:2bdf27254995bbfc6836c542c69a148ff8e39c156a3efc7daf28d5ab8442f19b","observation_id":"cbe872af-1240-4f64-9c6e-3f24462e89c1","resolution":{"observed_at":"2026-05-17T03:40:25.923933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.22584","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T13:46:59.035586Z","title":"Benchagents: Automated benchmark creation with agent interaction","venue":null,"work_id":"e848fb3c-7495-4586-928a-2026228ebe4e","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:42e9b9e4f4c9043c523cf2cbb3f736319f02bed805132cfa206d3d3abda511fb","observation_id":"4b9ba938-2cae-4a2f-92a9-1091500c1dd9","resolution":{"observed_at":"2026-05-17T03:40:25.885399Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15595","last_updated":"2023-06-28T04:26:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-27T16:26:26Z","title":"Extending Context Window of Large Language Models via Positional Interpolation","version":2},"cited_work":{"arxiv_id":"2306.15595","doi":"10.48550/arxiv.2306.15595","metadata_source":"pith","pith_arxiv_id":"2306.15595","snapshot_observed_at":"2026-07-10T20:07:33.466282Z","title":"Extending Context Window of Large Language Models via Positional Interpolation","venue":"cs.CL","work_id":"c8b6df85-e7da-4bd8-90a4-d309cc2a0f60","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2306.15595","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:0afe0a90ae294b5a1bec700305318b4eefbb4f14752e5913e3885128723feb33","observation_id":"975589be-8544-4620-88b4-f511fbc5888c","resolution":{"observed_at":"2026-05-17T03:40:25.890787Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-01T10:38:12.283235+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T10:38:12.283235+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Reinforcement learning for reasoning in small llms: What works and what doesn’t","venue":null,"work_id":"1146bd73-24c0-4e1c-b597-c0031c876817","year":null},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:646bb4df4020bd55ad1aa3f0d9dc97c35a8a279018a87078bcbf2fbe4e49d793","observation_id":"aa6524b6-d3ce-4db7-8f1a-ed4512309cd7","resolution":{"observed_at":"2026-05-17T03:40:25.931552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2503.16219","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T21:16:13.421349Z","title":"Reinforcement learning for reasoning in small llms: What works and what doesn’t","venue":null,"work_id":"efe39feb-3afa-48ed-9725-593906f19aac","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:0dfba4d33895df04a74aa83bc0232be6862d43d0e278c357fd5ba2380aeb54c5","observation_id":"26d8073d-d36d-46a8-a2b1-0b2749b72f0c","resolution":{"observed_at":"2026-05-17T03:40:25.747904Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Omni-math: A universal olympiad level mathematic benchmark for large language models.ICLR","venue":null,"work_id":"e9bd2b6e-d66d-46c9-852c-5f3c150450e4","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:0aef5628dfcca95af402c9e69d78edb2b8b57ae9b70606a040a2e72d2cf57f00","observation_id":"a0d419ea-9ae3-4750-a7f9-0201382ee9fd","resolution":{"observed_at":"2026-05-17T03:40:25.936482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-04T21:30:09.680521Z","title":"Scaling laws for reward model overoptimization","venue":null,"work_id":"52561758-0cb3-4ae1-8db4-d20a08e886a1","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:af3d1110344c94997f1161fd8fd44c4cf93203023e6ad58bc9c56e843844bb5b","observation_id":"b9aef3d6-f435-4f7a-9fc4-0d4910ffe383","resolution":{"observed_at":"2026-05-17T03:40:25.938967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Gemini flash thinking","venue":null,"work_id":"f2710b8b-b58a-43cc-96aa-da208014bd58","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:f8ec6b54ff9e424b2c625db1c197a8db320aeb535ca736491fd3476a444b3203","observation_id":"708c5db8-b9a9-4c31-8b02-4d88848d1fe1","resolution":{"observed_at":"2026-05-17T03:40:25.940982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04519","last_updated":"2025-01-08T14:12:57Z","snapshot_observed_at":"2026-07-06T20:18:12.456972Z","submitted_at":"2025-01-08T14:12:57Z","title":"rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking","version":1},"cited_work":{"arxiv_id":"2501.04519","doi":"10.48550/arxiv.2501.04519","metadata_source":"pith","pith_arxiv_id":"2501.04519","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking","venue":"cs.CL","work_id":"49792b83-569e-4f5f-ae80-e96cbd3b7a43","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2501.04519","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:7d91ca87d7ebeddd7ea1b300fb60c120f0785b7b25ed2cc17cc0a716d5a303d7","observation_id":"14c552b0-c3f1-4f16-aa6b-e44d33d2395b","resolution":{"observed_at":"2026-05-17T04:43:48.343981Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-23T21:53:07.471573+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T21:53:07.471573+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11644","last_updated":"2023-10-02T06:12:30Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-20T16:14:25Z","title":"Textbooks Are All You Need","version":2},"cited_work":{"arxiv_id":"2306.11644","doi":"10.48550/arxiv.2306.11644","metadata_source":"pith","pith_arxiv_id":"2306.11644","snapshot_observed_at":"2026-07-10T12:07:03.438550Z","title":"Textbooks Are All You Need","venue":"cs.CL","work_id":"9b14eca2-9e41-4755-88ac-c3e7b67253f5","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2306.11644","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:ba1d63a59b47af7144dfa2e638bfeb2c8dadd2917382330960c42b8ee04cc406","observation_id":"d2038791-e7e6-475e-aa8e-2a79b644e4f3","resolution":{"observed_at":"2026-05-17T03:40:25.760971Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2501.12948","doi":"10.1016/j.artmed.2024.103001","metadata_source":"pith","pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","venue":"cs.CL","work_id":"e6b75ad5-2877-4168-97c8-710407094d20","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:bf88185cc320c18734a9c24a6c65301425f8e8b86938a87cacf59108eeeca092","observation_id":"caaa20f3-36fb-4418-b8d3-d12c69e68d78","resolution":{"observed_at":"2026-05-17T03:40:25.767220Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Computers and intractability: a guide to the theory of np-completeness (michael r","venue":null,"work_id":"6fce8957-b69e-4f80-8e1e-3e4c2c83ba12","year":1982},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:56c0cc6fc45f0ed4b78d38a8a519fb9b1b08c44737fbbc990e16d1d2ab8a1f49","observation_id":"e1d81e83-35bb-4a4b-a9df-4b5d3e4ed44b","resolution":{"observed_at":"2026-05-17T03:40:25.952938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"ToxiGen: A large- scale machine-generated dataset for adversarial and implicit hate speech detection","venue":null,"work_id":"fe691543-68e5-4e11-89c6-ada649fa39b0","year":2022},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:671762a98c540d960cc356e48d9189d14499290ea97b3b567e2782c226c899bd","observation_id":"4af62f25-e51d-447f-b38d-9b1c5f2f522a","resolution":{"observed_at":"2026-05-17T03:40:25.955477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":"2009.03300","doi":"10.48550/arxiv.2009.03300","metadata_source":"pith","pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-07-11T01:37:42.666395Z","title":"Measuring Massive Multitask Language Understanding","venue":"cs.CY","work_id":"e87ec49a-544b-4ec8-8991-75298c64ff5e","year":2020},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:b5fd247f1c2c9d95c1c159346015fd2ecf2c9fb3b25c767c48ba35c84f09dd1d","observation_id":"0c7d2f1c-30bd-4bd3-aa73-70f7fecdc5c2","resolution":{"observed_at":"2026-05-17T03:40:25.773304Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T01:08:06.256034+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-04T01:08:06.256034+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.07086","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T11:16:11.346092Z","title":"A sober look at progress in language model reasoning: Pitfalls and paths to repro- ducibility.arXiv preprint arXiv:2504.07086","venue":null,"work_id":"293ecb4d-96d1-4ced-a46c-a3aa4e23d934","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:68e43ea33f293339940cfadc2149457744e9d525ec8ff4ae46826a0b088bfa1b","observation_id":"b3cdae9f-de2f-470d-a3ca-7c7a8245630f","resolution":{"observed_at":"2026-05-17T03:40:25.778105Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":"2410.21276","doi":"10.1177/15248380231178756","metadata_source":"pith","pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"GPT-4o System Card","venue":"cs.CL","work_id":"f37bf1c7-4964-4e56-9762-d20da8d9009f","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:150d5b16854646d541bcb96a715244928f3b9351bca5d5ed4dae1b49cacc4e10","observation_id":"97fc3336-691c-4ecb-9bfe-058c13bf4aeb","resolution":{"observed_at":"2026-05-17T03:40:25.782438Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16720","last_updated":"2026-04-30T02:46:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-21T18:04:31Z","title":"OpenAI o1 System Card","version":2},"cited_work":{"arxiv_id":"2412.16720","doi":"10.48550/arxiv.2412.16720","metadata_source":"pith","pith_arxiv_id":"2412.16720","snapshot_observed_at":"2026-07-11T03:17:51.754565Z","title":"OpenAI o1 System Card","venue":"cs.AI","work_id":"68d3c334-0fc9-49e3-b7b0-a69afae933e2","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2412.16720","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:da1f9d6bc1a9917d0b654db1a03766c9c5840f5acee59e45cd6046b69ced9cd6","observation_id":"879aa7e9-f46d-4dd6-891b-6f41031ca8e6","resolution":{"observed_at":"2026-05-17T03:40:25.786192Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Phi-2: The surprising power of small language models.Microsoft Research Blog","venue":null,"work_id":"5bc116b0-e54b-40dd-81ef-85e591328fc3","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:4a0a2bfb880f1cee5839c53f3efc351c9c41897f68fb3c1d76722f827c925467","observation_id":"77fb31cf-ce3f-4c8f-a2c5-06955ff09624","resolution":{"observed_at":"2026-05-17T03:40:25.969847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12025","last_updated":"2025-02-17T16:57:56Z","snapshot_observed_at":"2026-08-03T19:45:16.676191Z","submitted_at":"2025-02-17T16:57:56Z","title":"SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities","version":1},"cited_work":{"arxiv_id":"2502.12025","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.12025","snapshot_observed_at":"2026-07-01T08:55:35.552681Z","title":"Safechain: Safety of language models with long chain-of-thought reasoning capabilities","venue":null,"work_id":"99fb5b54-7b9b-4c81-8804-f8f01386b344","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2502.12025","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:00b53ed8cae73e7c8993e62678f653e0659e2fbf65996c2d132924d452dd5072","observation_id":"23a33d8e-2625-4bff-9419-3d0866e4a02e","resolution":{"observed_at":"2026-05-17T03:40:25.790569Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Same task, more tokens: the impact of input length on the reasoning performance of large language models","venue":null,"work_id":"1caf2a2c-ccbe-4336-8008-0d83e06b8ce4","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:dd768a914951fb5e1012d3b860b34bd068a1d7d2db3d6d250e5fc5dfb221473b","observation_id":"650992ac-952a-4c66-bf10-937a05982ffe","resolution":{"observed_at":"2026-05-17T03:40:25.905646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2503.12524","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exaone deep: Reasoning enhanced language models","venue":null,"work_id":"b82363c4-1f52-4360-934c-f462f7414b16","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:d0d039199a5200a8cd6e91b5768f7a5b6dd6b4e90d26d9d15b932a20f6f13ca6","observation_id":"1ae656e7-6a29-4e3b-9f02-e37a610bfacb","resolution":{"observed_at":"2026-05-17T03:40:25.794268Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04418","last_updated":"2024-03-03T02:13:27Z","snapshot_observed_at":"2026-07-06T16:28:55.209047Z","submitted_at":"2023-10-06T17:59:11Z","title":"Functional Interpolation for Relative Positions Improves Long Context Transformers","version":2},"cited_work":{"arxiv_id":"2310.04418","doi":"10.48550/arxiv.2310.04418","metadata_source":"arxiv_reference","pith_arxiv_id":"2310.04418","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2310.04418 , year=","venue":null,"work_id":"3825350f-09e2-4dbf-84fa-ce43e2edb89a","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2310.04418","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:492f4f7a4478cc80ce8de1dd84a5db128ed59b27d3ebac5e535a1de6053d571c","observation_id":"06acca0a-4a6b-4ef4-adf2-8e2a0bccc721","resolution":{"observed_at":"2026-05-17T03:40:25.798098Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11939","last_updated":"2024-10-14T18:11:58Z","snapshot_observed_at":"2026-08-02T11:33:41.297984Z","submitted_at":"2024-06-17T17:26:10Z","title":"From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline","version":2},"cited_work":{"arxiv_id":"2406.11939","doi":"10.48550/arxiv.2406.11939","metadata_source":"pith","pith_arxiv_id":"2406.11939","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline","venue":"cs.LG","work_id":"ad4ca175-a846-44ce-add1-5fd69a8d5c41","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2406.11939","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:5df0859a8187b83c2e2ed1887a24594b61c330b26dc8017990860cf65dcbb368","observation_id":"09202a5b-c0cc-48c2-9a0a-a4cd1f7c70c5","resolution":{"observed_at":"2026-05-17T03:40:25.802248Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-20T23:23:28.73804+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T23:23:28.73804+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Limr: Less is more for rl scaling","venue":null,"work_id":"52818279-a024-4d9c-998e-883f1aa5303a","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:56d5e3dd7ec869e6d9bd5ab67723f7ba156da1ffa690e22ddaeaad641b9ed2a0","observation_id":"22d88ba6-9fb2-4916-9253-2b9225e41f06","resolution":{"observed_at":"2026-05-17T03:40:25.926961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation","venue":null,"work_id":"dff850da-2281-450f-a106-405f573c2c3b","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:0ce9e6ef170de332baaf79c15cf78107cb99519ef38282d838edf175c511d354","observation_id":"067e08a1-e275-43f4-b043-771bcfa4cf1f","resolution":{"observed_at":"2026-05-17T03:40:25.929212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica","venue":null,"work_id":"eccb4b5e-8141-4c99-a270-9e38a2eada1b","year":null},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:c1b15e3b5cae34b64e5315c5d639011b62ae9d52d928c3f611847545968ddf58","observation_id":"caa1ec0a-9bce-413f-a700-0b243edd05ba","resolution":{"observed_at":"2026-05-17T03:40:25.934122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"A framework for automated measurement of responsible ai harms in generative ai applications","venue":null,"work_id":"b30ce5fd-faef-46b2-99f7-c52efd0fbfe5","year":null},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:07c28b00a301b2ca01f9297adb84b898ee32b5c778c74e5cbdc9348c5356c32a","observation_id":"8b4f0a72-814c-4c09-bcf3-d5361d44a8f9","resolution":{"observed_at":"2026-05-17T03:40:25.943637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17750","last_updated":"2023-10-26T19:45:06Z","snapshot_observed_at":"2026-08-02T18:17:53.271220Z","submitted_at":"2023-10-26T19:45:06Z","title":"A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications","version":1},"cited_work":{"arxiv_id":"2310.17750","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.17750","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3d1f4499-928f-4733-926b-2a7cd91c0b7f","year":null},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2310.17750","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:148cb8eda150773931a8de37935fcd8e0cb6a2eb534786c43a1978abff071d02","observation_id":"05cb8000-2abd-43a7-a6f0-9ed37764ac97","resolution":{"observed_at":"2026-05-17T03:40:25.805809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11045","last_updated":"2023-11-21T19:43:31Z","snapshot_observed_at":"2026-07-06T16:49:25.400974Z","submitted_at":"2023-11-18T11:44:52Z","title":"Orca 2: Teaching Small Language Models How to Reason","version":2},"cited_work":{"arxiv_id":"2311.11045","doi":"10.48550/arxiv.2311.11045","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11045","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"D., Mahajan, S., Codas, A., Simoes, C., Agrawal, S., Chen, X., Razdaibiedina, A., Jones, E., Aggar- wal, K., Palangi, H., Zheng, G., Rosset, C., Khanpour, H., and Awadallah, A","venue":null,"work_id":"43b4fe4e-8b4f-4766-b66c-19835b73d4e2","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2311.11045","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:e7d5ccec76d5dd09b8f337b00059378d5b1917e1181a3645e1512239437e0b92","observation_id":"bcab39d6-4e6a-41b1-830e-d5d23cf06dd6","resolution":{"observed_at":"2026-05-17T03:40:25.809710Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03502","last_updated":"2024-07-03T21:01:12Z","snapshot_observed_at":"2026-07-06T18:41:13.986887Z","submitted_at":"2024-07-03T21:01:12Z","title":"AgentInstruct: Toward Generative Teaching with Agentic Flows","version":1},"cited_work":{"arxiv_id":"2407.03502","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.03502","snapshot_observed_at":"2026-07-04T20:40:08.264332Z","title":"arXiv preprint arXiv:2407.03502 , year=","venue":null,"work_id":"63e6eb8b-90d9-4a08-a3b6-c71b0c9d40df","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2407.03502","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:f3d9930cf5d69331fbdd328c1df6fc7705e0d138c4dda066d1afd50f9ccaf7d2","observation_id":"48bba3f0-f6db-4db6-a44c-0abcb5d01cab","resolution":{"observed_at":"2026-05-17T03:40:25.813945Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Unearthing skill-level insights for understanding trade-offs of foundation models","venue":null,"work_id":"d26d6e67-f342-448a-b6a8-a400988c1b55","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:70c8ee0e1259757a7fe10e7558c59d32f5f97ef906ba8047e2860afb17457b75","observation_id":"62b830a3-b5e2-40bc-92a7-693df01b60cb","resolution":{"observed_at":"2026-05-17T03:40:25.960938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02707","last_updated":"2023-06-05T08:58:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-05T08:58:39Z","title":"Orca: Progressive Learning from Complex Explanation Traces of GPT-4","version":1},"cited_work":{"arxiv_id":"2306.02707","doi":"10.48550/arxiv.2306.02707","metadata_source":"pith","pith_arxiv_id":"2306.02707","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Orca: Progressive Learning from Complex Explanation Traces of GPT-4","venue":"cs.CL","work_id":"3e29b6b5-4ea8-4e8a-8fa8-6e63093e31c1","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2306.02707","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:cc291a0662b615cc9cebabfcdb8f12f6c8e1c6117f868ecd731f9e01f9f3d7fb","observation_id":"c3ce1455-2c71-4e32-9e01-7a65a732e463","resolution":{"observed_at":"2026-05-17T03:40:25.817493Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Towards accountable ai: Hybrid human-machine analyses for character- izing system failure","venue":null,"work_id":"458b00e5-63c4-456e-94b5-f12c8a5c2186","year":2018},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:d8ab8018aaf54a2f4b42d7dba4a4000ec266df7ef1686e64c966b38d654079b2","observation_id":"b9e2937a-24b1-4aaf-870d-206decf3c76a","resolution":{"observed_at":"2026-05-17T03:40:25.967169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Openai o3-mini system card","venue":null,"work_id":"09bed516-a8a9-45be-8ddb-0b8814e27f88","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:53142c9885022e0a5a43eb79c19b0edb86595c8c0ba5e7da44f59915df5f2235","observation_id":"3bb89c3a-e54e-48bf-ac37-2afb70abcbc3","resolution":{"observed_at":"2026-05-17T03:40:25.972146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Computational complexity","venue":null,"work_id":"5dc9a55f-ab7b-4de4-8c9f-ad73894a0fe5","year":2003},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:6b3548408236f7a4f56cc4b461aa1f7d7d7ddad8339a2f926aa4f2024c14325f","observation_id":"eda276f5-855e-4d1e-abfc-7d7e3999e968","resolution":{"observed_at":"2026-05-17T03:40:25.912239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Overreliance on ai literature review.Microsoft Research, 339:340","venue":null,"work_id":"0dd74aed-bab9-492e-a206-5358568c6822","year":2022},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:6abf176ec1bd4b45a8bcb9e0fd9f8e0c8dc959fe88503b529fc2005921c1b038","observation_id":"2935f4e6-a434-4fec-a593-134d636562c4","resolution":{"observed_at":"2026-05-17T03:40:25.915243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21934","last_updated":"2025-09-04T19:36:32Z","snapshot_observed_at":"2026-08-04T06:24:12.017370Z","submitted_at":"2025-03-27T19:21:05Z","title":"Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad","version":5},"cited_work":{"arxiv_id":"2503.21934","doi":"10.48550/arxiv.2503.21934","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.21934","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Purvis, B., Mao, Y., and Robinson, D","venue":null,"work_id":"3b6d416a-64c2-4ae2-8ac0-05d906a83bba","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2503.21934","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:39d19e0a29790004d7683a4ec1ebb7a907f67fb061f3af8ab3e29b98a96d5395","observation_id":"2ef8e4b6-d6f6-4472-a70a-31803eb8d840","resolution":{"observed_at":"2026-05-17T03:40:25.739163Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-07-09T16:16:20.660571Z","title":"Gpqa: A graduate-level google-proof q&a benchmark","venue":null,"work_id":"d8f784ac-8ee6-457b-b745-0686e653d8d5","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:65e05c04c3302969c2618ff51f310acfb5847a2b2243c806e7e00a6c9211c597","observation_id":"c6d2135e-2de7-4c80-98ca-708e03964762","resolution":{"observed_at":"2026-05-17T03:40:25.947365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2402.03300","doi":"10.1016/0004-3702(73)90011-8","metadata_source":"pith","pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","venue":"cs.CL","work_id":"c5006563-f3ec-438a-9e35-b7b484f34828","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:31faff477d55799d3449ebe8c1f75f0d3e877c92d074effcba8af1e88c0ac79b","observation_id":"07f4864d-994d-4a8c-bde5-534c44c89efe","resolution":{"observed_at":"2026-05-17T03:40:25.825422Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2409.19256","doi":"10.1145/3689031.3696075.url:","metadata_source":"pith","pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","venue":"cs.LG","work_id":"7eb9c9f4-b322-4bba-8011-09ff8d6ad801","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:b3ffda75dafec22f179c40879b394a3a49eeaa06e0099d58b514a632bdd7e5ea","observation_id":"dae5687a-7da1-497f-b6c0-301c4720db1e","resolution":{"observed_at":"2026-05-17T03:40:25.829028Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03057","last_updated":"2022-10-06T17:03:34Z","snapshot_observed_at":"2026-07-06T14:00:37.875054Z","submitted_at":"2022-10-06T17:03:34Z","title":"Language Models are Multilingual Chain-of-Thought Reasoners","version":1},"cited_work":{"arxiv_id":"2210.03057","doi":"10.48550/arxiv.2210.03057","metadata_source":"pith","pith_arxiv_id":"2210.03057","snapshot_observed_at":"2026-07-11T01:37:42.691693Z","title":"Language Models are Multilingual Chain-of-Thought Reasoners","venue":"cs.CL","work_id":"1534249c-ff7a-417c-9fb7-7505743d1c20","year":2022},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2210.03057","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:ed7f820a4f98d9de904f0372db9c4dc39859f7040af8bec7b8de8af02c914a52","observation_id":"d765b8ae-949d-4062-a282-f98a624ff0c2","resolution":{"observed_at":"2026-05-17T03:40:25.832469Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.09864","last_updated":"2023-11-08T13:36:32Z","snapshot_observed_at":"2026-07-06T11:01:58.137141Z","submitted_at":"2021-04-20T09:54:06Z","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","version":5},"cited_work":{"arxiv_id":"2104.09864","doi":"10.48550/arxiv.2104.09864","metadata_source":"pith","pith_arxiv_id":"2104.09864","snapshot_observed_at":"2026-07-11T01:27:45.562618Z","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","venue":"cs.CL","work_id":"4e5eee26-cd04-4c7a-988f-3e6d1a1f0eb9","year":2021},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2104.09864","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:fec2cb2fae5f6f5782a98ff2efe5ed9644c1c3edce6eec7ebc7df34b1370a011","observation_id":"702cef1a-b9e6-492f-8b63-0a6cea967aed","resolution":{"observed_at":"2026-05-17T03:40:25.835676Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-11T01:49:47.452101+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T01:49:47.452101+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12599","last_updated":"2025-06-03T02:14:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T02:48:14Z","title":"Kimi k1.5: Scaling Reinforcement Learning with LLMs","version":4},"cited_work":{"arxiv_id":"2501.12599","doi":"10.48550/arxiv.2501.12599","metadata_source":"pith","pith_arxiv_id":"2501.12599","snapshot_observed_at":"2026-07-10T22:47:36.964713Z","title":"Kimi k1.5: Scaling Reinforcement Learning with LLMs","venue":"cs.AI","work_id":"bff96ab1-bd6a-4585-be23-74fdb51969c7","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2501.12599","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:5ebe0189ae5e20d825b25aeb940501fb96ef0d31e788e7a38abe77745e6d1f45","observation_id":"7d51f35e-89ac-4b36-ade0-9ed494a3c128","resolution":{"observed_at":"2026-05-17T03:40:25.840520Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-07-09T10:48:38.585868+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T10:48:38.585868+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Open Thoughts","venue":null,"work_id":"a626fd75-501b-425c-b69f-0878f5bcb84d","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:b8475178c3ef6b5c6ccca97deda524f942c580323ff9411bfa3968cbe5d366fa","observation_id":"af2cc9d9-95e0-46d5-98f6-78193ca15954","resolution":{"observed_at":"2026-05-17T03:40:25.957980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Qwq-32b: Embracing the power of reinforcement learning, March 2025","venue":null,"work_id":"7a104d1a-8e14-49da-8298-d9302fdc10f8","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:a7e7204f296c0dd61e37c340e10f446cd1b6d08d2647e269c63b43f3487fd9df","observation_id":"2b4ff737-13ae-4ac6-a000-624913b1a9c2","resolution":{"observed_at":"2026-05-17T03:40:25.964635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.04388","last_updated":"2023-12-09T21:25:02Z","snapshot_observed_at":"2026-08-02T07:12:38.105035Z","submitted_at":"2023-05-07T22:44:25Z","title":"Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting","version":2},"cited_work":{"arxiv_id":"2305.04388","doi":"10.1109/cdics61497.2023.00014","metadata_source":"pith","pith_arxiv_id":"2305.04388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting","venue":"cs.CL","work_id":"6ed38946-7275-41a4-91b9-b9f7fa043250","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2305.04388","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:928f355fca465c45f332b70fa2eb20d2b31f87da423a714c03acbb2e5d9e296f","observation_id":"41c0b3e2-e67e-4c59-800d-c2526b0c2d1d","resolution":{"observed_at":"2026-05-17T03:40:25.843969Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Is a picture worth a thousand words? delving into spatial reasoning for vision language models.Advances in Neural Information Processing Systems, 37:75392–75421","venue":null,"work_id":"fe204c20-7a55-416a-8439-21ca72a36121","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:6e3277066c22e6e0f37086c5d049fc15c99638bc06e87ee50179caaf5317de2a","observation_id":"ed7a1c04-96b3-4eb0-a788-2bce9ef46613","resolution":{"observed_at":"2026-05-17T03:40:25.950192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01574","last_updated":"2024-11-06T02:54:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-03T17:53:00Z","title":"MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark","version":6},"cited_work":{"arxiv_id":"2406.01574","doi":"10.1145/3711896.3737413","metadata_source":"pith","pith_arxiv_id":"2406.01574","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark","venue":"cs.CL","work_id":"3c028052-035a-4c22-b80e-3046edb44adc","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2406.01574","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:28fcaaa8997654decb7fe99831bfa3ee00d1f554d82d67a2239845158ae58849","observation_id":"21a94ca9-336c-4565-b90e-f089ea37fe87","resolution":{"observed_at":"2026-05-17T03:40:25.847444Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2412.15115","doi":"10.1145/3581783.3612503","metadata_source":"pith","pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen2.5 Technical Report","venue":"cs.CL","work_id":"d8432992-4980-4a81-85c7-9fa2c2b87f85","year":2024},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:921bc5d163abe03e95aa8cde9ca1b0b85e592cb9b2edea8f2acdd1b05ae8a779","observation_id":"8307ea11-0e5e-435d-a947-f20e74869e07","resolution":{"observed_at":"2026-05-17T03:40:25.850758Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06773","last_updated":"2025-02-10T18:52:04Z","snapshot_observed_at":"2026-08-04T15:56:29.014251Z","submitted_at":"2025-02-10T18:52:04Z","title":"On the Emergence of Thinking in LLMs I: Searching for the Right Intuition","version":1},"cited_work":{"arxiv_id":"2502.06773","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.06773","snapshot_observed_at":"2026-07-03T05:57:41.592060Z","title":"On the emergence of thinking in llms i: Searching for the right intuition","venue":null,"work_id":"79ab0859-4c94-4e77-8733-792f09188125","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2502.06773","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:da17ebdd5c3e79dad8600cd340fa905c1038596aef821f892ccf609350987799","observation_id":"9c08c4c8-1eff-47cd-8605-016f003dce04","resolution":{"observed_at":"2026-05-17T03:40:25.854460Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03373","last_updated":"2025-02-05T17:13:32Z","snapshot_observed_at":"2026-07-06T20:31:41.231839Z","submitted_at":"2025-02-05T17:13:32Z","title":"Demystifying Long Chain-of-Thought Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":"2502.03373","doi":"10.48550/arxiv.2502.03373","metadata_source":"pith","pith_arxiv_id":"2502.03373","snapshot_observed_at":"2026-07-10T22:47:37.120136Z","title":"Demystifying Long Chain-of-Thought Reasoning in LLMs","venue":"cs.CL","work_id":"6d35a498-cdd6-463a-a0d9-5a29224a06d8","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2502.03373","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:7671e0bd470a95de982e8bb443f2522665b2fb7f12880e81ed6a288f3a8d0187","observation_id":"e28cb773-4c87-4c67-a3e1-afcfa5363cf0","resolution":{"observed_at":"2026-05-19T01:29:59.463518Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"2026-06-05T21:23:00.469572Z","title":"Dapo: An open-source llm reinforcement learning system at scale","venue":null,"work_id":"aae7ba35-8669-45db-b610-c4160ee87ad5","year":2025},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:9411391e4f8c5355220f8d59203ab4a79c162960252f3254d240af8810f8eb76","observation_id":"e6fd1974-f1f3-4512-9ee8-bb2b60627811","resolution":{"observed_at":"2026-05-17T03:40:25.918153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07911","last_updated":"2023-11-14T05:13:55Z","snapshot_observed_at":"2026-07-06T16:47:08.877195Z","submitted_at":"2023-11-14T05:13:55Z","title":"Instruction-Following Evaluation for Large Language Models","version":1},"cited_work":{"arxiv_id":"2311.07911","doi":"10.48550/arxiv.2311.07911","metadata_source":"pith","pith_arxiv_id":"2311.07911","snapshot_observed_at":"2026-07-10T12:07:03.672931Z","title":"Instruction-Following Evaluation for Large Language Models","venue":"cs.CL","work_id":"3aa06177-125a-4f5a-8f4a-8070c5986c26","year":2023},"citing_paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-17T03:40:25.706499Z"},"links":{"cited_paper":"/paper/2311.07911","citing_paper":"/paper/2504.21318"},"observation_digest":"sha256:b8cd40be720e986e8051da69ab160e689216ae28779fac1ddb8ab139ae6413ee","observation_id":"06a25481-bea5-48ac-a61f-9144bad29121","resolution":{"observed_at":"2026-05-17T03:40:25.862410Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.21318","last_updated":"2025-04-30T05:05:09Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T05:05:09Z","title":"Phi-4-reasoning Technical Report"},"reference_resolution":{"displayed":64,"state_counts":{"malformed_identifier":0,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":0,"verified_exact":31,"verified_fuzzy":28},"total_outbound_references":64},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 48 inbound Pith citation observations for arXiv:2504.21318."}