{"as_of":"2026-08-19T00:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5ebbd32289009a1c4005d0c2af8b4b493cc569d9d311a5ced350e7e35210eb8e","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:07:19.460963Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T07:50:50.670748Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T21:43:59.839553Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"cited_work":{"arxiv_id":"2506.11603","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.11603","snapshot_observed_at":"2026-06-29T21:43:59.839553Z","title":"Tongsearch-qr: Reinforced query reasoning for retrieval","venue":null,"work_id":"099256f8-e1dc-4ef8-9add-37e3604e309d","year":2025},"citing_paper":{"arxiv_id":"2511.11653","last_updated":"2026-04-30T09:50:26Z","snapshot_observed_at":"2026-08-14T02:03:17.010825Z","submitted_at":"2025-11-10T15:25:31Z","title":"GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-17T23:41:47.052697Z"},"links":{"cited_paper":"/paper/2506.11603","citing_paper":"/paper/2511.11653"},"observation_digest":"sha256:421695aa49f2ec81278b31fceddecbeca9809cc9ee3d2c1606aa001d434ee928","observation_id":"3b6a966c-143a-4524-af00-98b06bf21132","resolution":{"observed_at":"2026-05-17T23:42:13.013461Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.11603","snapshot_observed_at":"2026-08-02T23:59:26.636040Z","title":"Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, and Michael Bendersky","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.12192","last_updated":"2026-05-29T05:59:56Z","snapshot_observed_at":"2026-08-14T05:36:53.389611Z","submitted_at":"2026-02-12T17:23:38Z","title":"Query-focused and Memory-aware Reranker for Long Context Processing","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-02T23:59:26.636040Z"},"links":{"cited_paper":"/paper/2506.11603","citing_paper":"/paper/2602.12192"},"observation_digest":"sha256:0b00695204ee7948ffd5ffd302d003a853ae0fb03e36ed9bd161cf431e1b0d81","observation_id":"782a1bae-9acd-48dc-b1d4-f20b0b69877f","resolution":{"observed_at":"2026-08-02T23:59:26.636040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"cited_work":{"arxiv_id":"2506.11603","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.11603","snapshot_observed_at":"2026-06-29T21:43:59.839553Z","title":"Tongsearch-qr: Reinforced query reasoning for retrieval","venue":null,"work_id":"099256f8-e1dc-4ef8-9add-37e3604e309d","year":2025},"citing_paper":{"arxiv_id":"2605.26352","last_updated":"2026-08-06T01:35:38Z","snapshot_observed_at":"2026-08-09T23:09:45.111534Z","submitted_at":"2026-05-25T21:56:29Z","title":"RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T21:24:11.882268Z"},"links":{"cited_paper":"/paper/2506.11603","citing_paper":"/paper/2605.26352"},"observation_digest":"sha256:17046aebc29753f87830bda1f2b37af82dc57ee5cc83a3e957b058dbb0bfbfc4","observation_id":"1c1edb33-4d2b-4dd4-b9b3-9f5218a06bf2","resolution":{"observed_at":"2026-06-29T21:43:59.841216Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.11603","snapshot_observed_at":"2026-08-04T05:01:25.580743Z","title":"Tongsearch-qr: Reinforced query reasoning for retrieval.arXiv preprint arXiv:2506.11603, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.26352","last_updated":"2026-08-06T01:35:38Z","snapshot_observed_at":"2026-08-09T23:09:45.111534Z","submitted_at":"2026-05-25T21:56:29Z","title":"RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T05:01:25.580743Z"},"links":{"cited_paper":"/paper/2506.11603","citing_paper":"/paper/2605.26352"},"observation_digest":"sha256:5d60c9ffa87ef42db16e707419dc581956c9b3f02b258c4e9f00a6328e72a442","observation_id":"fd2ecae7-f5e9-4037-b165-905b42545647","resolution":{"observed_at":"2026-08-04T05:01:25.580743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"cited_work":{"arxiv_id":"2506.11603","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.11603","snapshot_observed_at":"2026-06-29T21:43:59.839553Z","title":"Tongsearch-qr: Reinforced query reasoning for retrieval","venue":null,"work_id":"099256f8-e1dc-4ef8-9add-37e3604e309d","year":2025},"citing_paper":{"arxiv_id":"2605.29507","last_updated":"2026-05-28T07:29:58Z","snapshot_observed_at":"2026-08-11T18:16:32.874786Z","submitted_at":"2026-05-28T07:29:58Z","title":"Xetrieval: Mechanistically Explaining Dense Retrieval","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T07:18:17.049765Z"},"links":{"cited_paper":"/paper/2506.11603","citing_paper":"/paper/2605.29507"},"observation_digest":"sha256:99468003395d03e2e0d9b3e6115509afef9c0d6bf94c1a135ef3962c2214fb06","observation_id":"b9853363-d90e-4ae3-8e33-596a8d9d94b5","resolution":{"observed_at":"2026-06-29T07:23:12.874814Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.11603","snapshot_observed_at":"2026-08-04T07:50:50.670748Z","title":"Tongsearch-qr: Reinforced query reasoning for retrieval, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02407","last_updated":"2026-08-03T15:49:14Z","snapshot_observed_at":"2026-08-16T21:18:38.451065Z","submitted_at":"2026-08-03T15:49:14Z","title":"Antares: Foundation Models for Agentic Vulnerability Localization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T07:50:50.670748Z"},"links":{"cited_paper":"/paper/2506.11603","citing_paper":"/paper/2608.02407"},"observation_digest":"sha256:1797b77c2cfdef9496ffb5c6d6e3fb4b6d652b6ff9263fc57ed6ca788928535b","observation_id":"7a94227a-af5c-4658-ba7a-75789f84180d","resolution":{"observed_at":"2026-08-04T07:50:50.670748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.11603/citation-record","integrity":"/paper/2506.11603/integrity","json":"/paper/2506.11603/citation-record.json","paper":"/paper/2506.11603"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.313070Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.313070Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:769ceaa46bd803ed0605604601cc61f2aaad5be64b9a8a3f9d6bec147489e710","observation_id":"9146e218-6c95-4e9d-9c61-075292f6c394","resolution":{"observed_at":"2026-08-07T04:07:19.313070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.09268","last_updated":"2018-10-31T14:46:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-11-28T18:14:11Z","title":"MS MARCO: A Human Generated MAchine Reading COmprehension Dataset","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.09268","snapshot_observed_at":"2026-08-07T04:07:19.318101Z","title":"Ms marco: A human generated machine reading comprehension dataset, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.318101Z"},"links":{"cited_paper":"/paper/1611.09268","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:cbcc2a473dc14017ed6d420156798b3dd69f08b3e2a131d85449e9f818eef788","observation_id":"c6444e3f-7cb9-4a39-8d5e-4973530b84b3","resolution":{"observed_at":"2026-08-07T04:07:19.318101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03216","last_updated":"2025-12-12T11:26:32Z","snapshot_observed_at":"2026-08-17T15:25:09.479397Z","submitted_at":"2024-02-05T17:26:49Z","title":"M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03216","snapshot_observed_at":"2026-08-07T04:07:19.321987Z","title":"BGE M3 - Embedding : Multi - Lingual , Multi - Functionality , Multi - Granularity Text Embeddings Through Self - Knowledge Distillation , June 2024 a","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.321987Z"},"links":{"cited_paper":"/paper/2402.03216","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:afaa47c8598af02fa6e9e33b0d4b7b1c6c21f314b3fa3cdb5d6c2573ae9bcc38","observation_id":"fecff692-9428-4972-ad1c-a95875a7a00a","resolution":{"observed_at":"2026-08-07T04:07:19.321987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02419","last_updated":"2024-06-04T21:20:33Z","snapshot_observed_at":"2026-08-16T14:12:54.897914Z","submitted_at":"2024-03-04T19:12:48Z","title":"Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02419","snapshot_observed_at":"2026-08-07T04:07:19.326608Z","title":"Q., Hanin, B., Bailis, P., Stoica, I., Zaharia, M., and Zou, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.326608Z"},"links":{"cited_paper":"/paper/2403.02419","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:67884677d3d9a8c90426276d522a1bbbaebd0c2d8c2c16f7794c240f45aff0b6","observation_id":"9060bdc8-54f9-4429-a4e3-00844932423b","resolution":{"observed_at":"2026-08-07T04:07:19.326608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8246.13482","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:20.263231Z","title":null,"venue":null,"work_id":"5ae9646c-49c6-4ab5-9096-ae97f8f66e9c","year":2008},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.330316Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:e20458a2defd5315c19e8ba7a28d095ed16886e0679962a6a0c9176cb755966e","observation_id":"18926880-691e-4559-ae96-697cebd3a98b","resolution":{"observed_at":"2026-08-07T04:07:20.269165Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-08-18T18:18:37.449517Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T04:07:19.334333Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.334333Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:d6971a8410843b4fee54874a74b506b34c518807880f709160bfe2c08438a091","observation_id":"95084e2c-7343-4c98-bd64-b88e6cdf4125","resolution":{"observed_at":"2026-08-07T04:07:19.334333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T04:07:19.338230Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.338230Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:24b32964bf23c4d775e07636dd2b9e9f574803abe61b0d7146707e210dd18610","observation_id":"d39d295f-c7cd-4a3e-ac50-334911e73ce4","resolution":{"observed_at":"2026-08-07T04:07:19.338230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.343772Z","title":"Open r1: A fully open reproduction of deepseek-r1, January 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.343772Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:f970fb4dce4d0585bbddbecaec2d71fb56fcd40c7625b854f239166f26d2c40e","observation_id":"147367ef-4df4-4719-b053-5888f395ed19","resolution":{"observed_at":"2026-08-07T04:07:19.343772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10496","last_updated":"2022-12-20T18:09:52Z","snapshot_observed_at":"2026-08-18T06:38:59.553305Z","submitted_at":"2022-12-20T18:09:52Z","title":"Precise Zero-Shot Dense Retrieval without Relevance Labels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10496","snapshot_observed_at":"2026-08-07T04:07:19.347137Z","title":"Precise zero-shot dense retrieval without relevance labels","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.347137Z"},"links":{"cited_paper":"/paper/2212.10496","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:529c8fe6eb000647cb2a7d5069a508a0edd53af1ff25cdcd17453254b6605a1c","observation_id":"951fa1a3-7ee4-4731-afe0-a8ead92f6b5a","resolution":{"observed_at":"2026-08-07T04:07:19.347137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T04:07:19.350918Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.350918Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:b74d7b1e4c81332d0bc0e57c7be79005ed8206d126ca5ca6716321cbecc0afae","observation_id":"bf766d2d-3df2-463e-9f3d-ce434e40d040","resolution":{"observed_at":"2026-08-07T04:07:19.350918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03653","last_updated":"2023-05-05T16:16:45Z","snapshot_observed_at":"2026-08-18T09:57:42.966695Z","submitted_at":"2023-05-05T16:16:45Z","title":"Query Expansion by Prompting Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03653","snapshot_observed_at":"2026-08-07T04:07:19.355526Z","title":"Query Expansion by Prompting Large Language Models , May 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.355526Z"},"links":{"cited_paper":"/paper/2305.03653","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:94f04e7c020259403394410de34fb074f15ec69444535582b9a74fee6ff23d6b","observation_id":"c33d0261-c2f9-46ce-8965-aaf4016bf124","resolution":{"observed_at":"2026-08-07T04:07:19.355526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","submitted_at":"2025-02-28T22:16:42Z","title":"DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-07T04:07:19.358923Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.358923Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:49f3d35b36bd8e76971d7871f9461a9d57d487b2d93e6591ff77d56224dc7d4b","observation_id":"fbc2cd86-1f29-49da-81de-c5c269e04507","resolution":{"observed_at":"2026-08-07T04:07:19.358923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.04906","last_updated":"2020-09-30T21:27:13Z","snapshot_observed_at":"2026-07-06T09:11:26.109763Z","submitted_at":"2020-04-10T04:53:17Z","title":"Dense Passage Retrieval for Open-Domain Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.04906","snapshot_observed_at":"2026-08-07T04:07:19.362484Z","title":"Dense passage retrieval for open-domain question answering, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.362484Z"},"links":{"cited_paper":"/paper/2004.04906","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:c34e99deee9872c30c13a6f7317d9e00ad71be6ff22a5f929f2a8dda1b6c93c5","observation_id":"43595bb9-c2c1-42f3-885e-d8b6cef0bcfb","resolution":{"observed_at":"2026-08-07T04:07:19.362484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:20.350996Z","title":"and Zaharia, M","venue":null,"work_id":"15bcde0a-66ea-488a-a628-d42e92001bbb","year":2020},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.365855Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:8fcfe171f919e39eb3ab71da9ff8032c524c2ca8e107348facbc4002938f84cc","observation_id":"c306b7d0-935a-4086-a23d-2fdaab2798e6","resolution":{"observed_at":"2026-08-07T04:07:20.354212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.06180","last_updated":"2023-09-12T12:50:04Z","snapshot_observed_at":"2026-08-02T09:51:08.145755Z","submitted_at":"2023-09-12T12:50:04Z","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.06180","snapshot_observed_at":"2026-08-07T04:07:19.369249Z","title":"H., Gonzalez, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.369249Z"},"links":{"cited_paper":"/paper/2309.06180","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:359b4c9346f789d9927b7e4ac397481081b704d0bb973c600d1c5765bc8f88be","observation_id":"afbe6870-d2ae-4e0c-9da4-18ed44a81c6c","resolution":{"observed_at":"2026-08-07T04:07:19.369249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:20.340047Z","title":"Huggingface h4 stack exchange preference dataset, 2023","venue":null,"work_id":"5509fba5-7fe2-4d85-8960-b999e56acfd7","year":2023},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.372797Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:b4ba6a93f54deaf16e0f05d974e2dbca6f09c02dba20ffa24b198b9b6a3ba06a","observation_id":"3faa0c52-2d0b-4842-bb56-9c3b084ab743","resolution":{"observed_at":"2026-08-07T04:07:20.343487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17428","last_updated":"2025-02-25T00:35:18Z","snapshot_observed_at":"2026-08-14T08:11:36.232487Z","submitted_at":"2024-05-27T17:59:45Z","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17428","snapshot_observed_at":"2026-08-07T04:07:19.376258Z","title":"Nv-embed: Improved techniques for training llms as generalist embedding models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.376258Z"},"links":{"cited_paper":"/paper/2405.17428","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:4cf2a943b20f6061f167428815a01b804bac33cd1b2598aa84e91563a3e1f4df","observation_id":"8c7307f3-3b06-4326-8ab1-c2c78b070365","resolution":{"observed_at":"2026-08-07T04:07:19.376258Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03281","last_updated":"2023-08-07T03:52:59Z","snapshot_observed_at":"2026-08-14T21:56:20.223557Z","submitted_at":"2023-08-07T03:52:59Z","title":"Towards General Text Embeddings with Multi-stage Contrastive Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03281","snapshot_observed_at":"2026-08-07T04:07:19.379517Z","title":"Towards general text embeddings with multi-stage contrastive learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.379517Z"},"links":{"cited_paper":"/paper/2308.03281","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:dae9805163cd49f65c0dfb608e0e0a0f5a9da0a080e73733f999b773a0ca8db0","observation_id":"877fda71-c1b3-4d75-a680-61be7b5e4a38","resolution":{"observed_at":"2026-08-07T04:07:19.379517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-08-16T14:33:50.657682Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-07T04:07:19.383164Z","title":"Roberta: A robustly optimized bert pretraining approach","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.383164Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:8a9b4d73df39dc15bf3773868b6f74bb3daeb537432a2dc16d4c34c86bf6b1f1","observation_id":"c1d41705-40cd-4630-b5eb-175a29f7c76f","resolution":{"observed_at":"2026-08-07T04:07:19.383164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03373","last_updated":"2021-10-16T15:12:57Z","snapshot_observed_at":"2026-08-16T18:19:12.641048Z","submitted_at":"2021-06-07T06:55:45Z","title":"Pre-trained Language Model for Web-scale Retrieval in Baidu Search","version":4},"cited_work":{"arxiv_id":"2106.03373","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.03373","snapshot_observed_at":"2026-08-07T04:07:20.077236Z","title":"Pre-trained Language Model for Web-scale Retrieval in Baidu Search","venue":"cs.IR","work_id":"df20dca9-a747-43c4-832a-7735519b4ae7","year":2021},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.386210Z"},"links":{"cited_paper":"/paper/2106.03373","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:e3cc76f7f6db7fc528e2a88382e085a6b17608b2a38ecf0969686821186f1501","observation_id":"f12b50fd-18c4-47ea-b5d2-46b798eeec39","resolution":{"observed_at":"2026-08-07T04:07:20.080729Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12194","last_updated":"2024-08-23T06:46:41Z","snapshot_observed_at":"2026-08-16T13:24:41.084311Z","submitted_at":"2024-08-22T08:16:07Z","title":"Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment","version":2},"cited_work":{"arxiv_id":"2408.12194","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.12194","snapshot_observed_at":"2026-08-07T04:07:20.059499Z","title":"Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment","venue":"cs.CL","work_id":"cc405d62-0a9c-4bb3-ac32-12d00c83ab7f","year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.389536Z"},"links":{"cited_paper":"/paper/2408.12194","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:f98fe7bbcdca0eeaca93ffb98f3c3b33a7053ba9916f80c8d4cbb35cec28c0ed","observation_id":"be74f976-bc67-44dc-b055-3a407859985e","resolution":{"observed_at":"2026-08-07T04:07:20.064856Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.392938Z","title":"Fine- Tuning LLaMA for Multi - Stage Text Retrieval","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.392938Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:195601b977ad84dcce8aa131ff02e6eb5d854c1c171bb5750eb962347a84d1ef","observation_id":"38e3d382-11ec-4609-af71-0ab839616faf","resolution":{"observed_at":"2026-08-07T04:07:19.392938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00142","last_updated":"2024-10-31T18:43:12Z","snapshot_observed_at":"2026-08-16T22:36:09.418618Z","submitted_at":"2024-10-31T18:43:12Z","title":"JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00142","snapshot_observed_at":"2026-08-07T04:07:19.396156Z","title":"Judgerank: Leveraging large language models for reasoning-intensive reranking, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.396156Z"},"links":{"cited_paper":"/paper/2411.00142","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:42a97d277c9dd3c4e90be299549fd72a3be2c537396cde68e6550772b649265c","observation_id":"ee029978-a80e-452e-8c8e-1524b60a1823","resolution":{"observed_at":"2026-08-07T04:07:19.396156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-08-15T14:02:47.366139Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-07T04:07:19.399718Z","title":"Gpt-4o system card, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.399718Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:c36de9004a9fd246067609321628896226f41a52c5a52a8eaddd7461eb857ae8","observation_id":"323d18d8-03a3-473d-9221-026d0abb35e1","resolution":{"observed_at":"2026-08-07T04:07:19.399718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-08-17T18:50:07.059564Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-07T04:07:19.403353Z","title":"Qwen2.5 technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.403353Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:c1edf62af051a14db80aa9bfe6ddc8012dd1b39801e33010b4b93d6c374c2905","observation_id":"3c66603b-0a06-4099-aeaa-029a5569fe03","resolution":{"observed_at":"2026-08-07T04:07:19.403353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.407164Z","title":"Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.407164Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:563d0665699bda7c744371a5e8c48be5e2e615ab908aa58953b0d33b06a4ffb8","observation_id":"79ee80bf-4da4-4952-902a-39b38f07e0fd","resolution":{"observed_at":"2026-08-07T04:07:19.407164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.410563Z","title":"and Zaragoza, H","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.410563Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:fd6f97d12006adfab92acfd255ede0f1857c1620c5b06423c307aaa421d6659b","observation_id":"1115d109-69b9-435f-a386-f12ad9b72813","resolution":{"observed_at":"2026-08-07T04:07:19.410563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20595","last_updated":"2025-04-29T09:49:28Z","snapshot_observed_at":"2026-08-18T13:04:17.234148Z","submitted_at":"2025-04-29T09:49:28Z","title":"ReasonIR: Training Retrievers for Reasoning Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20595","snapshot_observed_at":"2026-08-07T04:07:19.413729Z","title":"V., Rus, D., Low, B","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.413729Z"},"links":{"cited_paper":"/paper/2504.20595","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:8987677d2685731702f143461816fa5eec1c575ae1dbb3a6f05a7e189e2eecf4","observation_id":"55af5b55-7d72-43d4-a5d0-3ff9523a8324","resolution":{"observed_at":"2026-08-07T04:07:19.413729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-07T04:07:19.416983Z","title":"K., Wu, Y., and Guo, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.416983Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:0b623e31ca948a07763b27efab8a387a4cc00971be31ea28c67830384e43aabe","observation_id":"495b1f0b-6941-4802-a5e2-d85602547a93","resolution":{"observed_at":"2026-08-07T04:07:19.416983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.12883","last_updated":"2025-03-26T07:37:26Z","snapshot_observed_at":"2026-08-18T10:29:29.816909Z","submitted_at":"2024-07-16T17:58:27Z","title":"BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.12883","snapshot_observed_at":"2026-08-07T04:07:19.420264Z","title":"S., Tang, M., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.420264Z"},"links":{"cited_paper":"/paper/2407.12883","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:2ebd145c449bbd5c309c11e2acfbb055618c7b08766ef1e09e22425b7f28bd4f","observation_id":"8daa8b37-876a-4c04-a5da-b4ef2651a924","resolution":{"observed_at":"2026-08-07T04:07:19.420264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.423745Z","title":"Lref: A novel llm-based relevance framework for e-commerce search","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.423745Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:0fc8887d1a99088c17e8f04680e87252e4b1629cc8667030f43efbff9e6cfe0c","observation_id":"de1a8573-e761-4a0f-be5c-831311498640","resolution":{"observed_at":"2026-08-07T04:07:19.423745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-07T04:07:19.426953Z","title":"The llama 3 herd of models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.426953Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:0dabd7a90b9f5eeb899242af5507138a8ec23fd3c6c65e554226b60392afdb57","observation_id":"6a52af2a-a9c9-46cb-bfe3-991bb3003025","resolution":{"observed_at":"2026-08-07T04:07:19.426953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.03533","last_updated":"2024-02-22T06:21:51Z","snapshot_observed_at":"2026-07-06T14:27:46.217000Z","submitted_at":"2022-12-07T09:25:54Z","title":"Text Embeddings by Weakly-Supervised Contrastive Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.03533","snapshot_observed_at":"2026-08-07T04:07:19.430665Z","title":"Text embeddings by weakly-supervised contrastive pre-training","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.430665Z"},"links":{"cited_paper":"/paper/2212.03533","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:258dc735eb8eefc4a76aedc611befc2434caaa3201f6848d5d76aa8861445ec8","observation_id":"3d5012e8-6185-4cc0-b07d-394820d27710","resolution":{"observed_at":"2026-08-07T04:07:19.430665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00368","last_updated":"2024-05-31T07:22:01Z","snapshot_observed_at":"2026-08-16T14:30:41.754354Z","submitted_at":"2023-12-31T02:13:18Z","title":"Improving Text Embeddings with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00368","snapshot_observed_at":"2026-08-07T04:07:19.433935Z","title":"Improving text embeddings with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.433935Z"},"links":{"cited_paper":"/paper/2401.00368","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:1fe047882eca660a4e8ada0fd634d8418c29b1eb1e450bc10d55bd7557a3335f","observation_id":"a0227755-5e81-461f-8903-ac5d4e5731df","resolution":{"observed_at":"2026-08-07T04:07:19.433935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:20.329558Z","title":"Uniir: Training and benchmarking universal multimodal information retrievers","venue":null,"work_id":"ddc29d2a-9b4e-4ffa-96af-e4946a775416","year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.437492Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:4f90ffa635a93e99e8377a04d699370f7a95924db950f67550c7e43b2714669c","observation_id":"2d5e5cab-549e-4704-bd34-315a172a1fda","resolution":{"observed_at":"2026-08-07T04:07:20.332955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.440642Z","title":"V., Zhou, D., et al","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.440642Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:1f9baa3d176756b0a2cfae7318f96f2410f3436690c5fca5306ef944f41efee0","observation_id":"496a3448-20da-4d1e-bfc5-8946286ecffa","resolution":{"observed_at":"2026-08-07T04:07:19.440642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18418","last_updated":"2025-08-08T14:48:31Z","snapshot_observed_at":"2026-08-18T06:38:56.309497Z","submitted_at":"2025-02-25T18:14:06Z","title":"Rank1: Test-Time Compute for Reranking in Information Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18418","snapshot_observed_at":"2026-08-07T04:07:19.443757Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.443757Z"},"links":{"cited_paper":"/paper/2502.18418","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:54d28aa8ef578f54d16785e7758704b79d13d8dae430d8ce8e2ecc810fba4b7a","observation_id":"76c7ff2b-7f30-4014-86bd-96075cc57ae5","resolution":{"observed_at":"2026-08-07T04:07:19.443757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:20.311939Z","title":"Inference scaling laws: An empirical analysis of compute-optimal inference for llm problem-solving","venue":null,"work_id":"cd9b4d94-05c0-4779-981f-dacece6c0912","year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.447117Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:a32c048b600ba0f5e33d90d76e6270ff65e866bf1d316612a0ba5debd3931649","observation_id":"d0edc26b-4c28-439d-9e45-c3d4e2f0c30f","resolution":{"observed_at":"2026-08-07T04:07:20.315126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.14768","last_updated":"2025-02-20T17:49:26Z","snapshot_observed_at":"2026-08-15T06:02:45.207770Z","submitted_at":"2025-02-20T17:49:26Z","title":"Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.14768","snapshot_observed_at":"2026-08-07T04:07:19.450518Z","title":"Logic-rl: Unleashing llm reasoning with rule-based reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.450518Z"},"links":{"cited_paper":"/paper/2502.14768","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:f692f915c29387889a0b0f83d7f728dc4b836e6fcc4996c495bfea8a04be9761","observation_id":"fb3a8364-0f15-45e4-a92c-71d7b8ad0889","resolution":{"observed_at":"2026-08-07T04:07:19.450518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.453993Z","title":"Enhancing asymmetric web search through question-answer generation and ranking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.453993Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:2fce9f34b6b426a1a374ea1331f2f6b2e600e56ce428e84c29f3110418c3b3ed","observation_id":"9ea1ccfb-6162-4195-a9c1-7c875eeb26c7","resolution":{"observed_at":"2026-08-07T04:07:19.453993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07301","last_updated":"2025-06-05T16:34:24Z","snapshot_observed_at":"2026-08-17T12:00:23.281116Z","submitted_at":"2025-01-13T13:10:16Z","title":"The Lessons of Developing Process Reward Models in Mathematical Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.07301","snapshot_observed_at":"2026-08-07T04:07:19.457366Z","title":"The lessons of developing process reward models in mathematical reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.457366Z"},"links":{"cited_paper":"/paper/2501.07301","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:d9b9d421b3b49508568b0631e0e05caad6c4e0606010780cbf87e8da493d4dd2","observation_id":"b75992df-3462-43cd-8071-351633a7bb42","resolution":{"observed_at":"2026-08-07T04:07:19.457366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:07:19.460963Z","title":"Large language models for information retrieval: A survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.460963Z"},"links":{"citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:0e9f4209594345d343f4934e34dd10c1fa1c0b7ce622b2a8ecb09af00930836a","observation_id":"15c2237f-a813-49d5-b0b9-90c6013388f6","resolution":{"observed_at":"2026-08-07T04:07:19.460963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":35,"verified_exact":2,"verified_fuzzy":4},"total_outbound_references":42},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 6 inbound Pith citation observations for arXiv:2506.11603."}