{"as_of":"2026-08-18T13:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ce279ba4b8fc5ff706bce4a17c5eb825d0d0bd0f7efb840d87e43fd6c4d2a4f7","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:12:07.910923Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.19466/citation-record","integrity":"/paper/2506.19466/integrity","json":"/paper/2506.19466/citation-record.json","paper":"/paper/2506.19466"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:10.281078Z","title":"Dense passage retrieval for open-domain question answering,","venue":null,"work_id":"aee7081f-e0fe-4cb1-91e0-32f2e5fcc85f","year":2020},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.095697Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:5e47e2516b483ff9e156b268181b2ad00b9242672708c91cef109874837c9409","observation_id":"b28f482c-f30e-4069-9102-48002d62b226","resolution":{"observed_at":"2026-08-06T23:12:10.344904Z","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":"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":"2026-08-06T23:12:05.170334Z","title":"Text embeddings by weakly-supervised contrastive pre-training,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.170334Z"},"links":{"cited_paper":"/paper/2212.03533","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:d7318b7c4a5703cf4e0a8d4e9d56f231512a7ec4d592bda8ff94242361c199af","observation_id":"b456b0fe-f99f-428a-b5e0-3657a4598904","resolution":{"observed_at":"2026-08-06T23:12:05.170334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13576","last_updated":"2025-02-24T02:46:52Z","snapshot_observed_at":"2026-08-18T01:49:22.860545Z","submitted_at":"2024-05-22T12:12:40Z","title":"FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13576","snapshot_observed_at":"2026-08-06T23:12:05.245732Z","title":"Flashrag: A modular toolkit for efficient retrieval-augmented generation research,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.245732Z"},"links":{"cited_paper":"/paper/2405.13576","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:a51eb55bb8cadb378a8f14c3a5e4e68525a10cb197d4da6724ae0df885dd2f22","observation_id":"a14e490f-a1e1-4c33-b0fe-a68a7d81baeb","resolution":{"observed_at":"2026-08-06T23:12:05.245732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-08-06T23:12:05.297026Z","title":"Hybrid- flow: A flexible and efficient RLHF framework,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.297026Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:49a27c299ad7229ea87c33cd3d8d6eb5852dc8a96cca124f87b4a2e3d6591c7f","observation_id":"b209c25f-16bf-460a-a077-e843f2a1ce1c","resolution":{"observed_at":"2026-08-06T23:12:05.297026Z","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":"2026-08-06T23:12:10.148616Z","title":"Wikidata: a free collaborative knowledgebase,","venue":null,"work_id":"c423f9d7-16db-4f49-a41c-2d74b0b8fb46","year":2014},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.351642Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:afaf3c3f4ccf3f39cde3474074a25215222802bda37639bebc8a0decc2e898f6","observation_id":"d2657462-a267-4b9b-965e-2db8927dbc53","resolution":{"observed_at":"2026-08-06T23:12:10.186818Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:10.016001Z","title":"Constructing A multi-hop QA dataset for comprehensive evaluation of reasoning steps,","venue":null,"work_id":"b3d26437-5800-493b-9a30-9a30549c15cb","year":2020},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.452781Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:7f17b31d9a1bccdb4006bb4a87c5bbb72d41f18895a3835d2f4d2fe58a256a91","observation_id":"6f692e0f-9a36-420c-a77a-1bc7cf1da85b","resolution":{"observed_at":"2026-08-06T23:12:10.069329Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.921114Z","title":"Hotpotqa: A dataset for diverse, explainable multi-hop question answering,","venue":null,"work_id":"014d3fb9-74b8-4d79-8175-996540abee5a","year":2018},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.505525Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:f06c4eab2ab833696f434acb5b346b214e820c3d7b2dfbf6cdaee1bc5f68b820","observation_id":"40be7c40-705a-4692-8f61-023ed46fd783","resolution":{"observed_at":"2026-08-06T23:12:09.984588Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.742136Z","title":"Musique: Multihop questions via single-hop question composition,","venue":null,"work_id":"30b14a59-da9f-4c14-b31a-937a83ce8502","year":2022},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.601056Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:a4142fa1a027bb08b7b399efe55d45d3465c2bcab8ca4e0829d12ebd74687a5f","observation_id":"8c9347db-16f0-45bb-ba9d-7d54f55aba41","resolution":{"observed_at":"2026-08-06T23:12:09.829325Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.649521Z","title":"Dense text retrieval based on pretrained language models: A survey,","venue":null,"work_id":"3e7c5963-4890-4367-81e4-7ecde8871ee9","year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.674945Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:2aa430d38c4fdd283fa6e3e9266e7abc6d7d524628640c173f544eee373fc541","observation_id":"6450132c-0e5b-47b5-9f59-1122737e9215","resolution":{"observed_at":"2026-08-06T23:12:09.703511Z","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":"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":"2026-08-06T23:12:05.739682Z","title":"Deepseek- math: Pushing the limits of mathematical reasoning in open language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.739682Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:b79ecd880ab840f6678971018720dabf2ccf2eb04d989d650b990123a30f6254","observation_id":"9bfeb630-36f1-4b1a-be7d-15f5428bf719","resolution":{"observed_at":"2026-08-06T23:12:05.739682Z","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":"2026-08-06T23:12:09.502082Z","title":"Measuring and narrow- ing the compositionality gap in language models,","venue":null,"work_id":"02085da0-1c77-4161-93ae-6f2b97767a76","year":2023},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.819735Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:e1729932a5fb92e0ddc9c024a8d3aa209fea128c80a6ef7ffd93f4c65ed31131","observation_id":"e258f0cc-ba7b-42e7-9d36-7ccf52781576","resolution":{"observed_at":"2026-08-06T23:12:09.550540Z","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":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-06T23:12:05.872413Z","title":"Scaling LLM test-time compute optimally can be more effective than scaling model parameters,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.872413Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:8be8a6891edd60d450e23c23a8fa2cf97362ae167ec1268648b2a6f8f6be14d4","observation_id":"0ff7d332-dcbf-48da-9744-4dcde3b6636b","resolution":{"observed_at":"2026-08-06T23:12:05.872413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19393","last_updated":"2025-03-01T06:07:39Z","snapshot_observed_at":"2026-08-17T11:00:39.333660Z","submitted_at":"2025-01-31T18:48:08Z","title":"s1: Simple test-time scaling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19393","snapshot_observed_at":"2026-08-06T23:12:05.928572Z","title":"s1: Simple test-time scaling,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:05.928572Z"},"links":{"cited_paper":"/paper/2501.19393","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:897eb4db227e309b65a559979c9b146b2a167933c8d2791b9067c08bbcbf622b","observation_id":"42f763d3-caf8-4495-a44b-374b559cd64e","resolution":{"observed_at":"2026-08-06T23:12:05.928572Z","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":"2026-08-06T23:12:09.348949Z","title":"Star: Bootstrapping reasoning with reasoning,","venue":null,"work_id":"e474a8f4-a464-4d62-8af4-8e8a3fe6c494","year":2022},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.016903Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:2dba3ee741d401bbd8dfc01cc9fd095a6c8c84b33b03a7ba6da8fe46c9615816","observation_id":"1672311e-6c52-42a0-89d0-1b61e1f9b726","resolution":{"observed_at":"2026-08-06T23:12:09.385853Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.212883Z","title":"Chain-of-thought prompting elicits reasoning in large language models,","venue":null,"work_id":"21aa1f71-ebe5-461e-bd23-9ef74e182fb5","year":2022},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.159651Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:cedae4d04ffbac803cb993fd4a5f3a39b061b607db9c912f8b013eb280b850df","observation_id":"baebe486-1ace-4d6c-b545-2adfd06a3280","resolution":{"observed_at":"2026-08-06T23:12:09.272443Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:09.079860Z","title":"Claude 3.7 sonnet and claude code,","venue":null,"work_id":"8227c17c-5ad3-4168-b8c9-155b4f9335da","year":2025},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.268786Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:ea283193a5447d5a4021b77d213d302d6190e310b68bae6af5128b2ffaac3c4f","observation_id":"eb846aeb-6d90-4727-bb14-c174096e2d13","resolution":{"observed_at":"2026-08-06T23:12:09.155434Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:08.986656Z","title":"Learning to reason with LLMs,","venue":null,"work_id":"6fec66a8-f161-49b5-ab06-7c0337148b80","year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.394461Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:435d32209b90e697b3d39db864968c8b68420fa2c7c629f2f9f1b4e602cfea94","observation_id":"666e854f-8694-4af3-9503-9ff282f7e764","resolution":{"observed_at":"2026-08-06T23:12:09.049985Z","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":"2401.15884","last_updated":"2024-10-07T02:19:21Z","snapshot_observed_at":"2026-08-17T09:14:38.619275Z","submitted_at":"2024-01-29T04:36:39Z","title":"Corrective Retrieval Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15884","snapshot_observed_at":"2026-08-06T23:12:06.524660Z","title":"Corrective retrieval augmented generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.524660Z"},"links":{"cited_paper":"/paper/2401.15884","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:f3b9e70a41fe7ba77b5a80ad2610501d160e2bb5a05d0417e109e095bf19d4ec","observation_id":"93d76a24-1926-42c6-a41e-01496f84d72a","resolution":{"observed_at":"2026-08-06T23:12:06.524660Z","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":"2026-08-06T23:12:08.830899Z","title":"Self-rag: Learning to retrieve, generate, and critique through self-reflection,","venue":null,"work_id":"af86f13c-bd19-4262-a3bc-bb828bfc3f82","year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.669760Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:3d6e5edde9f638a38dd3f58805a8a61f73d4bb3ff03bf11e98b2838bf46ddbc6","observation_id":"2ded99ab-9ff1-4a4d-9aac-966e85c8cf3d","resolution":{"observed_at":"2026-08-06T23:12:08.914395Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:08.690364Z","title":"Interleaving retrieval with chain- of-thought reasoning for knowledge-intensive multi-step questions,","venue":null,"work_id":"bf433a73-ebfc-40b4-8f4f-65bffac7ce27","year":2023},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.775608Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:96cd9d69fb8c953a1b8299c976cf5ffcca5572ed171b9d287d68e3dac8d4ce0f","observation_id":"c46de477-cbaf-4cfc-a8f6-9a5bd52babb7","resolution":{"observed_at":"2026-08-06T23:12:08.749270Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:08.573849Z","title":"Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy,","venue":null,"work_id":"412f8750-4a22-4758-a5fc-9f365c7d22dd","year":2023},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:06.897310Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:f3621da19f1b2f490232ee2950bfd7379570eba8f43dc955530ac9913510a43b","observation_id":"68727962-532d-4d20-9350-0acf85bbec17","resolution":{"observed_at":"2026-08-06T23:12:08.608468Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:08.434013Z","title":"Hugginggpt: Solving AI tasks with chatgpt and its friends in hugging face,","venue":null,"work_id":"feecb7c9-240e-470d-abf7-70abe857c7d6","year":2023},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.060761Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:778662a3edbfd29badfd42c2bd781f9053aec6549f2975c4dd900ccfbf7b1657","observation_id":"928a7eef-c0e7-40ce-96ce-f78a9cfaf476","resolution":{"observed_at":"2026-08-06T23:12:08.483283Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:12:08.290824Z","title":"Agentboard: An analytical evaluation board of multi-turn LLM agents,","venue":null,"work_id":"f041824b-2ea7-4f42-8b23-d251635285d5","year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.174006Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:96ecb3e6719bace7dab41c03568b449891c41383709e790fec16f5c45589b751","observation_id":"c1f16e2b-ed7f-4310-82ac-01e630ba5290","resolution":{"observed_at":"2026-08-06T23:12:08.358651Z","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":"2410.12952","last_updated":"2025-03-03T02:27:02Z","snapshot_observed_at":"2026-08-18T11:02:17.603500Z","submitted_at":"2024-10-16T18:40:26Z","title":"Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12952","snapshot_observed_at":"2026-08-06T23:12:07.292016Z","title":"Facilitating multi-turn function calling for llms via compositional instruction tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.292016Z"},"links":{"cited_paper":"/paper/2410.12952","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:905e891ce123407434bfc3a627d3a4ba9a4e11a66677dc17dd4b2362224922aa","observation_id":"b2b5741f-5f9a-4168-bd57-cfb611c8c69f","resolution":{"observed_at":"2026-08-06T23:12:07.292016Z","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":"2026-08-06T23:12:07.399789Z","title":"Toolformer: Language models can teach themselves to use tools,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.399789Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:19016c98319e0bcf1008a7f3facfab3464a614ebc226474371f7dc68b1a213a9","observation_id":"488cc88f-5cdc-43ce-8c9a-fc21d0ad6400","resolution":{"observed_at":"2026-08-06T23:12:07.399789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-06T23:12:07.518791Z","title":"Retrieval-augmented generation for large language models: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.518791Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:4864ebebf8641226946bf53b48bc02ba0d872504a1ced6b92aa5657a22230e4a","observation_id":"45fa2f83-20da-49ce-9a68-b323a055354d","resolution":{"observed_at":"2026-08-06T23:12:07.518791Z","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":"2026-08-06T23:12:08.153808Z","title":"Learning to plan for retrieval-augmented large language models from knowledge graphs,","venue":null,"work_id":"514f46ec-c78d-4a0a-89e0-0e2cf1b769f9","year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.614369Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:b61c5f1eb916dad3f8de2eb53ed8840ab07e0e6d2c2c8e6700cf16abf9868057","observation_id":"b7d9e762-9a52-4293-ac28-0bbd49793e2b","resolution":{"observed_at":"2026-08-06T23:12:08.250087Z","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":"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":"2026-08-06T23:12:07.710550Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.710550Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:782f204b2e08c5418af29c008811c16b0b7a5cdc99512e25db12eb679d9d2b86","observation_id":"540ce1d4-3a5b-4d22-acfb-47d197481a22","resolution":{"observed_at":"2026-08-06T23:12:07.710550Z","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":"2026-08-06T23:12:07.860861Z","title":"Baichuan alignment technical report,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.860861Z"},"links":{"citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:e418c517792fc6184fb03b23ea2f9a5073a22d7b5903e111a70e7a6c4b757754","observation_id":"4e434e1e-7fc2-40ee-9add-daf756dc936c","resolution":{"observed_at":"2026-08-06T23:12:07.860861Z","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":"2026-08-06T23:12:07.910923Z","title":"Qwen2.5 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:07.910923Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2506.19466"},"observation_digest":"sha256:07df539253e2189921acf0f46b5d4fef0953cea1b32662b54a2918e72a5365af","observation_id":"1a78b8b8-2a12-4384-8966-402c0b4d2ebb","resolution":{"observed_at":"2026-08-06T23:12:07.910923Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.19466","last_updated":"2025-06-27T08:11:14Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-14T05:57:23.640404Z","submitted_at":"2025-06-24T09:48:01Z","title":"KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":30},"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 18 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.19466."}