{"as_of":"2026-08-06T20:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91aea91da247f92ca84cffd79a1ddf84f9e2d70fdce1e075ad11033dbcde7984","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T06:55:50.287160Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2510.14264/citation-record","integrity":"/paper/2510.14264/integrity","json":"/paper/2510.14264/citation-record.json","paper":"/paper/2510.14264"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T15:56:19.744764Z","title":"Learning representations by back-propagating errors.nature, 323(6088):533–536","venue":null,"work_id":"ec5289ca-1c80-4dca-92de-daaf18d78d17","year":1986},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:b66abd21a4b8df07e8fddf9c5c5544b3bbbcd613461337523a124e927f35af4e","observation_id":"cd4c3ec8-844f-4068-b57e-543342fce9ef","resolution":{"observed_at":"2026-05-18T06:56:01.741632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Support-vector networks.Machine learning, 20(3): 273–297","venue":null,"work_id":"822d3ab6-ec9d-4236-af57-22ec352c2bda","year":1995},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:770163c65e97aa6d3f6b83350c6ca8a638419eb20b4997a6c058a7074abf2cc2","observation_id":"db6e5813-3e1d-4968-b75c-40d9beb6b7cf","resolution":{"observed_at":"2026-05-18T06:56:01.730002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T16:26:20.846937Z","title":"Random forests.Machine learning, 45(1):5–32","venue":null,"work_id":"2fa899e2-80ef-4a5d-8ae4-c9386f6b3564","year":2001},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:188be05168cf20e052161477a79377d14bba977ce3ce18cf9b617b8cd7b324d1","observation_id":"866c89aa-d258-42fc-9775-a6c5fefa435f","resolution":{"observed_at":"2026-05-18T06:56:01.721005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reinforcement learning for trading","venue":null,"work_id":"b5eef2fe-3879-422a-a922-e34f909c18b4","year":1998},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:e5f9d6727e93d01c181b13ae03d1c01612fd7424b9fdae5f591c4e702bbca9c8","observation_id":"ee2e7241-cbcc-402a-85bf-5240d7d4af59","resolution":{"observed_at":"2026-05-18T06:56:01.725324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deeptrader: a deep reinforce- ment learning approach for risk-return balanced portfolio management with market conditions embedding","venue":null,"work_id":"e2bed70d-743c-4296-8a6e-d1ebf0ed0c57","year":2021},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:b6cc0b96acb02a421d6a3de5dba917934894a890c1d98e776cdfa0e9a5a7b35b","observation_id":"4a67bece-7e7f-4ff7-b281-4ca0386d5187","resolution":{"observed_at":"2026-05-18T06:56:01.712155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20138","last_updated":"2025-06-03T05:45:06Z","snapshot_observed_at":"2026-07-06T20:14:03.824268Z","submitted_at":"2024-12-28T12:54:06Z","title":"TradingAgents: Multi-Agents LLM Financial Trading Framework","version":7},"cited_work":{"arxiv_id":"2412.20138","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.20138","snapshot_observed_at":"2026-07-04T20:00:07.722366Z","title":"Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang","venue":null,"work_id":"e792c1b3-342a-4de2-98eb-fa551be974af","year":2024},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2412.20138","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:72f635cf4b7435386a54c119ba19bac88147c34bc763ade8ba41f45c9f61fe4a","observation_id":"0f61b116-846a-44f5-a1f4-8603d8fe8936","resolution":{"observed_at":"2026-05-18T06:56:00.812350Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7528.367180","doi":"10.1145/3637528.3671800","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A multimodal 9 foundation agent for financial trading: Tool-augmented, diversified, and generalist","venue":null,"work_id":"2f5156f1-9a81-42ae-9ddc-2508cce455d5","year":2026},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:a1bb77d19421de923abee280655fd1837becb3059655de962abab1f9122f05bc","observation_id":"f2e71900-b842-4cfc-953d-db4d64fc3f71","resolution":{"observed_at":"2026-05-18T06:56:00.610902Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2308.00016","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T21:37:25.580871Z","title":"org/abs/2308.00016","venue":null,"work_id":"583bce62-bb2e-4f60-b6c4-b9a4dabab3e2","year":2023},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:6752c4dc52b5e43cacfc185c0eb1b42fcfe4cca871c5c37920743421d35faedc","observation_id":"d9c80896-3d89-4cdc-aa4e-02d6e001625f","resolution":{"observed_at":"2026-05-18T06:56:00.792298Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T01:45:51.694053Z","title":"Narasimhan, and Yuan Cao","venue":null,"work_id":"581327e3-c4e8-466f-99d5-41cf7bf7d919","year":2023},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:47f4149d92b14ef55bb574ab3545b8e52ea10ac5a632580ef542bcdace287e86","observation_id":"fce868b5-0840-4e36-9eeb-a543a4d991b1","resolution":{"observed_at":"2026-05-18T06:56:01.716391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T06:04:35.231940Z","title":"URLhttps://openreview.net/forum?id=WE_vluYUL-X","venue":null,"work_id":"599f2607-43b0-4882-88a9-46bc86953ca3","year":2023},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:c52fcb0b506972428933059f0d5e0a2224e9a3d5015a7c371a5903eea39b4c3e","observation_id":"6233f61d-0742-44e3-b130-b9de3020b84b","resolution":{"observed_at":"2026-05-18T06:56:01.734935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2501.12948","doi":"10.1016/j.artmed.2024.103001","metadata_source":"pith","pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","venue":"cs.CL","work_id":"e6b75ad5-2877-4168-97c8-710407094d20","year":2025},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:c32aa209923b2f760f80d1401597635712101e1824b76c17e965d6fcb9ce3c2e","observation_id":"c2dfe8b7-e6cf-4d2d-b238-13e484cabb80","resolution":{"observed_at":"2026-05-18T06:56:00.778782Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15124","last_updated":"2025-04-14T22:39:09Z","snapshot_observed_at":"2026-07-06T19:55:37.400185Z","submitted_at":"2024-11-22T18:44:04Z","title":"Tulu 3: Pushing Frontiers in Open Language Model Post-Training","version":5},"cited_work":{"arxiv_id":"2411.15124","doi":"10.48550/arxiv.2411.15124","metadata_source":"pith","pith_arxiv_id":"2411.15124","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tulu 3: Pushing Frontiers in Open Language Model Post-Training","venue":"cs.CL","work_id":"28c9dbea-056a-48c2-8000-85f809827e45","year":2024},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2411.15124","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:9f19251f140ac27064e759ad61ca844baf4797b0bec3c6cb9817f69e39af3f54","observation_id":"66451e8b-f1a0-4062-95f4-bf5f768e059f","resolution":{"observed_at":"2026-05-18T06:56:00.768159Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-23T21:53:00.522112+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T21:53:00.522112+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advancements and applications of artificial intelligence in stock market prediction","venue":null,"work_id":"a2afabb2-1909-48c8-b127-14db517b0be1","year":2025},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:c33560136a55014fedcb3d271dc5573410a72ed0e236d390716069da92a5db32","observation_id":"f659e2ed-19cf-4713-8223-4c59400b19d4","resolution":{"observed_at":"2026-05-18T06:56:01.746370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adaptive quantitative trading: An imitative deep reinforcement learning approach","venue":null,"work_id":"63b43754-db51-4124-ad41-9420a2344d34","year":2020},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:d5f0ada5e64c4f08bed26396da372d7f4a793d0dc68ea94407a19299ceed7889","observation_id":"be065ac7-a2c2-448b-9d8d-e005d972d62a","resolution":{"observed_at":"2026-05-18T06:56:01.755219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11433","last_updated":"2025-02-19T03:40:56Z","snapshot_observed_at":"2026-07-06T20:37:33.041473Z","submitted_at":"2025-02-17T04:45:53Z","title":"FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading","version":3},"cited_work":{"arxiv_id":"2502.11433","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.11433","snapshot_observed_at":"2026-07-01T10:05:41.295960Z","title":"Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, and Qianqian Xie","venue":null,"work_id":"d6476605-0c1d-49c7-8727-216114f4fe5d","year":2025},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2502.11433","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:f28ff66f949c085f27a8c92bd9317b544381ef03f19a924111be40bbfcdcf729","observation_id":"3c9a0e90-8ebe-4924-b734-d47e76be8cb0","resolution":{"observed_at":"2026-05-18T06:56:00.798608Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2509.11420","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Trading-r1: Financial trading with llm reasoning via reinforcement learning","venue":null,"work_id":"150b2c6b-c78e-4c8a-aad4-4384ee4c23a3","year":2025},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:5fa158ecf24a1d4006dc25fec278b03fc2ea9056c4928e30b00a7be8416fc131","observation_id":"461facbd-c4cd-48d6-a1b0-297b31927dff","resolution":{"observed_at":"2026-05-18T06:56:00.785869Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/cvpr.2016.308","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T06:05:28.583621Z","title":"URLhttps://doi.org/10.1109/CVPR.2016.308","venue":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","work_id":"1496bd95-cdd3-4b5c-a899-57e2470c31d9","year":2016},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:d21b452eb6b18b52d8630bb819342a2601e0b821338b00b043659e95a3587b4a","observation_id":"0c1ef657-dd13-4f15-ac3e-df4dcff26fae","resolution":{"observed_at":"2026-05-18T06:56:00.619545Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-01T11:08:08.075478+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T11:08:08.075478+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.09607","last_updated":"2022-03-02T14:28:11Z","snapshot_observed_at":"2026-08-04T16:53:20.285596Z","submitted_at":"2020-11-19T01:35:05Z","title":"FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance","version":2},"cited_work":{"arxiv_id":"2011.09607","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.09607","snapshot_observed_at":"2026-07-03T04:27:36.822253Z","title":"Finrl: A deep reinforcement learning library for automated stock trading in quantitative finance.CoRR, abs/2011.09607","venue":null,"work_id":"38898c4b-189f-426f-87de-6fea9671ac8a","year":2020},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2011.09607","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:5ae597fb8d8e77d7e4b38280a091158c12d4f7f28bf7aa04c75acfcc89bfa4f5","observation_id":"8ed66bbf-a0a8-4a94-9af3-9e6b78c9d8f2","resolution":{"observed_at":"2026-05-18T06:56:00.760949Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2975974","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T02:59:26.025874Z","title":"URL http://www.jstor.org/stable/2975974","venue":null,"work_id":"d12e0fa9-009e-41d0-915f-84bc79c7afac","year":1952},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:43d1dec2d342d9def2f50c4e7372bb0aedbd8099d470557dd229f5f56c00ab54","observation_id":"045206d7-0f81-4f75-9f2a-a3383f29cdb1","resolution":{"observed_at":"2026-05-18T06:56:00.805518Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v38i13.29384","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Earnhft: Effi- cient hierarchical reinforcement learning for high frequency trading","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"cd90f110-6b8b-4c77-81d8-3259b22a50b1","year":2024},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:da94d917984d7fe0cef73aff03a6057719fb7cfb3235ad4a9d86be717e352ac7","observation_id":"570a9cfa-672b-4b05-ac8e-1ff45b4d56db","resolution":{"observed_at":"2026-05-18T06:56:00.634823Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, and Qianqian Xie","venue":null,"work_id":"b7399d2d-2135-45e2-8ddd-c00c782b0cd1","year":2025},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:610a552944f304458caf22dde337520880ef21f118c9cf9b5731e4e6f092acfc","observation_id":"013b766a-e00f-4424-aced-0853b640c64e","resolution":{"observed_at":"2026-05-18T06:56:01.751019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":"2410.21276","doi":"10.1177/15248380231178756","metadata_source":"pith","pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPT-4o System Card","venue":"cs.CL","work_id":"f37bf1c7-4964-4e56-9762-d20da8d9009f","year":2024},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:74bfaa85b7e83f8abdc47919fb7efcbeada201173bab2e63cc40db1db056f2e6","observation_id":"2058b29e-7120-4521-9727-b63e3b966145","resolution":{"observed_at":"2026-05-18T06:56:00.645766Z","resolver_source":"local_arxiv","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"9031.369607","doi":"10.1145/3689031.3696072","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Hybridflow: A flexible and efficient rlhf framework","venue":null,"work_id":"4909736c-3fe1-4820-b489-cca51669c6d2","year":2025},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:1fc96fab9abeb3132c376fe23a8f5dd29517443a4c6fdb625e1069d380b69116","observation_id":"762d1386-aad8-4c45-a080-417cc6602a68","resolution":{"observed_at":"2026-05-18T06:56:00.655598Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2402.03300","doi":"10.1016/0004-3702(73)90011-8","metadata_source":"pith","pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","venue":"cs.CL","work_id":"c5006563-f3ec-438a-9e35-b7b484f34828","year":2024},"citing_paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-18T06:55:50.287160Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2510.14264"},"observation_digest":"sha256:5100efeeb812867b5f23d7a83d5526d9197dcd17326bd71ae205d1a3025b3d43","observation_id":"52b02aa8-09b8-4164-8d43-1432a52290a5","resolution":{"observed_at":"2026-05-18T06:56:00.752936Z","resolver_source":"local_arxiv","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2510.14264","last_updated":"2026-04-19T16:34:18Z","latest_version":2,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-02T11:29:41.329801Z","submitted_at":"2025-10-16T03:30:22Z","title":"AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":2,"metadata_mismatch":7,"parse_uncertain":0,"unresolved":0,"verified_exact":5,"verified_fuzzy":10},"total_outbound_references":24},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2510.14264."}