{"as_of":"2026-08-09T22:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fbbcfba6ca32cda96b2406a4ed03efc0e39d36c61aab27350e4bb390f956dbbf","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T04:37:28.378324Z","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":"2026-07-03T09:07:48.430556Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2309.03409","last_updated":"2024-04-15T07:50:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-07T00:07:15Z","title":"Large Language Models as Optimizers","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T00:04:31.212102Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2309.03409"},"observation_digest":"sha256:4b016bc245562a66abe5138ed0aef8e5464f8639492d5e1f8ac2b78c6c1c419f","observation_id":"88d78e69-6e92-4862-96d1-121383366bc7","resolution":{"observed_at":"2026-05-15T00:04:31.254934Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2407.17491","last_updated":"2026-04-03T23:10:26Z","snapshot_observed_at":"2026-08-02T18:34:18.562354Z","submitted_at":"2024-07-04T02:35:00Z","title":"Robust Adaptation of Foundation Models with Black-Box Visual Prompting","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-23T23:23:03.562550Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2407.17491"},"observation_digest":"sha256:54210208ec965fc3484da2889b33583dfee0d533f7c80f721297dd76d98a2295","observation_id":"fe76dec6-689a-4f2a-8034-f114824f7bde","resolution":{"observed_at":"2026-05-23T23:23:36.387152Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-08-08T04:37:28.378324Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.08586","last_updated":"2025-02-12T17:19:36Z","snapshot_observed_at":"2026-08-08T23:24:16.398221Z","submitted_at":"2025-02-12T17:19:36Z","title":"Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T04:37:28.378324Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2502.08586"},"observation_digest":"sha256:7a09e6b4997c5f2de3957a4057982da95d4a2a71230f5cab5a86d9c38ddcad91","observation_id":"bc7e9b01-f2a8-4054-9ba0-f5b095ca68cf","resolution":{"observed_at":"2026-08-08T04:37:28.378324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-08-07T12:02:10.501676Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00806","last_updated":"2025-06-01T03:15:29Z","snapshot_observed_at":"2026-08-09T07:52:39.727951Z","submitted_at":"2025-06-01T03:15:29Z","title":"Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:02:10.501676Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2506.00806"},"observation_digest":"sha256:d454669c19187a03dc64d29bda916cba7827017f6cf03883435522c73536db0e","observation_id":"d579cd0e-5493-453e-8983-a637d4941938","resolution":{"observed_at":"2026-08-07T12:02:10.501676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-08-07T05:10:39.500635Z","title":"P., and Hu, Z","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08669","last_updated":"2025-06-10T10:30:43Z","snapshot_observed_at":"2026-08-08T04:30:56.447904Z","submitted_at":"2025-06-10T10:30:43Z","title":"Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T05:10:39.500635Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2506.08669"},"observation_digest":"sha256:9e1ae611b2a3874dcf266a9fa7c468f03ce400dacde2c53319f2bb6dea362644","observation_id":"1d8ed3ec-49bf-4b51-9ed1-26ae0b82aff0","resolution":{"observed_at":"2026-08-07T05:10:39.500635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-08-06T20:21:22.259301Z","title":"Deng, et al., ”RLPrompt: Optimizing discrete text prompts with reinforcement learning,” arXiv preprint arXiv:2205.12548 , 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.03223","last_updated":"2025-07-03T23:44:50Z","snapshot_observed_at":"2026-08-06T23:56:27.012210Z","submitted_at":"2025-07-03T23:44:50Z","title":"SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:21:22.259301Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2507.03223"},"observation_digest":"sha256:a4cb3bf2b01eac9669da1ad0cd605b93ef1de3b8732b82ab7d053173376421ad","observation_id":"224d88e8-ab61-4fb8-93bf-d059a9da626b","resolution":{"observed_at":"2026-08-06T20:21:22.259301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-08-06T16:42:30.841622Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.12855","last_updated":"2025-07-17T07:26:22Z","snapshot_observed_at":"2026-08-07T15:04:47.728946Z","submitted_at":"2025-07-17T07:26:22Z","title":"DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:42:30.841622Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2507.12855"},"observation_digest":"sha256:cd9b84ab8dbc07fbb81561743c69529074662718068eea140860027813b17352","observation_id":"75487985-9309-43ef-b8a0-89b8d87f7ec6","resolution":{"observed_at":"2026-08-06T16:42:30.841622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-08-06T14:35:46.534226Z","title":"Rlprompt: Optimizing discrete text prompts with reinforce- ment learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.18618","last_updated":"2025-07-24T17:54:44Z","snapshot_observed_at":"2026-08-09T21:46:06.381115Z","submitted_at":"2025-07-24T17:54:44Z","title":"TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:35:46.534226Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2507.18618"},"observation_digest":"sha256:cff9519998e5a865bf5748b6288f7e331e87849a25a9f6aed56a103b91281d94","observation_id":"8344a737-9e84-4b6f-b48d-f63975534ed0","resolution":{"observed_at":"2026-08-06T14:35:46.534226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2512.11013","last_updated":"2026-04-07T17:30:34Z","snapshot_observed_at":"2026-07-06T22:38:50.534580Z","submitted_at":"2025-12-11T16:55:30Z","title":"PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-16T23:15:52.444217Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2512.11013"},"observation_digest":"sha256:00328e62b9cbac34964a68dbe63f3d74ed4271086aab4bde00ead3bbf21eef6f","observation_id":"3bd1bca7-01ff-4995-ab4f-ae97d6c44e1b","resolution":{"observed_at":"2026-05-16T23:18:40.114020Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2603.10477","last_updated":"2026-04-08T07:03:59Z","snapshot_observed_at":"2026-08-03T13:46:05.416163Z","submitted_at":"2026-03-11T07:00:59Z","title":"PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-15T13:57:41.428695Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2603.10477"},"observation_digest":"sha256:2c61b872e7ba919a3be5cbf50a62c66d9fdda716f40bfb879c3580100877c21d","observation_id":"55da2032-0889-4030-b6ed-b493c56ee86f","resolution":{"observed_at":"2026-05-15T14:00:03.031641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2605.12484","last_updated":"2026-05-14T17:49:32Z","snapshot_observed_at":"2026-08-02T06:23:20.104772Z","submitted_at":"2026-05-12T17:58:20Z","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-13T05:00:31.452781Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2605.12484"},"observation_digest":"sha256:19bd60fa8c6ebf48279b938685205d5d66a41d4712dad840a06b78aef48eeb94","observation_id":"395c8fda-1aa3-435d-928c-c3640072b345","resolution":{"observed_at":"2026-05-13T05:07:18.437010Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2605.12484","last_updated":"2026-05-14T17:49:32Z","snapshot_observed_at":"2026-08-02T06:23:20.104772Z","submitted_at":"2026-05-12T17:58:20Z","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T05:19:05.368681Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2605.12484"},"observation_digest":"sha256:cd9430fd21f05ace66faab8627be77206c226898b000180f72769cfa23cd244d","observation_id":"803935cc-b03b-412d-a9e3-80369770b8cb","resolution":{"observed_at":"2026-05-15T05:19:45.771542Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2605.19102","last_updated":"2026-05-18T20:42:23Z","snapshot_observed_at":"2026-07-06T23:29:52.061489Z","submitted_at":"2026-05-18T20:42:23Z","title":"Prompt Optimization for LLM Code Generation via Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-20T08:49:36.986452Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2605.19102"},"observation_digest":"sha256:910d6e9ce30a03ca4c8557832b720f94c81203b4f4b75d0cd8e26b9c7b7cf343","observation_id":"139d43b9-f050-42c1-b168-10468afda8a5","resolution":{"observed_at":"2026-05-20T08:53:10.355874Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2205.12548","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.12548","snapshot_observed_at":"2026-07-03T09:07:48.430556Z","title":"Rlprompt: Optimizing discrete text prompts with reinforcement learning","venue":null,"work_id":"7d76c540-517d-4c8b-aa8b-cd53486d6ca8","year":2022},"citing_paper":{"arxiv_id":"2606.11853","last_updated":"2026-06-10T09:30:25Z","snapshot_observed_at":"2026-08-02T20:07:37.622537Z","submitted_at":"2026-06-10T09:30:25Z","title":"Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-06-27T10:28:11.440915Z"},"links":{"cited_paper":"/paper/2205.12548","citing_paper":"/paper/2606.11853"},"observation_digest":"sha256:7b0313f076483b4c602e33c33e125dcef649c173f89acfd791c73128ae499041","observation_id":"8f7e93f8-0978-4034-aa9a-110e29e52fd2","resolution":{"observed_at":"2026-07-03T09:07:48.432137Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2205.12548/citation-record","integrity":"/paper/2205.12548/integrity","json":"/paper/2205.12548/citation-record.json","paper":"/paper/2205.12548"},"outbound":[],"paper":{"arxiv_id":"2205.12548","last_updated":"2022-10-22T15:21:46Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T09:47:52.854204Z","submitted_at":"2022-05-25T07:50:31Z","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2205.12548."}