{"as_of":"2026-08-12T21:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e20a276d90911ece49af4ce7daa2b98835fbd98a6d4b1233b446403bf94c05e6","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:36:11.220159Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2501.15217/citation-record","integrity":"/paper/2501.15217/integrity","json":"/paper/2501.15217/citation-record.json","paper":"/paper/2501.15217"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T14:36:11.152230Z","title":"Human-level control through deep reinforcement learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.152230Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:13b6420bb6e2ba652275e115fc0ca2e0b88a88714de18408b2b4264420c31e5b","observation_id":"8a851b8a-4e39-495e-89d9-e70dabceabf5","resolution":{"observed_at":"2026-08-10T14:36:11.152230Z","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":"2026-08-10T14:36:11.456945Z","title":"Mastering the game of go with deep neural networks and tree search,","venue":null,"work_id":"20cae382-43d0-4cae-b199-84e7a445c2b3","year":2016},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.157730Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:b7806816a1fa9f459329d350999435ac762d7f662273fc3000d0d2f30096aabe","observation_id":"f1c9607c-fa41-443e-a16c-fa4cb1b674d3","resolution":{"observed_at":"2026-08-10T14:36:11.461677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.442212Z","title":"Distributional soft actor-critic: Off-policy reinforcement learning for addressing value estimation errors,","venue":null,"work_id":"ee788229-2db8-4555-8dca-c1084d9ef88b","year":2021},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.162463Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:27643190ecd67544bbeb8bc32215913f7d13d61ca468c16ce7e27afe6b32a771","observation_id":"4b5092f4-f73c-4d3f-8f7e-606da9b3c761","resolution":{"observed_at":"2026-08-10T14:36:11.446832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.427670Z","title":"A transformation-aggregation framework for state representation of au- tonomous driving systems,","venue":null,"work_id":"7ca98811-3d12-4cd6-a3d1-5926dc8b5e91","year":2024},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.166882Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:189f5039809547fc4a875af94cfa09a17507ef60fd2a718c5eef8c79535a7b28","observation_id":"d0f7a593-2512-485b-a701-94ffbbb68fad","resolution":{"observed_at":"2026-08-10T14:36:11.432376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.413155Z","title":null,"venue":null,"work_id":"7cfeb37c-4714-49e9-9b7b-4776f21b1eb2","year":2023},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.171305Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:34baa84418934ad33b891b3045e43aa2dad9ea615df7baaa43d2b293738dafbc","observation_id":"559ca1fd-67fc-428c-98c1-0d1223bbd7d8","resolution":{"observed_at":"2026-08-10T14:36:11.417777Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.397052Z","title":"Direct and indirect reinforcement learning,","venue":null,"work_id":"d04d9c7f-c9ea-4eb3-a6d4-fcb4b4ebab5d","year":2021},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.176191Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:6a48984a5f9d5454e70a9416e9417e1d952f0a1fba1e1fc3440031f119a44a05","observation_id":"2c0456ab-157a-4930-b08b-4430b6f269bf","resolution":{"observed_at":"2026-08-10T14:36:11.402702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.380788Z","title":"A dynamic penalty function approach for constraint-handling in reinforcement learning,","venue":null,"work_id":"c74fd278-62de-4be0-b838-081b5682cfe4","year":2021},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.182344Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:be97744fd59e3877d6c492347e06f304ef7a18fe3a93d9a328eca0d703cf546a","observation_id":"eaf139ff-44c5-4d61-90e2-573c482a486f","resolution":{"observed_at":"2026-08-10T14:36:11.385834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.365708Z","title":"Self- learned intelligence for integrated decision and control of automated vehicles at signalized intersections,","venue":null,"work_id":"28312596-b428-4f6e-954a-22d289fcdd24","year":2022},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.187789Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:2c8c84a8431da6e4988493a317db0232b3c794ef0199d9a74959072ff610f507","observation_id":"5e382ad8-89aa-45ab-a24c-03adecf7b045","resolution":{"observed_at":"2026-08-10T14:36:11.370392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.349021Z","title":"Learning safe policies via primal-dual methods,","venue":null,"work_id":"df240278-6f2b-4960-a6c4-b94c885d62f0","year":2019},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.192364Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:4d69c7e4ee8fa25df6ebe56aa8508ff36435a51bc398b85305afe14aa5ae9de1","observation_id":"8fcd0f6a-4877-4e97-bc40-228b30594f1c","resolution":{"observed_at":"2026-08-10T14:36:11.355274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.333077Z","title":"Dynamical, symplectic and stochastic perspectives on gradient-based optimization,","venue":null,"work_id":"71df1d9f-49c9-49e9-b309-21a1f1c6b9df","year":2018},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.197335Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:159b9d7105da878a2706c28a06dd98e78d693c77ba2ab591608d32aff64735f7","observation_id":"3adb2ecd-37e9-412e-9bc7-d9fa8bf646e2","resolution":{"observed_at":"2026-08-10T14:36:11.338350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.315976Z","title":"Responsive safety in re- inforcement learning by PID lagrangian methods,","venue":null,"work_id":"245e3df3-fba1-4059-9d4e-49129cbcc0b9","year":2020},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.201907Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:3bf6c26e27e8fef7233b165fa1e742fee99f9ab3667c882d653ec64a403a3f46","observation_id":"d2bac549-e87d-4faf-98f1-423e9b374c6e","resolution":{"observed_at":"2026-08-10T14:36:11.321304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.299862Z","title":"Separated proportional-integral lagrangian for chance constrained reinforcement learning,","venue":null,"work_id":"5e04d8a8-7de8-4c30-b95b-2c6a011c8954","year":2021},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.206495Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:f5e7f905d248b63b145d02b914f063f1a4dfa4137632dc86f51955a2278000b3","observation_id":"9586ec56-8c2c-4bd9-8de8-93f16ca8bca8","resolution":{"observed_at":"2026-08-10T14:36:11.305139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.284228Z","title":"Model- based actor-critic with chance constraint for stochastic system,","venue":null,"work_id":"b8af0038-f229-4ed9-8156-dae58996e4b1","year":2021},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.210943Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:d28e152d30b5803f003e0e1f0eea1f277cf70de184d81bd38ae9c7e5537f66b9","observation_id":"68dee65c-b556-4932-822c-bcf042e05201","resolution":{"observed_at":"2026-08-10T14:36:11.289398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.268721Z","title":"Gops: A general optimal control problem solver for autonomous driving and industrial control applications,","venue":null,"work_id":"568addf2-c682-4f50-93c4-7f2ddc7bb9c5","year":2023},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.215935Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:e524abf8394a5931e3a36a150060aad028229603eafc69425edd445d04ae1a91","observation_id":"3072ebe2-72ff-4e3a-9328-6b3c26e8080f","resolution":{"observed_at":"2026-08-10T14:36:11.273556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-10T14:36:11.251034Z","title":"Enhance generality by model-based reinforcement learning and domain ran- domization,","venue":null,"work_id":"c2ee60e5-6b23-4d7c-8ce6-e6300cc5fee8","year":2023},"citing_paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T14:36:11.220159Z"},"links":{"citing_paper":"/paper/2501.15217"},"observation_digest":"sha256:4e52a2e192c0c702a076019a623481934f02738bc8adc54554628d0ea8df7f85","observation_id":"d01f9777-6183-40de-89dc-611f6e010edf","resolution":{"observed_at":"2026-08-10T14:36:11.257771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.15217","last_updated":"2025-01-25T13:39:45Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T14:27:52.446249Z","submitted_at":"2025-01-25T13:39:45Z","title":"Predictive Lagrangian Optimization for Constrained Reinforcement Learning"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":13},"total_outbound_references":15},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2501.15217."}