{"as_of":"2026-08-19T14:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7467bf98fb1d152c5c7288e61cf1abafe913c41c7f7b07e33c246e036b5e74fb","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T04:49:22.328169Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2606.27861/citation-record","integrity":"/paper/2606.27861/integrity","json":"/paper/2606.27861/citation-record.json","paper":"/paper/2606.27861"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:49:22.328169Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:0fb0797d0a8262f9ca9391a671a89e598c6a1de3c32897ca0f2fcd2ce500a4ae","observation_id":"c96278a5-237c-409b-a7a7-0857c926ccfe","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Precise and dexterous robotic manipulation via human-in-the-loop reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:182f2a9c28be44cc5e7aa0e6dbe5ea205b4535356ee7c72493f042e1615b1c9f","observation_id":"100e703d-211f-4de3-ac5b-de68a88f4f42","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"On policy learning robust to irreversible events: An application to robotic in-hand manipu- lation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:22fe9eba5bd9ef8ff9cb8aae46e5d848e928e72ac8e04d2048dbb53cfa3d976c","observation_id":"534c3e2b-9eae-46ad-a973-907d07c01559","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Robot deformable object manipulation via nmpc-generated demonstrations in deep reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:12650f97494e0d788be30998de5d8c94674cb843fc9b620b46e17b662f535744","observation_id":"54b84989-4d73-4a15-b2c6-9510c86abf90","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Real-world humanoid locomotion with reinforcement learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:b8ebdededd8c291cdaa95a5ce3fec122ed5120a93b95c0c3a035671e66462150","observation_id":"b12bb180-98c1-47f8-a8c7-3698e6516b57","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Anymal parkour: Learning agile navigation for quadrupedal robots,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:ddf13175510a1dc570916177569fa7699ea443bd13f0173538a349dec4910f1a","observation_id":"59d0a74a-0d33-4f9d-aafa-88ec54fb6ddf","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Curriculum-based reinforcement learning for quadrupedal jumping: A reference-free design,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:fa940a216180e22856cd29fc05153115bc15d33c4e5d2b8bf03a122f7fb40332","observation_id":"9f5dc912-deb2-4dc2-857d-7ab6132d7f76","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Ex- plosive jumping with rigid and articulated soft quadrupeds via example guided reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:4e2827f530265b01911406d1190b25018baf8b3d0612fcd5d504989f3f34dcf9","observation_id":"a0f08ae0-9618-4fb0-9490-2f67e90c75d2","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Curriculum-enhanced rein- forcement learning for robust humanoid locomotion,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:b9e1fea186f8dd91e6273eba013a64213627ba5cd3f7ec8eaccacbd30deb5695","observation_id":"78874c25-7241-41d4-8fd2-2271e19a34e0","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Physics-informed multi-agent reinforcement learning for distributed multi-robot problems,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:3d9587e80a6e75a38a6438b960f9212ec721399e6d5f3a73edf2db02cd65791b","observation_id":"88ca1694-7db5-4e7f-b1d2-c16e6631f922","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Multi-agent reinforcement learning for connected and automated vehicles control: Recent advancements and future prospects,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:6d2a506bcd721e27ac641c5cc344d6e6491e83bc829996113bd81b46b24084db","observation_id":"ba9f2b63-9204-4619-9975-69be0d6dae75","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Altman,Constrained Markov decision processes","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:8cf07d9b1bd3e978ae81bfcf6c0e636255b8d0487f6419691c82d7c145997da6","observation_id":"88e0032d-3357-456a-be1a-47387ac7d89a","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Not only rewards but also constraints: Applications on legged robot locomotion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:05b6ad2972e503dae7e63ba43854d43508f6c814fa05744d69dd2efb4bf7079a","observation_id":"18435243-e299-4cc9-a34c-62567b18a257","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Constrained policy optimization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:8f8b9101093bb3027173c6636eafce5084b34d29f207f06216633991919dc9ab","observation_id":"647bc504-8ec5-4dc0-98a6-b94e2c5d39f6","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03152","last_updated":"2020-10-07T04:22:45Z","snapshot_observed_at":"2026-08-19T00:23:07.470458Z","submitted_at":"2020-10-07T04:22:45Z","title":"Projection-Based Constrained Policy Optimization","version":1},"cited_work":{"arxiv_id":"2010.03152","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.03152","snapshot_observed_at":"2026-07-04T08:49:42.827587Z","title":"Projection-based constrained policy optimization","venue":null,"work_id":"75762d92-0be1-48c0-853c-6ea6bcc061d7","year":2010},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"cited_paper":"/paper/2010.03152","citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:c6325f469b8fc0b8dd957843581b0cf10191cce41e19a13667c435e6254f9835","observation_id":"72aa2f1c-96ae-495b-a307-7c421392e84d","resolution":{"observed_at":"2026-06-29T19:13:53.387161Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01708","last_updated":"2019-10-03T20:15:55Z","snapshot_observed_at":"2026-08-14T05:35:42.838302Z","submitted_at":"2019-10-03T20:15:55Z","title":"Benchmarking Batch Deep Reinforcement Learning Algorithms","version":1},"cited_work":{"arxiv_id":"1910.01708","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.01708","snapshot_observed_at":"2026-07-04T19:30:07.900798Z","title":"Benchmarking Batch Deep Reinforcement Learning Algorithms","venue":"cs.LG","work_id":"399c3bf3-740c-41a8-bb6b-dfe1ea43e56d","year":2019},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"cited_paper":"/paper/1910.01708","citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:55c57c7bf991dddfe8875ea81253d7acbd48879ffa89d1e2d8ab3fb438ed9f20","observation_id":"7ee01f2c-7765-48d8-9460-9b2f934d0a60","resolution":{"observed_at":"2026-06-29T19:13:53.381758Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":"1707.06347","doi":"10.1016/j.artint.2010.12.005","metadata_source":"pith","pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proximal Policy Optimization Algorithms","venue":"cs.LG","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","year":2017},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:5454c4c99b7fae68d55915993429dd8680db6e5348bc62e8ec82dd8c45a05c41","observation_id":"3eda2d8e-03e2-43a0-9146-96d929a6e159","resolution":{"observed_at":"2026-06-29T19:13:53.384174Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:49:22.328169Z","title":"First order constrained optimization in policy space,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:c8cf04022891384ef95a2528dfb047cdfbcfaaf7134cf49b47070edbc0dce740","observation_id":"7dc201c7-0ee0-4582-8dfb-00beb06c5c64","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.11074","last_updated":"2018-12-26T11:09:40Z","snapshot_observed_at":"2026-08-14T19:10:15.765536Z","submitted_at":"2018-05-28T17:31:11Z","title":"Reward Constrained Policy Optimization","version":3},"cited_work":{"arxiv_id":"1805.11074","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.11074","snapshot_observed_at":"2026-07-04T11:39:46.348956Z","title":"Reward Constrained Policy Optimization","venue":"cs.LG","work_id":"c4fdaea7-11ae-432a-8a0c-0b650e87b855","year":2018},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"cited_paper":"/paper/1805.11074","citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:15eb25750e57acf989a4593e3ae3d118658ed3590ad3cad7a50e70b38418721e","observation_id":"9afc1fb1-e993-4bb1-9dae-da24c13f9769","resolution":{"observed_at":"2026-06-29T19:13:53.386598Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:49:22.328169Z","title":"Con- strained reinforcement learning has zero duality gap,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:2c6bf920e7133e6c2e25fbfbb95af2d8bdc65d4a2a933fe2e561c3ef02afab1e","observation_id":"1d7c10e5-4287-4122-be1c-d89bcd8f66ca","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Responsive safety in reinforce- ment learning by pid lagrangian methods,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:4e4162aff42e8035691416f46674bdc6d07cf5553f27b6653e3f58ba2010d606","observation_id":"8531a978-d2a2-480f-9d87-23b4ce260e06","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Ipo: Interior-point policy optimization under constraints,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:4ea8eee9f7d13ba5cc3c6c0ae242abeb8b56e7d30ad74091bf80d6a52255e0be","observation_id":"cea4ff49-4b14-4e9d-ae63-f36597bfc4df","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Penalty and barrier methods for constrained optimiza- tion,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:ee44db1a6014557c4c0a99f052a73708bd3827bdffb5e03c6c630d720976a6e6","observation_id":"8ba9e44d-d310-4358-9276-f5592ad50eb0","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11814","last_updated":"2022-06-17T02:39:04Z","snapshot_observed_at":"2026-08-16T16:58:11.417118Z","submitted_at":"2022-05-24T06:15:51Z","title":"Penalized Proximal Policy Optimization for Safe Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2205.11814","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.11814","snapshot_observed_at":"2026-07-04T03:09:30.121805Z","title":"Penalized proximal policy optimization for safe reinforcement learning","venue":null,"work_id":"dc417fbd-0f64-4309-bd76-f2bced4cb7a5","year":2022},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"cited_paper":"/paper/2205.11814","citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:4c2534d3a267afa116128465a5a10aef5e99d1b693228675b0b7aab3b8098bc7","observation_id":"26a9e85a-f40c-4a97-bb80-c72c70d6e9f3","resolution":{"observed_at":"2026-06-29T19:13:53.375308Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:49:22.328169Z","title":"Exploring constrained reinforcement learning algorithms for quadrupedal locomotion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:ae5e2370ca7e1035f929e7f7ae0ddd3cd3a445c21c8b35278babac794973ff36","observation_id":"ec5df937-aefc-421f-97db-be5cdf7b3d01","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Augmented proximal policy optimization for safe reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:9bd24ba7e081e21291726cc71ec91f2c72e1016d01a00eba0aca301b26634d49","observation_id":"dbcf3c6b-3fb9-4239-8b36-66f954416c54","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Approximately optimal approximate rein- forcement learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:3513972e647a9bf9c9585c40a3845a7bf89a17b6a48abf49255d5ba30d0195a7","observation_id":"68d848af-47a3-4c18-a534-b6e5d6ebf2ac","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"High- dimensional continuous control using generalized advantage estimation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:4bf637f1aeec46307b207b8ec5bf45928e3e8dce80ee2029d8ba727130133181","observation_id":"8814dd5f-b433-421f-b76f-05b7d4927a49","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:d02ceb92a93038df4337559cd09c8116f188e5179cca3ea5b648ba2c23bebca3","observation_id":"fb70ed47-0952-400b-b38d-29df6eb7c085","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Natural actor–critic algorithms,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:d16943c9d1fb2527670849686c95eb15b48236d26d047c0263928bd008c5d438","observation_id":"4f2deb89-2b88-4264-a3fc-23b74049b619","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Bhatnagar, H","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:51a7abaec941b42e7ae0a07892d12027ff0b64d08fd586abc5cb4834b0f44813","observation_id":"3a70359e-ac2f-472a-aab5-f576f9658576","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Risk- constrained reinforcement learning with percentile risk criteria,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:bb7e1a8960a2f03f7fc97b8e7458ef1e2d5ab8ec2b84637eb7cfaa3e75b9288d","observation_id":"56d6c4fe-8434-4d85-bb97-204e03a6035c","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Augmented lagrangians and applications of the proximal point algorithm in convex programming,","venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:41519a0f5202b86053aadf390f5e6f2072ca1f493cd4fcbcc64d0d17054f4c22","observation_id":"9e325e2e-63d8-4050-8a2d-ec29af5d70e9","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Orbit: A unified simulation framework for interactive robot learning environments,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:f62a8188e4969d379505b75cd6721e17033dac8b15bcb3e94c4e77ddd03ed38c","observation_id":"4442ed40-6dbd-4d5f-aa4f-189bba531dc0","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","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":"2026-06-29T04:49:22.328169Z","title":"Isaac Sim","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:8756dbec1963002d9b951430273ce3dd08e2fad40be0fc59a426aeb6aa52b6c8","observation_id":"5dd33a11-c2f8-4762-8b76-39e0c22d4353","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.10771","last_updated":"2025-09-13T01:31:43Z","snapshot_observed_at":"2026-08-18T20:08:53.214133Z","submitted_at":"2025-09-13T01:31:43Z","title":"RSL-RL: A Learning Library for Robotics Research","version":1},"cited_work":{"arxiv_id":"2509.10771","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.10771","snapshot_observed_at":"2026-07-04T19:40:06.222586Z","title":"Schwarke, M","venue":null,"work_id":"715d23f4-4767-4719-956d-ce4e005f53c5","year":2025},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"cited_paper":"/paper/2509.10771","citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:2b416e926fc06cf75b761d8c32f301489229b5b67a9d3e207a494894a16dd0f7","observation_id":"b33d0ae4-9b6a-4036-a867-60e1149ae472","resolution":{"observed_at":"2026-06-29T19:13:53.375760Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:49:22.328169Z","title":"The franka emika robot: A standard platform in robotics research [survey],","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T04:49:22.328169Z"},"links":{"citing_paper":"/paper/2606.27861"},"observation_digest":"sha256:cd7121799dc2b94df6d60332fe9b19e74cfa1ccd816d7e79d920e2f605ed813f","observation_id":"10264a73-1f7e-4f3d-adda-9af94eeef7a0","resolution":{"observed_at":"2026-06-29T04:49:22.328169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.27861","last_updated":"2026-06-26T08:59:38Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-14T09:55:43.968590Z","submitted_at":"2026-06-26T08:59:38Z","title":"PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":31,"verified_exact":5,"verified_fuzzy":0},"total_outbound_references":37},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2606.27861."}