{"as_of":"2026-08-14T15:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8db315db0cc175800144b5aaf6be545bac0ea3facf94101d47bf4bf4fca51feb","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T13:56:46.054210Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T02:14:59.549191Z","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":"2026-07-03T01:17:30.876959Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"cited_work":{"arxiv_id":"2412.12650","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.12650","snapshot_observed_at":"2026-07-03T01:17:30.876959Z","title":"arXiv preprint arXiv:2412.12650 (2024)","venue":null,"work_id":"0aeca8a2-a689-4356-bab2-4667e20e9f37","year":2024},"citing_paper":{"arxiv_id":"2606.10167","last_updated":"2026-07-31T12:05:24Z","snapshot_observed_at":"2026-08-05T23:10:46.115458Z","submitted_at":"2026-06-08T20:53:11Z","title":"FlexPath: Adapting Learned Connectivity Guidance to Path Preferences","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T16:41:29.124842Z"},"links":{"cited_paper":"/paper/2412.12650","citing_paper":"/paper/2606.10167"},"observation_digest":"sha256:858c8e3971dfebcc8f7694338ce0c9a6dd388f743c3786c8b0dadd11b4858d30","observation_id":"ec59d2f6-381a-4cc1-81aa-9ff3a9f9bf1e","resolution":{"observed_at":"2026-07-03T01:17:30.878284Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12650","snapshot_observed_at":"2026-08-03T02:14:59.549191Z","title":"arXiv preprint arXiv:2412.12650 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.10167","last_updated":"2026-07-31T12:05:24Z","snapshot_observed_at":"2026-08-05T23:10:46.115458Z","submitted_at":"2026-06-08T20:53:11Z","title":"FlexPath: Adapting Learned Connectivity Guidance to Path Preferences","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T02:14:59.549191Z"},"links":{"cited_paper":"/paper/2412.12650","citing_paper":"/paper/2606.10167"},"observation_digest":"sha256:7cb7077a1395b992ff0608f7f39ca973b7aed7946d359787a3b8ea795e7bf19a","observation_id":"f1cc3219-adb5-47a3-afd5-083188c8579b","resolution":{"observed_at":"2026-08-03T02:14:59.549191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.12650/citation-record","integrity":"/paper/2412.12650/integrity","json":"/paper/2412.12650/citation-record.json","paper":"/paper/2412.12650"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:56:46.240826Z","title":"Path planning of autonomous mobile robot in comprehensive unknown environm ent using deep reinforcement learning,","venue":null,"work_id":"ebb42c15-178c-45bc-b5fc-59d3c791ca1c","year":2024},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:45.991267Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:e4d143df4e3db31995e0058b7d41fed7841831de212d501c0b346a5eedfedb64","observation_id":"cd769b6d-c0b8-4905-9455-0550f6f123eb","resolution":{"observed_at":"2026-08-11T13:56:46.244187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.232856Z","title":"Re search on path planning with the integration of adaptive a-star alg orithm and improved dynamic window approach,","venue":null,"work_id":"dd4b34a3-ce03-4068-906c-b9cafcd038fa","year":2024},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:45.994645Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:1aa6143215e306552bb27d1683edcbcbca393ef783e74beb5f8f0e40ac181063","observation_id":"911dfc40-9644-46f5-a1b0-6a0151573a7e","resolution":{"observed_at":"2026-08-11T13:56:46.235560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.225966Z","title":"Agv path planning based o n improved a-star algorithm,","venue":null,"work_id":"d44ba78c-6f15-4170-8e3b-68e8504bebee","year":2024},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:45.997687Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:a00a7684a3700562178568be44ce526a9a9f81938735a2ded3de61432b03994e","observation_id":"300d232e-8069-4f49-9bac-4a25080d26f2","resolution":{"observed_at":"2026-08-11T13:56:46.228411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.218988Z","title":"Intelligent path planning by an improved rrt algorithm wit h dual grid map,","venue":null,"work_id":"edc83637-6a93-4539-8838-0169a188b9ee","year":2024},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.000660Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:38b46b785a34270de262dd94d5ea1baf7ccbb307b95e0974e2a35b6234d585d8","observation_id":"5f6a58f7-88d7-4133-aed5-4fca6f61d9c5","resolution":{"observed_at":"2026-08-11T13:56:46.221703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.210801Z","title":"Path planning of modular robots on various terrain s using q- learning versus optimization algorithms,","venue":null,"work_id":"b72d64a0-468f-44cf-b81b-7466f52a3e07","year":2017},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.003932Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:5197e2ebdc4c9bf182f04aab1f423ba4b9d66721de029f3b9bc8466e0f4b1d25","observation_id":"6da94ad2-4dcb-46dd-beeb-b2a96e341a24","resolution":{"observed_at":"2026-08-11T13:56:46.214057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.203361Z","title":null,"venue":null,"work_id":"f18a380c-8385-41a5-9c36-303f436d14a4","year":1989},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.006949Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:7ff18aaa0b3fa20f9d9467fccfe8472326b09f088fc59151848d9a1f5f0ba42d","observation_id":"5d9ba05c-88d3-4893-a515-5062b92e6f1b","resolution":{"observed_at":"2026-08-11T13:56:46.206509Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.009757Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.009757Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:17583eee454a4cc30ef646183ee7ef5cdb18d37b6aaa0144e324ad64ac8a3b12","observation_id":"dc14d06f-d1ba-4580-bf4e-bc1af12f4d9c","resolution":{"observed_at":"2026-08-11T13:56:46.009757Z","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":"2026-08-11T13:56:46.191459Z","title":"Simu lation of sequential data: An enhanced reinforcement learning appro ach,","venue":null,"work_id":"bfe45c2d-0925-4b5c-85c4-11e6078274b9","year":2009},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.012876Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:86f578be5960aaff202ad08360d84b26adec564c1ddce3072234f552a150d28e","observation_id":"c17dd4e8-e303-430f-b215-2e67ec9ff513","resolution":{"observed_at":"2026-08-11T13:56:46.194367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.183167Z","title":"Path planning for mobile robot based on improved reinforcement learning algorithm,","venue":null,"work_id":"777ea4bf-0014-4493-947a-05458c30fd2b","year":2019},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.015456Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:1e968787f15b204472962a8738af8a2a7484f4369cb9b5f35f936fa0efe8f783","observation_id":"5d63ad05-057f-4fc5-9010-7defedfb26e8","resolution":{"observed_at":"2026-08-11T13:56:46.186571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.175599Z","title":"Efﬁciency improve ment to neural-network-driven optimal path planning via region an d guideline prediction,","venue":null,"work_id":"e416d1a5-f98e-4651-b2d1-21526344e35f","year":2024},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.018060Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:d2027a7638f676d8f64397458852cd62a4434d72cf8644a5fd7ccc599e75bb2d","observation_id":"035763c9-bb65-4840-9ec2-0dc55fff3fe3","resolution":{"observed_at":"2026-08-11T13:56:46.178522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.167694Z","title":"Lightweight ne ural path planning,","venue":null,"work_id":"74aa7648-56ed-4a5f-8fad-5f0e5274113e","year":2023},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.020628Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:1eef98276a54bb24e6f62c5095e44488208fc5a896accd49fb0036481f30b761","observation_id":"62050bd2-3d1c-4325-9b48-25a156ecfa6d","resolution":{"observed_at":"2026-08-11T13:56:46.170667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.023280Z","title":"Neural rrt*: Learning-based optimal path planning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.023280Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:7c0176f4048f820a6db0fada5d797fe00c0b43a26464cd17b4d20ae1862c0461","observation_id":"15704d30-28ee-4adb-92f7-43053164161a","resolution":{"observed_at":"2026-08-11T13:56:46.023280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.02791","last_updated":"2022-11-13T17:26:43Z","snapshot_observed_at":"2026-08-14T06:46:44.108068Z","submitted_at":"2021-06-05T04:29:16Z","title":"Motion Planning Transformers: A Motion Planning Framework for Mobile Robots","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.02791","snapshot_observed_at":"2026-08-11T13:56:46.025754Z","title":"Motion planning transformers: A motion planning fram ework for mobile robots,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.025754Z"},"links":{"cited_paper":"/paper/2106.02791","citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:c18876ba6f434b7245710e69b9dd441cf7c2bb6996fb5630cae00e62f1039ced","observation_id":"73d54946-9542-494e-97d9-74de77af85fc","resolution":{"observed_at":"2026-08-11T13:56:46.025754Z","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":"2026-08-11T13:56:46.155842Z","title":"Solving the optimal pa th planning of a mobile robot using improved q-learning,","venue":null,"work_id":"922d5bdc-e76d-4246-9807-c46a7ff863d8","year":2019},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.028677Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:555a0c15a7b4a289d1e066246bf0ff91f2cdf91267679263b34273cba2d9e37e","observation_id":"44c8057b-1008-42d6-9eed-9f9f50724ec3","resolution":{"observed_at":"2026-08-11T13:56:46.158931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.147605Z","title":"Optimal path planning approa ch based on q-learning algorithm for mobile robots,","venue":null,"work_id":"046e74dd-9c5d-414a-a9d3-aac60354acb7","year":2020},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.030703Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:845fea965927e074af4b0e897324335a46c1e1539f37cafb9862d373befb665e","observation_id":"6fa7a69b-e0e2-4b4b-ad15-15984c227ca0","resolution":{"observed_at":"2026-08-11T13:56:46.150486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.140633Z","title":"A modiﬁed q-learning pat h planning approach using distortion concept and optimizati on in dynamic environment for autonomous mobile robot,","venue":null,"work_id":"7b8d8c04-af43-416c-98ba-b50bdc668db8","year":2023},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.032647Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:2fbbe58eafe240ad70c9a1d23fbfc3646efd9db6de0ebf16a1e0533232a262eb","observation_id":"aeb4ba0b-657f-48ad-ac5a-98aaee002f22","resolution":{"observed_at":"2026-08-11T13:56:46.143104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.133257Z","title":"An opt imized q-learning algorithm for mobile robot local path planning,","venue":null,"work_id":"cab4fc52-9d34-4f43-b407-31df5d565fd2","year":2024},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.034776Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:2a6994f007382f2fe138807c160101fa2c95b1f7d88d91173b8ea4b1607bde96","observation_id":"6e8806bb-afb3-45e9-84d8-29036f445382","resolution":{"observed_at":"2026-08-11T13:56:46.136048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.124899Z","title":"Robot path planning via neural-network-driven prediction,","venue":null,"work_id":"2962db8b-d4dd-4bc2-a5a9-fb441f6fbd80","year":2021},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.036809Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:26c5191d2099387d3e2609e726763d572908eba6fd86b4f2864fbe66b6745c65","observation_id":"412ea01e-1614-48a2-bbad-ce06a7cb1d25","resolution":{"observed_at":"2026-08-11T13:56:46.128441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.115906Z","title":"Re- thinking bisenet for real-time semantic segmentation,","venue":null,"work_id":"ae8d3c7d-83c8-4e3d-ab1c-e57e002e8f76","year":2021},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.038927Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:bf6c42800fce20d6bc1c4577ff9246e636204cf6fe4d7d760a1ba3f48e613785","observation_id":"d274e834-aeaa-4976-a1a5-889fd4652f07","resolution":{"observed_at":"2026-08-11T13:56:46.119789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.041034Z","title":"Deep residual learni ng for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.041034Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:d6e40a3398942e58b73353636f56ecb33332c40635521a4a51285c31c0e11dc0","observation_id":"05003c01-d50f-4427-b810-8d2d4b0ab57e","resolution":{"observed_at":"2026-08-11T13:56:46.041034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02681","last_updated":"2022-04-06T09:02:41Z","snapshot_observed_at":"2026-08-13T16:07:45.685796Z","submitted_at":"2022-04-06T09:02:41Z","title":"PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02681","snapshot_observed_at":"2026-08-11T13:56:46.043083Z","title":"Pp-liteseg: A superior real-time semantic segmentation model,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.043083Z"},"links":{"cited_paper":"/paper/2204.02681","citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:a25fcf109847347ef8dec8c01bfb080c13e230133ede4f18a75bb31e1a5eeff4","observation_id":"20cc1c91-20c7-461d-a0f6-df008325edfd","resolution":{"observed_at":"2026-08-11T13:56:46.043083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:56:46.045910Z","title":"Pyramid scene parsing network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.045910Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:64e809bb118995209a5fb938c402ba08603d012aa16ea04cb380fe930147b20d","observation_id":"1bd533b5-f55f-4b53-a36f-4d30effb7ec7","resolution":{"observed_at":"2026-08-11T13:56:46.045910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:56:46.048639Z","title":"U-net: Convol utional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.048639Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:477e4c1ab6ec3204b246d984bc531185f388f68c8df8946bfc03214a47eb8e51","observation_id":"b9441dd8-19c6-43e8-b4f4-d83b36a79c5f","resolution":{"observed_at":"2026-08-11T13:56:46.048639Z","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":"2026-08-11T13:56:46.095391Z","title":"A novel e nergy management strategy based on dual reward function q-learni ng for fuel cell hybrid electric vehicle,","venue":null,"work_id":"7cadbdee-15b4-433f-b334-d395371e3257","year":2021},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.051718Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:a7083c00f2a50bec9679410cd877794298e0847c3629aa98c56e202de7fffcf6","observation_id":"118d51c6-d56f-4af5-bab3-bc65fa595244","resolution":{"observed_at":"2026-08-11T13:56:46.098463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-11T13:56:46.085331Z","title":"Modiﬁed q-learn ing with distance metric and virtual target on path planning of mobil e robot,","venue":null,"work_id":"baecca63-856b-4d4a-a7e3-ddaf32fa1d93","year":2022},"citing_paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T13:56:46.054210Z"},"links":{"citing_paper":"/paper/2412.12650"},"observation_digest":"sha256:b117ec46880982629e5e53250cf7d217d1166ce0b6a87d6b227644f44ae6a8ec","observation_id":"c0f76316-5f74-46ad-989f-7a6802095cd1","resolution":{"observed_at":"2026-08-11T13:56:46.090202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.12650","last_updated":"2024-12-17T08:19:40Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-14T11:10:57.472120Z","submitted_at":"2024-12-17T08:19:40Z","title":"Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":25},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:2412.12650."}