{"as_of":"2026-08-22T09:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34f81f7f5fccbbbbc52b413eb26498a28d20e38209f250c8b79fb186d6c01b3c","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T23:40:16.865390Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2608.01649/citation-record","integrity":"/paper/2608.01649/integrity","json":"/paper/2608.01649/citation-record.json","paper":"/paper/2608.01649"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:40:17.220036Z","title":"Comprehensive analysis of recent leo satellite constellations: Capabilities and innovative trends,","venue":null,"work_id":"9c1d9926-c75c-4465-8a8c-e2671529f257","year":2025},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.055304Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:a087b26dbe60e356ab6da350ea65917bbf5ef9d9727e072b7c9616c5afdb7fed","observation_id":"65e7acd9-45bc-4afc-bbdc-3505d84a4fa5","resolution":{"observed_at":"2026-08-04T23:40:17.225412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.203202Z","title":"Low earth orbit satellite network routing algorithm based on graph neural networks and deep q-network,","venue":null,"work_id":"71a5945e-585f-470b-ab9e-c4a66782acc4","year":2024},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.134876Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:b7dc85ac808fd239d4588c7fa621f95e3d9e8e7e1a164313f16ce7ec879bdb7b","observation_id":"d6f3c786-139a-4fdb-bc80-c3b64ff2d7d8","resolution":{"observed_at":"2026-08-04T23:40:17.208412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.187008Z","title":"An integrated routing and data fragmentation strategy for optimizing end-to-end delay in leo satellite networks,","venue":null,"work_id":"aa835adf-d22b-438e-8062-a42a3d9d32a6","year":2025},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.239984Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:0d107d7f3fb186f904b9cd78e3e34f2e29ba06948ab1586031f7104a2f02c808","observation_id":"c95a6e30-a65d-40a9-b135-9d2e8f120959","resolution":{"observed_at":"2026-08-04T23:40:17.192278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.170922Z","title":"Reward design with language models,","venue":null,"work_id":"32977d49-8ffb-44ac-8d79-b8aa01837064","year":2023},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.325224Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:2def73e13a355b69283d3127e09defc8c677aeca6fd01b6ff948ff3aecb8ce04","observation_id":"974637e2-0ad5-4594-a59f-8f00099bc9ed","resolution":{"observed_at":"2026-08-04T23:40:17.175980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.155415Z","title":"The perils of trial-and-error reward design: misdesign through overfitting and invalid task specifications,","venue":null,"work_id":"5dda6cc5-fb51-472b-a098-1e60d2f0796a","year":2023},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.404338Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:51044a7642f48ffe4f8f8fda68fb0df7aa368806c7570265f15c850b5928892f","observation_id":"9b8a80fa-17fd-43e1-8c16-926d68e3992b","resolution":{"observed_at":"2026-08-04T23:40:17.160520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.138604Z","title":"Language to rewards for robotic skill synthesis,","venue":null,"work_id":"4ddb658d-e878-4d5a-9208-1517f5e3f487","year":2023},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.448430Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:20e6785cf0df023e63bdb59090255d636433f4aeee567c1aa36bf3c9a68d89e0","observation_id":"0292b561-4e2e-4095-9002-d0129fbcbcfa","resolution":{"observed_at":"2026-08-04T23:40:17.144253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.123207Z","title":"Text2reward: Automated dense reward function gen- eration for reinforcement learning,","venue":null,"work_id":"0ae75bce-2d1a-4def-91fe-7cc68abff3f6","year":2024},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.525791Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:c088b9ce30e020e1792751079a00692eede0c4e07d35736ecd44e99bdada1c2e","observation_id":"6fb3ab15-40ee-4849-a390-69d124ca725b","resolution":{"observed_at":"2026-08-04T23:40:17.127932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.107630Z","title":"Reward design framework based on reward components and large language models,","venue":null,"work_id":"f472708a-1880-4e68-9341-20b146352e11","year":2024},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.694155Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:e056f46e38f2ee8bce08d659b3e72de51c4ac250618893bf2b3e39a3373dd569","observation_id":"954aa18c-f2f4-4d87-a9c2-7bc567c95276","resolution":{"observed_at":"2026-08-04T23:40:17.112641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.092057Z","title":"A large language model-driven reward design framework via dynamic feedback for reinforcement learning,","venue":null,"work_id":"3ee373be-ea8e-44e4-9c87-5e20b08b0775","year":2025},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.832600Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:18065dd7041a6848c44321969b502394f21f15da57917ec53e99465f4b02023d","observation_id":"09a9e851-e56e-4ad9-bfb8-2a9121a4066b","resolution":{"observed_at":"2026-08-04T23:40:17.097055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.076063Z","title":"Autoreward: Closed-loop reward design with large language models for autonomous driving,","venue":null,"work_id":"c51a3cf3-ce78-44d2-8112-12262e26a1aa","year":2024},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:15.941147Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:a7d91c2663acc3ab8cd6223d344cc7f5c62deff2948c2a24a939b205e8bff8c6","observation_id":"f86e4190-ef3b-4af2-b88a-98d970948224","resolution":{"observed_at":"2026-08-04T23:40:17.081013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.060254Z","title":"Chatpcg: Large language model-driven reward design for procedural content generation,","venue":null,"work_id":"45ad4c0c-13db-4640-b924-874101e2325a","year":2024},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.052414Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:cc7c848a4393116ad43d0a9c49652077749fac70f51cc164d820cf8f2546a253","observation_id":"1b63e86c-652f-4868-acbf-a5db44000199","resolution":{"observed_at":"2026-08-04T23:40:17.065465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.041953Z","title":"Llm-based reward engineering for reinforcement learn- ing: A chain of thought approach,","venue":null,"work_id":"031901d0-e57c-4fa3-8bae-9a0a15aecf0e","year":2025},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.059566Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:88ca52a7901fa5d1bbe9232e4f7760db286f798bc3c8e0a9eca46d9b35674780","observation_id":"f69e2054-25a2-484d-bad1-8a997c5191df","resolution":{"observed_at":"2026-08-04T23:40:17.048226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.025173Z","title":"Large language model (llm) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities,","venue":null,"work_id":"0e7410b3-ad6a-4b64-a962-85846f45da77","year":1955},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.199844Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:873f5c1a7fa26780c6cf70810aaa041669baaf112b96b5a2f9eca37aba06237f","observation_id":"ec8d0db7-1023-463f-a2de-515dc3e0d1fc","resolution":{"observed_at":"2026-08-04T23:40:17.030204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:17.009189Z","title":"A comprehensive survey of large ai models for fu- ture communications: Foundations, applications and challenges,","venue":null,"work_id":"baa9c281-b483-4bab-a989-6f27a14be89b","year":2026},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.279223Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:96d42e0296f830a32025d034c38c11a2665d2263ace76824e841ae08dafc020c","observation_id":"7fecb655-6414-46c5-b640-d8602491a6f7","resolution":{"observed_at":"2026-08-04T23:40:17.014447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:16.991884Z","title":"Large language model-enabled reinforcement learning for wireless network optimization,","venue":null,"work_id":"3de27d3f-ad21-47ab-94f0-f109e93063d8","year":2026},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.331587Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:5b1c0d17290a078271d1e710911143274918c4c7393c21e79c6fa1fc39c40b90","observation_id":"cc39d6ad-e747-4214-b991-639947ba74e4","resolution":{"observed_at":"2026-08-04T23:40:16.997647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:16.975117Z","title":"Large language model-enhanced deep reinforcement learning for secure data collection in low-altitude economy networking,","venue":null,"work_id":"7bb19f1e-9ece-463e-a67c-51e98917ca15","year":2026},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.437277Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:6a45e509d21faa37dbf4a97783c10b01c6cf14f562702a02b2bf6e92e7bed756","observation_id":"b47210e5-3bc4-4a02-a7be-967eaff23c54","resolution":{"observed_at":"2026-08-04T23:40:16.980015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:16.957389Z","title":"Where do rewards come from,","venue":null,"work_id":"e491f7b7-001c-454a-9373-62a7e1923cb4","year":2009},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.510044Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:04f8b131bbb7fdb1347ff1b2da775c5474cdd715c7b6493e2fdb9b67f5b154d5","observation_id":"05642e42-a79d-4bf1-9d78-80743bba04f5","resolution":{"observed_at":"2026-08-04T23:40:16.963343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:16.939329Z","title":"An open source multi-agent deep reinforce- ment learning routing simulator for satellite networks,","venue":null,"work_id":"3fbfae46-12c5-4510-8b86-c58db85f7114","year":2024},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.617356Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:92e758b42f0b260b951e0a5fbd6b567387a67300dc70c78396eff72cfdeaddab","observation_id":"a762f6ab-7ef3-4765-a69a-dcfbfcc2c53b","resolution":{"observed_at":"2026-08-04T23:40:16.945444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:16.921198Z","title":"GPT-5.4 Thinking System Card,","venue":null,"work_id":"8e7041ef-550a-4643-9b23-713d6d55647c","year":2026},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.753563Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:80ffcb6eb8da14a77b12cf8219e82002dec2c90a40d7b742647ff8ae03155ce8","observation_id":"75262587-91f5-4b0a-9b50-7bc619df6915","resolution":{"observed_at":"2026-08-04T23:40:16.926813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-04T23:40:16.899820Z","title":"Claude Opus 4.6 System Card,","venue":null,"work_id":"9063dfbe-0f32-4622-bb37-73ef08f2b8df","year":2026},"citing_paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T23:40:16.865390Z"},"links":{"citing_paper":"/paper/2608.01649"},"observation_digest":"sha256:47af394d10abe48da43257c8bc302fdfb1d4b08b6c2c14e695dd56b63d780df7","observation_id":"500c358c-6943-40d7-992e-158b14670887","resolution":{"observed_at":"2026-08-04T23:40:16.907553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.01649","last_updated":"2026-08-03T03:40:09Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-14T07:15:15.297085Z","submitted_at":"2026-08-03T03:40:09Z","title":"LLM-Driven Automated Reward Design for Reinforcement Learning-Based Routing in LEO Satellite Networks"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":20},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2608.01649."}