{"as_of":"2026-08-07T15:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c73237061535395ca1af3f4397c227b7fd58dddbbd22fce892fba7c2cf4116eb","coverage":[{"denominator":70,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":70,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:52:30.425255Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T02:44:19.395548Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.17795","snapshot_observed_at":"2026-08-02T02:44:19.395548Z","title":"Lsdm: Llm- enhanced spatio-temporal diffusion model for service-level mobile traffic prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.14249","last_updated":"2026-07-15T18:07:54Z","snapshot_observed_at":"2026-08-04T17:50:03.131814Z","submitted_at":"2026-07-15T18:07:54Z","title":"MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T02:44:19.395548Z"},"links":{"cited_paper":"/paper/2507.17795","citing_paper":"/paper/2607.14249"},"observation_digest":"sha256:18bbb9e1bbcfc2071dcac3fdb7cc33e43faec7de65f15ec2890fe1fe8deeece5","observation_id":"beb09dde-006c-4f42-b535-ee9f6ea04430","resolution":{"observed_at":"2026-08-02T02:44:19.395548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.17795/citation-record","integrity":"/paper/2507.17795/integrity","json":"/paper/2507.17795/citation-record.json","paper":"/paper/2507.17795"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:52:31.023915Z","title":"Kgda: A knowledge graph driven decomposition approach for cellular traffic prediction,","venue":null,"work_id":"75bbab35-fe80-403e-9752-845bb628f3ce","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.229925Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:8da4c835f77b30ea9bde4dc480db4bcaf9361e356c712586f1dafbcd14169a92","observation_id":"4d596a77-a2e3-485f-9766-50ffd82dafee","resolution":{"observed_at":"2026-08-06T14:52:31.026952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:31.015319Z","title":"Safe-nora: Safe reinforcement learning-based mobile network resource allocation for diverse user demands,","venue":null,"work_id":"266d7695-1569-486f-acd8-0a6359b8668a","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.233173Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:3f4772736e689181608aca3656d395092ddbe696a144f84a2b211a3666daacec","observation_id":"482a3fb5-c132-4831-b07b-0bc9d0eaaa5f","resolution":{"observed_at":"2026-08-06T14:52:31.018447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:31.006884Z","title":"Dynamic channel allocation scheme based on traffic prediction in dense wireless networks,","venue":null,"work_id":"ed01ae34-9919-4a1f-b065-e588c01c0493","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.236001Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:0b69aee3d13fcc2e81a1711eb904ca20978728dd72d00d4276cea9feabfdb764","observation_id":"2f46bb2e-032d-4785-a469-4b0c5cc1f3e9","resolution":{"observed_at":"2026-08-06T14:52:31.010090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.998361Z","title":"Carbon emissions of 5g mobile networks in china,","venue":null,"work_id":"fba0ee1e-e193-4d76-b406-fd3cfbdfb41d","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.238939Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:8df8322716b01308caa321a22af7014bb5fe1c5c90a645ef738fb1a00924b1a6","observation_id":"666e001f-1f05-456f-aa5c-d8b81b6dcd5e","resolution":{"observed_at":"2026-08-06T14:52:31.001373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.989841Z","title":"Artificial intelligence for reducing the carbon emissions of 5g networks in china,","venue":null,"work_id":"52d90c28-c96c-4643-9b95-853ff4aff894","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.241767Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:5ee8e7131206330c5261baa15d9cb01d77d097c13ee49110da45d3a0635a8669","observation_id":"b3e3cef9-9c76-4d6b-a600-1fff8b0d594c","resolution":{"observed_at":"2026-08-06T14:52:30.992899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.981458Z","title":"Mobile traffic prediction from raw data using lstm networks,","venue":null,"work_id":"fce47e60-ab08-4062-9cbd-c14c6d5f7d99","year":2018},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.244539Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:db24eca58c64c917fe042db8c39936aa4551d129cb5d9174f9aa6e3ac6a9d3a7","observation_id":"c73f8125-1247-400e-8c41-d8caad9141d9","resolution":{"observed_at":"2026-08-06T14:52:30.984356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.972219Z","title":"Deeptp: An end-to-end neural network for mobile cellular traffic prediction,","venue":null,"work_id":"3c0e353b-4e21-40ae-bced-120609a403ae","year":2018},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.247606Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:e37427402d8d80fc2c10a950bc216a60beefbc4a94efc45213d391ab1215e7fa","observation_id":"36a6aa95-8cc7-4fa8-9388-11a1bff05725","resolution":{"observed_at":"2026-08-06T14:52:30.975597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.963383Z","title":"Spatial- temporal cellular traffic prediction for 5g and beyond: A graph neural networks-based approach,","venue":null,"work_id":"e0ebfd0a-c520-49c0-a9de-94ef5858300e","year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.250397Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:a9f9de25ead92118b533a9cb910d6c0b2b9420167ed423b66320be76f1b42c1b","observation_id":"c56dea9f-3691-4c49-9490-2da22ee6e6ca","resolution":{"observed_at":"2026-08-06T14:52:30.966683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.953875Z","title":"Empowering spatial knowledge graph for mobile traffic prediction,","venue":null,"work_id":"1b76e7e3-5734-408c-ad45-3919c9481bd9","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.253277Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:56d67ff2bc63a1c8871968a7289b3e6fa63ec7ae41401145e51ba6f1107003d3","observation_id":"2389ee2f-adae-4774-90c1-d1865d826bdb","resolution":{"observed_at":"2026-08-06T14:52:30.957442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.944861Z","title":"Sdgnet: A handover-aware spa- tiotemporal graph neural network for mobile traffic forecasting,","venue":null,"work_id":"a47a510e-d652-413e-99cf-9a0e5fdb59e1","year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.256022Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:f320dce6496b8ac0190fd0f6add10b8d115ef629b52588f6862d851ae4cbf95f","observation_id":"338741ad-5121-40fd-852b-7ef43d5d26f1","resolution":{"observed_at":"2026-08-06T14:52:30.948199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.935892Z","title":"To what extent we repeat ourselves? discovering daily activity patterns across mobile app usage,","venue":null,"work_id":"a089a3ba-66f6-4f53-8969-387226785d23","year":2020},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.258761Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:f93e1ac09fb3d4608d9c2bd1886125ff9dd7f3abdb6abaadef18cc4119adb6d5","observation_id":"8062db49-169f-435d-9e53-87b21d63aa53","resolution":{"observed_at":"2026-08-06T14:52:30.939112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.926751Z","title":"Atpp: A mobile app prediction system based on deep marked temporal point processes,","venue":null,"work_id":"abe262d1-a181-4d70-9152-37bf1732a51e","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.261882Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:bc0707c5727fb8d510314c299cc00ce361b4a124be7243be05b92a77a2816779","observation_id":"dcc05820-8fec-4109-8389-a47310d8abdc","resolution":{"observed_at":"2026-08-06T14:52:30.930230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.916837Z","title":"On mining mobile apps usage behavior for predicting apps usage in smartphones,","venue":null,"work_id":"52e2d466-97fe-44b1-ade0-c2e433423dfe","year":2013},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.264584Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:0f26707cfc9cb1fc30f1228137e31a0a0abb97a124dcd9b60fff8eaf9f40f9bb","observation_id":"1acd2ed6-a7cf-48b1-8b81-de4717ffb5dc","resolution":{"observed_at":"2026-08-06T14:52:30.919868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.267210Z","title":"Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.267210Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:c1bc1b4cfb5251cce39391ec3070492062f82bc97a26c04929f0354c9172492c","observation_id":"70e41f19-9a44-493e-83df-0c1c33a49955","resolution":{"observed_at":"2026-08-06T14:52:30.267210Z","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":"2026-08-06T14:52:30.269919Z","title":"Diffusion models in vision: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.269919Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:9783337817c721f1a282e7bf2b3e39eabd9bedbb85e63ec5e6bf4d7ca990ff1f","observation_id":"c8ca2f43-4af8-490e-a113-290cb9adbfc0","resolution":{"observed_at":"2026-08-06T14:52:30.269919Z","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":"2026-08-06T14:52:30.897473Z","title":"Exploiting geographical influence for collaborative point-of-interest recommendation,","venue":null,"work_id":"dfeefc36-a08e-41f3-bca2-1bf8e309d5e3","year":2011},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.272570Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:ab99e246abcd9481a089950164495e369d6edf6db51463e5c82a9f77b36eaccc","observation_id":"1629446b-3391-4029-8860-f5e70650deb8","resolution":{"observed_at":"2026-08-06T14:52:30.900540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.888672Z","title":"Netdiff: A service-guided hierarchical diffusion model for network flow trace generation,","venue":null,"work_id":"91606e66-5263-4364-a460-b6c294f90463","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.275659Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:a0532dd19e7084266acd456ee2690dae5a1b38c714b05a891d232cfaa6f32554","observation_id":"4a3b4439-7bc2-4dc4-9acc-83b101751bfe","resolution":{"observed_at":"2026-08-06T14:52:30.891924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.879684Z","title":"Cellular traffic prediction with machine learning: A survey,","venue":null,"work_id":"8be67a7b-12af-4584-868a-ebf1c4099941","year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.278371Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:24249d792c06aaf95e1ecf38bfd98c61f9592e7aabe896c4996da7e7922966c0","observation_id":"3ca901f2-a368-4106-826c-a5a05fffeed9","resolution":{"observed_at":"2026-08-06T14:52:30.883294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.870837Z","title":"Mobile traffic prediction in consumer applications: a multimodal deep learning approach,","venue":null,"work_id":"f857331b-8e75-40b0-9813-3f8b91537340","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.281175Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:6fa84b6f07afd57ddd161e1f1b72158bd5855811a2e48cdb7ced532849f52db9","observation_id":"df67170c-b2c0-42e6-baa9-5995323a35c1","resolution":{"observed_at":"2026-08-06T14:52:30.874101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.01926","last_updated":"2018-02-22T19:52:51Z","snapshot_observed_at":"2026-08-01T21:20:06.448984Z","submitted_at":"2017-07-06T18:20:59Z","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.01926","snapshot_observed_at":"2026-08-06T14:52:30.283831Z","title":"Diffusion convolutional re- current neural network: Data-driven traffic forecasting,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.283831Z"},"links":{"cited_paper":"/paper/1707.01926","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:e49a116b58a25ceec107011885e24ea5b4ce1d4521f151d372693fe1c98104a9","observation_id":"b517f8ac-3b64-43dd-b858-5709d965f989","resolution":{"observed_at":"2026-08-06T14:52:30.283831Z","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":"2026-08-06T14:52:30.862808Z","title":"Attentive crowd flow machines,","venue":null,"work_id":"d6bb96d5-fe00-4073-8e76-8ff04d1a19f5","year":2018},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.286841Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:01c5ac580266d307c53a423b21bb048978b7554fad455306750b7d6d5edb6845","observation_id":"0782b1ff-ef4b-4412-ba04-deede76065d4","resolution":{"observed_at":"2026-08-06T14:52:30.865729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.854668Z","title":"Deep spatio-temporal residual networks for citywide crowd flows prediction,","venue":null,"work_id":"e1a5756e-660b-41d9-922f-18cd211a213c","year":2017},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.289634Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:9f738b9001307185ccf126bf34fa95784498a39dc55d026dcd0d42a58220fd55","observation_id":"d6f613ad-36bd-4932-b8da-07189f4f6671","resolution":{"observed_at":"2026-08-06T14:52:30.857693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.846306Z","title":"Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning,","venue":null,"work_id":"b6b4231e-d3d9-4d97-b3f7-3f8268473afc","year":2018},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.292202Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:c8b2262f7793f8ce9e272982138eaae47249c4e545bad17f40bfae6f0c24d441","observation_id":"8d17f852-1d04-48e5-95c3-c5c980abc22b","resolution":{"observed_at":"2026-08-06T14:52:30.849415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.838055Z","title":"Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms,","venue":null,"work_id":"6d57072c-ebfd-405c-b569-8e480a519401","year":2017},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.294700Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:10ee79f102c6eddd44cf975491f1bcccc14c181bfb0a926a181bf7e4cafda937","observation_id":"d16067a4-b4fd-49c7-969e-ff168b0aa790","resolution":{"observed_at":"2026-08-06T14:52:30.841153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.829678Z","title":"Graph attention spatial- temporal network with collaborative global-local learning for citywide mobile traffic prediction,","venue":null,"work_id":"a7b2dddd-d19e-4180-a07c-97df3acddf57","year":2020},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.297376Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:83efc4c38cfea4c8d4567a5eab8facf5c313008e01b505e35b2e2b5c24bf94d0","observation_id":"3843c212-ec54-4e15-81b1-cf7d3a4e7654","resolution":{"observed_at":"2026-08-06T14:52:30.832672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.821143Z","title":"Transformer-based spatio-temporal traffic prediction for access and metro networks,","venue":null,"work_id":"f216e0a5-f144-4ce2-91fc-38219f49887d","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.299978Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:b70f9b91ab0c375e48404dbe0ce619a511f11ff435e724c20ba3dae927be8068","observation_id":"6e5e0f8f-14ae-4476-8297-f4813ce6e809","resolution":{"observed_at":"2026-08-06T14:52:30.824285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.812817Z","title":"Spatio-temporal graph transformer networks for pedestrian trajectory prediction,","venue":null,"work_id":"cc0566eb-6572-4672-be65-c574e07085e5","year":2020},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.302761Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:0ac77572ac8f1ce200386435223ba18cc8e96fb1ccd94891024738ec70e1b18a","observation_id":"dbcdb904-ad56-4d65-8278-1d562bb273dd","resolution":{"observed_at":"2026-08-06T14:52:30.815766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.804603Z","title":"Transformer based traffic flow forecasting in sdn- vanet,","venue":null,"work_id":"9ac3156a-4ac0-442d-be2f-f5dda5353f38","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.305763Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:5c371ea8e78535ef6d08cdc8e867a8d6c8a1294dece0b1b1e0bc865c914349b4","observation_id":"8b03a829-90f7-45dd-aa32-6f5be20108da","resolution":{"observed_at":"2026-08-06T14:52:30.807718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.795268Z","title":"Mobile network traffic prediction using mlp, mlpwd, and svm,","venue":null,"work_id":"e6865ccf-042e-4aaa-8d75-5f23be3c5b3c","year":2016},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.308446Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:abe9adfd1a5a92a1904c6c120c8acb1cacffe24c817f077b0628b3fcb380e75d","observation_id":"8af82261-c32c-448d-bb01-e6b804664057","resolution":{"observed_at":"2026-08-06T14:52:30.798728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.786329Z","title":"Characterization and prediction of mobile-app traffic using markov modeling,","venue":null,"work_id":"5d3bbc2d-bd9d-4d0c-a84c-9bda26b573fb","year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.311049Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:ba3e371c1ee8f48a541935291881c806bdc6b6d5a2e016b9bdb321a4b1772645","observation_id":"c74e0c4d-d2b8-4d5a-b582-815d43c95b9d","resolution":{"observed_at":"2026-08-06T14:52:30.789482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.777824Z","title":"Spatio-temporal diffusion point processes,","venue":null,"work_id":"66c52899-25ad-4aa0-bdea-f194416c1c6d","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.313641Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:025857d20f7acb10b39935a520ccd75aff3d670012de9a419ea35c22d8f69d51","observation_id":"b5f4a88b-c5ac-4134-9fe5-a99311d9d608","resolution":{"observed_at":"2026-08-06T14:52:30.780722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.768804Z","title":"Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion,","venue":null,"work_id":"d79a0e80-8ca5-42a0-8c75-2922ba5c1a1d","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.316331Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:e150cbff23d1a172209a9044d6ec06c2e4521d4aafa0f4aa984a8ea19f62da93","observation_id":"cb00cdf8-d632-4481-8d24-fb851b2fd2fd","resolution":{"observed_at":"2026-08-06T14:52:30.771936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.760223Z","title":"Network traffic prediction based on diffusion convo- lutional recurrent neural networks,","venue":null,"work_id":"47f7a501-a0bc-42bb-981e-c25f4b41e305","year":2019},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.319627Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:0992e8c0499456c72e7a795b831d315f3a474c5177fee0021e120dbfc3dacd26","observation_id":"3d34985b-d35d-4931-bda4-c30d148cc6fc","resolution":{"observed_at":"2026-08-06T14:52:30.763310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.751405Z","title":"Spatio-temporal knowledge driven diffusion model for mobile traffic generation,","venue":null,"work_id":"bdfea533-ebb0-4564-9d02-cace684d139e","year":2025},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.322278Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:019578f69c656335ee32dcecb43ec67bad3f8e5b5a07606b409aaedd4f4c2e68","observation_id":"7e58d410-a982-45d0-83da-9696e6d4afe0","resolution":{"observed_at":"2026-08-06T14:52:30.754454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.325006Z","title":"Practical gan-based synthetic ip header trace generation using netshare,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.325006Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:3bf09ef1f2e85e2f7f51135ce49b2443c8702521f62efbaa22706a6ff80252ca","observation_id":"a77fa932-f978-4148-be9c-4098829681cd","resolution":{"observed_at":"2026-08-06T14:52:30.325006Z","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":"2026-08-06T14:52:30.737578Z","title":"Mobile user traffic generation via multi-scale hierarchical gan,","venue":null,"work_id":"2814ced4-5b4e-4c1b-b06e-93c3f1e21557","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.327727Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:3efa14466ea22b119bd049fa7d934971619df77a8839c0d38ce378d277af9168","observation_id":"13e1a435-4a08-49d0-8dc4-d10a5acf3310","resolution":{"observed_at":"2026-08-06T14:52:30.740508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.729519Z","title":"Mobile data traffic prediction by exploiting time-evolving user mobility patterns,","venue":null,"work_id":"b3624f0b-e495-4604-8d7d-d2b85cb95783","year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.330608Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:f8b005eeb0b1e2200c4d8a3657f1aa742b006b494b2e02345c32f0751af6f632","observation_id":"c9d0c458-de68-489c-9816-e23eb44f25fc","resolution":{"observed_at":"2026-08-06T14:52:30.732396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.13606","last_updated":"2021-11-26T17:10:07Z","snapshot_observed_at":"2026-08-06T16:37:51.297897Z","submitted_at":"2021-11-26T17:10:07Z","title":"Conditional Image Generation with Score-Based Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.13606","snapshot_observed_at":"2026-08-06T14:52:30.333174Z","title":"Conditional image generation with score-based diffusion models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.333174Z"},"links":{"cited_paper":"/paper/2111.13606","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:f22db1d7c5d3b7a395bc38ff3506ecf685e15baa260358b9dcc462efaa6ed7d8","observation_id":"51331746-8f6f-49d4-a643-d07bfb493b30","resolution":{"observed_at":"2026-08-06T14:52:30.333174Z","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":"2026-08-06T14:52:30.720779Z","title":"scdiffusion: conditional generation of high-quality single-cell data using diffusion model,","venue":null,"work_id":"e1bee504-ee28-4745-8f44-fdea23bbc0a6","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.336101Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:c0018fa274c6de6fb4b478846f0f51042c043b1a12624166ffe81668837b5a46","observation_id":"c1de19e6-b402-4847-b098-b8534928b8e0","resolution":{"observed_at":"2026-08-06T14:52:30.724105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.711350Z","title":"Netdiffus: Network traffic generation by diffusion models through time-series imaging,","venue":null,"work_id":"971513e1-96e5-442f-9d3e-51e6c49aeceb","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.339227Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:f4dcecfa6029fd3b57651182658400afc98694542dc397670d2f51101cbccaf5","observation_id":"ef4a9845-5647-4d9c-a910-691174dbbb30","resolution":{"observed_at":"2026-08-06T14:52:30.714697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.701436Z","title":"Pcapgan: Packet capture file gen- erator by style-based generative adversarial networks,","venue":null,"work_id":"56d6552e-010a-445a-a543-e121aa7c4f90","year":2019},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.342041Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:1420f13217800cd7a56efd863ba6d201cf3a816545fbe68dd39767b748a7cfc3","observation_id":"1f8640d4-f12f-4864-a175-b55c12106288","resolution":{"observed_at":"2026-08-06T14:52:30.704880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06196","last_updated":"2025-03-23T14:51:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-09T05:37:09Z","title":"Large Language Models: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06196","snapshot_observed_at":"2026-08-06T14:52:30.344769Z","title":"Large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.344769Z"},"links":{"cited_paper":"/paper/2402.06196","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:5e1bed6835f9fd8fb17226f9336fab9fbf505d567f4518f998d63ff04cbd2057","observation_id":"81cdf536-6fe4-48eb-b57d-df9b21fbd680","resolution":{"observed_at":"2026-08-06T14:52:30.344769Z","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":"2026-08-06T14:52:30.692638Z","title":"Clip and complementary methods,","venue":null,"work_id":"556a8d58-15fc-4118-8501-45b676e46bc8","year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.347645Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:1469f178ba2a4912cd6c643deff7f83415294ce36bce4036cd75e9e5e7fe352b","observation_id":"2adfa8dd-3e6e-435d-8bee-0167cf05a8e0","resolution":{"observed_at":"2026-08-06T14:52:30.695669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.683959Z","title":"Contrastive learning of medical visual representations from paired images and text,","venue":null,"work_id":"18e260ff-5da9-46ed-b137-f6cb2b8b80c5","year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.350354Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:524fa72178d02748b7cc293204c79ca25f562d5de6484adbfc47f66da0d88578","observation_id":"3424e544-b730-4008-9aed-f869d2cb2814","resolution":{"observed_at":"2026-08-06T14:52:30.687094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.674765Z","title":"Pubmedclip: How much does clip benefit visual question answering in the medical domain?","venue":null,"work_id":"7ef581c2-e801-4235-8cfc-8edbd4fa3f12","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.352911Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:333b5c801fe95f7271b33a30cfce03388c41a0436f3b13960e04b53ab7e59feb","observation_id":"e12e45ea-e0e2-4aeb-b837-1ef066495a93","resolution":{"observed_at":"2026-08-06T14:52:30.678509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.10163","last_updated":"2022-10-18T21:06:29Z","snapshot_observed_at":"2026-08-07T02:27:07.862492Z","submitted_at":"2022-10-18T21:06:29Z","title":"MedCLIP: Contrastive Learning from Unpaired Medical Images and Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.10163","snapshot_observed_at":"2026-08-06T14:52:30.355550Z","title":"Medclip: Contrastive learning from unpaired medical images and text,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.355550Z"},"links":{"cited_paper":"/paper/2210.10163","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:a91c4f800d1d4c1ab3666c8e8704236ee8fbf0787d8b5fc94a9e408d2213d44c","observation_id":"b3a9437b-054d-4430-b2b7-d30a91b6fa42","resolution":{"observed_at":"2026-08-06T14:52:30.355550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00915","last_updated":"2025-01-08T22:58:51Z","snapshot_observed_at":"2026-07-06T14:57:39.647497Z","submitted_at":"2023-03-02T02:20:04Z","title":"BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.00915","snapshot_observed_at":"2026-08-06T14:52:30.358453Z","title":"Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.358453Z"},"links":{"cited_paper":"/paper/2303.00915","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:daa8472ff5f2692a23519a62ba5cb7be14799469f4b448f0191fc195ea483db9","observation_id":"29f1039b-8118-469f-9030-5ffbfe08f020","resolution":{"observed_at":"2026-08-06T14:52:30.358453Z","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":"2026-08-06T14:52:30.361475Z","title":"Remoteclip: A vision language foundation model for remote sensing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.361475Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:c427f3fe987af7ee9364697733c2b3c3183c8fa8eac3a5ac253fb980ee672661","observation_id":"1a150cc0-bdc6-49b6-854b-4a959281a48d","resolution":{"observed_at":"2026-08-06T14:52:30.361475Z","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":"2026-08-06T14:52:30.363998Z","title":"Diffusion models: A comprehensive survey of methods and applications,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.363998Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:0798e91994713db5d1d53874634744661bd3f26e988a0c07f78b3fc0a58849bb","observation_id":"f0ecf7bf-e081-4593-8b40-1a00576c36a0","resolution":{"observed_at":"2026-08-06T14:52:30.363998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.06383","last_updated":"2021-07-13T20:48:12Z","snapshot_observed_at":"2026-08-07T04:07:46.441741Z","submitted_at":"2021-07-13T20:48:12Z","title":"How Much Can CLIP Benefit Vision-and-Language Tasks?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.06383","snapshot_observed_at":"2026-08-06T14:52:30.367048Z","title":"How much can clip benefit vision-and-language tasks?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.367048Z"},"links":{"cited_paper":"/paper/2107.06383","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:68e18233b59bbaf513d3d19f855fbe584adc059b80f6b7dc7bc0ac6a412c9c4e","observation_id":"2ed88d33-cd88-4c23-a33b-68593143e07b","resolution":{"observed_at":"2026-08-06T14:52:30.367048Z","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":"2026-08-06T14:52:30.656618Z","title":"Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web,","venue":null,"work_id":"1e92b503-d0d8-49f1-bf87-8b2b18329d5a","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.370209Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:bbc7909e9ff89e2e10168a433281abcc5cb87aaee9d958757800bf4b5f5516c1","observation_id":"7d37b8c6-015e-4dbe-b1a9-5d88f531a5c4","resolution":{"observed_at":"2026-08-06T14:52:30.659666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.648143Z","title":"Enhancing multi- modal understanding with clip-based image-to-text transformation,","venue":null,"work_id":"cf6bf84f-7785-48dd-955f-c18ab785d3c1","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.373484Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:9a79f7b3e2e29db441aa3698f6b434d7a340ad11b529264988adadd4c719d85d","observation_id":"71d3ccca-9c03-4b40-9241-322eb84ab13b","resolution":{"observed_at":"2026-08-06T14:52:30.651255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.639843Z","title":"Forecasting long-term spatial-temporal dynamics with generative transformer networks","venue":null,"work_id":"e9b91b52-511c-48fd-9b12-6f92ab211c41","year":null},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.376274Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:8cd63a63b90742bfb15d1be72acdd0631a971332cf756898439fdc96b9b1efcd","observation_id":"ca03d061-884e-4707-9cf0-b9cbf1692876","resolution":{"observed_at":"2026-08-06T14:52:30.643053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.12218","last_updated":"2023-03-18T03:03:25Z","snapshot_observed_at":"2026-08-04T03:37:29.718089Z","submitted_at":"2021-09-24T22:11:46Z","title":"Long-Range Transformers for Dynamic Spatiotemporal Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.12218","snapshot_observed_at":"2026-08-06T14:52:30.379718Z","title":"Long-range trans- formers for dynamic spatiotemporal forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.379718Z"},"links":{"cited_paper":"/paper/2109.12218","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:a41f64cb46aaa5201bd372cb31834bb58cc65838a45fa497676ade2a699744a2","observation_id":"75019fac-709b-41b4-8f3c-5c876310c937","resolution":{"observed_at":"2026-08-06T14:52:30.379718Z","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":"2026-08-06T14:52:30.631944Z","title":"Poster: A one-size-fits-all solution for cross-technology communication via transformer,","venue":null,"work_id":"a2813cf5-d9e7-4558-9766-615aff482785","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.382553Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:fce62d16109a1fa56636a3ef9d94db070d795f7e3fd4d6481e4b95ee58602526","observation_id":"a3b9ca40-afe7-4e90-bade-4f135aacb453","resolution":{"observed_at":"2026-08-06T14:52:30.634749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.623742Z","title":"Multimodal condi- tioned diffusion model for recommendation,","venue":null,"work_id":"8df6f01b-06fd-43dd-a118-27f7d7f0186d","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.385212Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:41f71a04c956156a100838ec522170af5e92ad7211d36021f9f24b6a8cc6a538","observation_id":"faf65947-94aa-4775-a1a1-6f0745da3250","resolution":{"observed_at":"2026-08-06T14:52:30.626668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23905","last_updated":"2024-10-31T13:10:50Z","snapshot_observed_at":"2026-07-06T19:42:51.617598Z","submitted_at":"2024-10-31T13:10:50Z","title":"Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model","version":1},"cited_work":{"arxiv_id":"2410.23905","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.23905","snapshot_observed_at":"2026-08-06T14:52:30.499037Z","title":"Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model","venue":"cs.CV","work_id":"4ca86830-d6f7-48ae-a32f-ac94aec4fbbe","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.387941Z"},"links":{"cited_paper":"/paper/2410.23905","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:87345cabba95f9f2a0a39f8d0b90d7dc6aa8a39ba60716f30d55cc87c4a40c21","observation_id":"eee34581-edbb-433e-ac83-d5ea91e10ba2","resolution":{"observed_at":"2026-08-06T14:52:30.504214Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.615705Z","title":"Latent diffusion transformer for probabilistic time series forecasting,","venue":null,"work_id":"9e10968e-6737-4dbf-9d00-368f5e9e437a","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.390871Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:ff6140cae0623492ccaafd58c98883bd62f11916cfe1c05f4460f935788a833b","observation_id":"162d3657-f430-4720-bd3b-3e57cd9adc80","resolution":{"observed_at":"2026-08-06T14:52:30.618682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12376","last_updated":"2024-10-15T02:51:00Z","snapshot_observed_at":"2026-07-06T17:32:24.161667Z","submitted_at":"2024-02-19T18:59:07Z","title":"FiT: Flexible Vision Transformer for Diffusion Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12376","snapshot_observed_at":"2026-08-06T14:52:30.393598Z","title":"Fit: Flexible vision transformer for diffusion model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.393598Z"},"links":{"cited_paper":"/paper/2402.12376","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:e9fe39cfa0f350a4a576b66de310d5df696f7cccaa1550bdb89e34883918ac55","observation_id":"bc875865-23ec-4d0a-8c9c-318b9df21e4f","resolution":{"observed_at":"2026-08-06T14:52:30.393598Z","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":"2026-08-06T14:52:30.606523Z","title":"Samples: Self adaptive mining of persistent lexical snippets for classifying mobile application traffic,","venue":null,"work_id":"37431154-fa51-4ab9-a0ba-2022d8ab7c67","year":2015},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.396669Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:04c462ef4b25eeab8f7d49f47207670a69d24cadd480bc8719f5601140dc2a29","observation_id":"81b85b49-4af9-40d2-be5c-f37b607df58f","resolution":{"observed_at":"2026-08-06T14:52:30.610277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.598104Z","title":"Machine learning for interconnect network traffic forecasting: Investigation and exploitation,","venue":null,"work_id":"37f2fbc0-6456-4163-8ea8-74e1dd36cf35","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.399830Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:c975ba86c6b44f873c47f0f81a9c1987bbb2702fd1bc447b1f7f313aa09d47ba","observation_id":"e9cde03a-116d-4566-a045-860197779f33","resolution":{"observed_at":"2026-08-06T14:52:30.600979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04948","last_updated":"2024-04-02T04:39:08Z","snapshot_observed_at":"2026-08-05T15:23:02.346614Z","submitted_at":"2023-10-08T00:02:25Z","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04948","snapshot_observed_at":"2026-08-06T14:52:30.402593Z","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.402593Z"},"links":{"cited_paper":"/paper/2310.04948","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:d00d2add5d9580bc6e31ab8a62168fa59c8d7adc30dffa6c73dcb4c483d81fcc","observation_id":"0a1e5efe-ee16-46f1-b5b1-ae1ef6b27901","resolution":{"observed_at":"2026-08-06T14:52:30.402593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-04T04:31:27.172482Z","submitted_at":"2023-10-03T01:31:25Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01728","snapshot_observed_at":"2026-08-06T14:52:30.405503Z","title":"Time-llm: Time series forecasting by re- programming large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.405503Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:017bee631929e832d54828693151cf2e4d2987fca51ac68b6018d6ccbaaf3e70","observation_id":"3a0ea26e-939d-4f58-a5b3-29e553196b43","resolution":{"observed_at":"2026-08-06T14:52:30.405503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-07-31T22:45:35.561492Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-06T14:52:30.408394Z","title":"A time series is worth 64 words: Long-term forecasting with transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.408394Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:5e96f7dc8c61a02f80edba2c382b4abb31c28db26b4b10077dcc9814027fd520","observation_id":"bbafa8bb-ca8a-4068-8097-76f20feddd0e","resolution":{"observed_at":"2026-08-06T14:52:30.408394Z","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":"2026-08-06T14:52:30.590035Z","title":"Scalable diffusion models with transformers,","venue":null,"work_id":"d046fbf9-f086-4987-b843-1075d9fef671","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.411289Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:2750c51a661aa08c434fc95b1f251f9f571730beaf62c7515b9e84c5de40cfbb","observation_id":"110c11bb-d51a-4b14-82db-154309306ce1","resolution":{"observed_at":"2026-08-06T14:52:30.592837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.581524Z","title":"Csdi: Conditional score- based diffusion models for probabilistic time series imputation,","venue":null,"work_id":"787c80cc-e2f1-4bf5-a1f9-8cde6df3e9be","year":2021},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.414114Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:433945d436e5e519266c693f0260650988f2a79ddb66867b60db54a6836ab3eb","observation_id":"641a3f35-b8bd-4ea4-9cfa-709ee27144f2","resolution":{"observed_at":"2026-08-06T14:52:30.584682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.572934Z","title":"Rf-diffusion: Radio signal generation via time-frequency diffusion,","venue":null,"work_id":"ba1d7348-395e-446d-be95-943cf2f72161","year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.416855Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:74d9c673a507d09f8ee20e626e7d43e02d3e8af381b9235c2c1bbf826629479e","observation_id":"2080ae6a-e7c9-447e-ab60-5f2b65b883f4","resolution":{"observed_at":"2026-08-06T14:52:30.575965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T14:52:30.564520Z","title":"ChatGPT,","venue":null,"work_id":"86738f88-a73b-40d8-99fc-4c3f53f614a2","year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.419562Z"},"links":{"citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:63ee52849c5b0745e033699c31db242addb7dfbbf4e021229da0b1906e3315c4","observation_id":"4f3090a5-4eac-41c2-bc49-2446256dc555","resolution":{"observed_at":"2026-08-06T14:52:30.567625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-06T14:52:30.422403Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.422403Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:d16883e193119aad55a98f2535662c6cb72f19b04547f84dba97f35d6cb7d273","observation_id":"23dc3bce-dcb4-4c57-9cdc-1519d57cc4e6","resolution":{"observed_at":"2026-08-06T14:52:30.422403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-06T14:52:30.425255Z","title":"Gpt-4o system card,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T14:52:30.425255Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2507.17795"},"observation_digest":"sha256:a5c4731ad9b37b046b7662ef2c0d1cd4259c10b666926d039cee47715ca22929","observation_id":"9e3c72b1-a1a3-4727-9f68-02f88100ed1e","resolution":{"observed_at":"2026-08-06T14:52:30.425255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.17795","last_updated":"2025-07-23T14:01:16Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T14:44:07.047902Z","submitted_at":"2025-07-23T14:01:16Z","title":"LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction"},"reference_resolution":{"displayed":70,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":1,"verified_fuzzy":51},"total_outbound_references":70},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2507.17795."}