{"as_of":"2026-08-09T08:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:73e9380e0c0f4f033f9afc51003c27eccf16549c45d26e81a187d1861acfb6f0","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T15:20:48.339408Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.02287/citation-record","integrity":"/paper/2606.02287/integrity","json":"/paper/2606.02287/citation-record.json","paper":"/paper/2606.02287"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Mobility trajectory generation: a survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:898b9447130e918b8c1aa97f18e1f4c146681797e596dd2a06758766f6ef3b00","observation_id":"7c977877-c486-4fcc-8e7e-71f9a4b888be","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Trajsgan: A semantic-guiding adversarial network for urban trajectory generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:3b7cc3dcc1146dc55ec90d3b0b8be8323a8b975c8990add27328cfe55300f035","observation_id":"bc2d9c18-25c9-4a66-bb7b-e75a971428d0","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"An urban trajectory data-driven approach for covid-19 simulation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:6821933bd38d3ca280edfc70fe67b7ed566739a8cc50f36c0307d8e3e7c9ad6f","observation_id":"e7dfcf6f-c12c-465a-843f-a5ff31842394","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Controltraj: Controllable trajectory generation with topology- constrained diffusion model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:f9901920aaf2104e7e414b543628e458562da1c94941803a5d6901ee1eaf63c7","observation_id":"42ecbc2d-305c-4026-a8e7-74d8ed71c494","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Difftraj: Generating gps trajectory with diffusion probabilistic model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:2fe84c0718bdab87c4a72fe450958fad34279ec0c8b43a18059038588d847b37","observation_id":"9e993e07-11dd-4090-a9bd-9c080051fe52","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Diff-rntraj: A structure-aware diffusion model for road network- constrained trajectory generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:d55ff882a83921b30ca004c1a839fb733e96b400710b5177c02c286738fd9afc","observation_id":"9ebd19bf-2888-4978-97c7-4f0322396b7b","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Trajvae: A vari- ational autoencoder model for trajectory generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:000a9e95e6f3a49c1c0544cc95b0680272d8d0f879d2318929c662929e6ddf97","observation_id":"b4f8cf83-ea7a-4f5e-8ad0-04042e8070d7","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10521","last_updated":"2020-06-14T03:04:19Z","snapshot_observed_at":"2026-07-06T09:30:23.305109Z","submitted_at":"2020-06-14T03:04:19Z","title":"LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection","version":1},"cited_work":{"arxiv_id":"2006.10521","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.10521","snapshot_observed_at":"2026-07-01T22:26:18.161911Z","title":"Lstm-trajgan: A deep learning approach to trajectory privacy protection","venue":null,"work_id":"15e34b3f-d793-4a3b-8291-f3461f4df161","year":2006},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"cited_paper":"/paper/2006.10521","citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:37e705147859e57cd9895dc0aab62608d212a78d77a81387b964ef8cf1be84a6","observation_id":"41500db1-004d-4ba3-ac98-9c3d377290a2","resolution":{"observed_at":"2026-07-01T22:26:18.163342Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Trajflow: Nation-wide pseudo gps trajectory generation with flow matching models,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:42fcf910af387be78bd0b95026d93cc80d7360b9a8f3205a9f4d0457c346e30d","observation_id":"04338ec4-c506-41c5-b7ec-eee8f3b10732","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Human trajectory forecasting in crowds: A deep learning perspective,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:38b664f5d0828b956626566577382e95283b951a6505a3e03c50a94f6533d574","observation_id":"53e1eb11-c3ee-4aa4-aa13-07d392f2709f","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Unitraj: A unified framework for scalable vehicle trajectory prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:1ee6225d968174d16999ee5730b73a91c0e46950fad02a8cec044a32ee2ebb77","observation_id":"0f85744a-b292-4a69-be8a-719210abc6f5","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Understanding individual human mobility patterns,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:de3870065703c6b3c75edc1d444a1389d5b38a261e458cc0cbc6df9954947a88","observation_id":"19999a8a-5deb-4df6-b156-ac81dcbba478","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Next place prediction using mobility markov chains,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:5759f814fb5a0c2642bf5812964f3183bba7419fa7d9f08bc4c14941617ce49e","observation_id":"2f5a89d2-2d44-4439-bbaa-2167c32f2306","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Trajgail: Generating urban vehicle tra- jectories using generative adversarial imitation learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:cff5296ff112490f87db0f47be961c6c5952b958fa95b770184690ae7559e8ff","observation_id":"9dbde1f6-6361-47c7-90b9-599338c1d47e","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"trajgans: Using generative adversarial networks for geo-privacy protection of trajectory data (vision paper),","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:190c224a94224cbeb7e40d9200b5f784664b6a5e04b36c2f9ca05fad9b77e8ae","observation_id":"795385e8-5c84-4278-8e3b-23149111fce9","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Simulating continuous-time human mobility trajectories,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:ac5ab7acafd1a1bf2d5e91a7fdc874cd9b87ac48e9e4a3817231506cfb4242bf","observation_id":"b8093391-96e6-402d-8921-3f0d10d8172e","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Activity trajectory generation via modeling spatiotemporal dynamics,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:a65e6095475cc602e4665ca1a3ae352d65fbe5b1444dcc9e458bf2be2cf827f9","observation_id":"57c20912-2e14-4c48-bcc4-d1c09a18be5f","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"A non- parametric generative model for human trajectories","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:e961f4b59596b81bffd863937379454fa3f4c45c5bf0f10c0907b542ce3f97e5","observation_id":"19e1fb4f-571d-411a-bb0d-4659a9707a8d","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T15:20:48.339408Z","title":"Generating mobility trajectories with retained data utility,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:3db42ac902a7f50e28a98ffca4e0f3686443c975993da1644a7aa99fef5725d2","observation_id":"f465d25a-4d6f-42e5-9be0-829077832301","resolution":{"observed_at":"2026-06-28T15:20:48.339408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":"1312.6114","doi":"10.2139/ssrn.4269703","metadata_source":"pith","pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Auto-Encoding Variational Bayes","venue":"stat.ML","work_id":"97d95295-30e1-42b4-bbf6-85f0fa4edb44","year":2013},"citing_paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T15:20:48.339408Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2606.02287"},"observation_digest":"sha256:bf0ab862efd76d8166e8637c2dc56dfbe87d6090d417d43ed221875ee4b6c705","observation_id":"067e79e4-a891-4de9-b1db-25fbee9ccde9","resolution":{"observed_at":"2026-07-01T22:26:18.165544Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.02287","last_updated":"2026-06-01T14:08:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T21:34:45.510710Z","submitted_at":"2026-06-01T14:08:56Z","title":"CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2606.02287."}