{"as_of":"2026-08-13T08:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:11d5d1f2d7c3eca5987ef7a0853e308789b0d6f806f54f3615445121620d81f6","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T22:43:00.819999Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2607.15656/citation-record","integrity":"/paper/2607.15656/integrity","json":"/paper/2607.15656/citation-record.json","paper":"/paper/2607.15656"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T22:42:59.784096Z","title":"An autonomous excavator system for material loading tasks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T22:42:59.784096Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:1dfc22a2815f22256e1283b60795cb5870d3cc8a1a5a24e4b1510ebf2eb840bb","observation_id":"d6c98ac6-5db0-4997-a481-8ce3f13832ee","resolution":{"observed_at":"2026-08-01T22:42:59.784096Z","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":"2026-08-01T22:42:59.836271Z","title":"A general approach for the automation of hydraulic excavator arms using reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T22:42:59.836271Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:d31dd68b5e2eea2c296e8c9f1098c1f77562e8f21ce9338a81aaae61cc586773","observation_id":"86190aa4-47e1-47d2-a0ae-b78475ed844e","resolution":{"observed_at":"2026-08-01T22:42:59.836271Z","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":"2026-08-01T22:42:59.910488Z","title":"Data-driven modeling and control for the automation of industrial machinery with limited instrumentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T22:42:59.910488Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:94d9a0eaedcd97c9aeaf688001314671a0ae0f4642fc43b427041f3eff267401","observation_id":"cb1d3a3a-9e7c-48a8-94e6-1a8117be2a65","resolution":{"observed_at":"2026-08-01T22:42:59.910488Z","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":"2026-08-01T22:43:00.007709Z","title":"Precision motion control of robotized industrial hydraulic excavators via data- driven model inversion,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.007709Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:027fa706bdec5b96adc9276e95970fb696fee5dfbdd2615c2a48a63b80cc1364","observation_id":"c74403cd-851a-453c-822a-3c8b0775931e","resolution":{"observed_at":"2026-08-01T22:43:00.007709Z","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":"2026-08-01T22:43:00.103052Z","title":"Identification and control of dynamical systems using neural networks,","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.103052Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:5655057eff930427733cc6eb0f22b40d5fd06f6b92880b95f89a5e54c5d6f0c1","observation_id":"ba7a88b2-c681-4257-9da5-72f5d98f57fd","resolution":{"observed_at":"2026-08-01T22:43:00.103052Z","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":"2026-08-01T22:43:00.172067Z","title":"Robot model identification and learning: A modern perspective,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.172067Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:e4e4835f86f7d262c6be5c837af5a0682d8641df80113fdcca660a063522120a","observation_id":"50af7e75-0ca0-4808-b167-9864edf6dc56","resolution":{"observed_at":"2026-08-01T22:43:00.172067Z","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":"2026-08-01T22:43:00.195615Z","title":"A data-driven approach for approximating non-linear dynamic systems using LSTM networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.195615Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:a632f159dea2f500e13f1f210cbe17e2574e08d1d0240a7477d8eb25d3cc2074","observation_id":"30abb597-56a6-4688-b739-513d76556950","resolution":{"observed_at":"2026-08-01T22:43:00.195615Z","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":"2026-08-01T22:43:00.249156Z","title":"LSTM-based adaptive robust nonlinear controller design of a single-axis hydraulic shaking table,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.249156Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:0662fed36f88676d6634a58d9d897b36843e5d34308fc54803a3ac34f8fd4bda","observation_id":"67111447-9101-4486-be79-0b69a2c6fac6","resolution":{"observed_at":"2026-08-01T22:43:00.249156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.13859","last_updated":"2024-11-21T05:35:27Z","snapshot_observed_at":"2026-08-12T15:45:48.697655Z","submitted_at":"2024-11-21T05:35:27Z","title":"Data-Driven Multi-step Nonlinear Model Predictive Control for Industrial Heavy Load Hydraulic Robot","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.13859","snapshot_observed_at":"2026-08-01T22:43:00.312331Z","title":"Data-driven multi-step nonlinear model predictive control for industrial heavy load hydraulic robot,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.312331Z"},"links":{"cited_paper":"/paper/2411.13859","citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:9f21c43718e79e6755c7786eaff17b051b4069a871e7edf7e580896522ec6db7","observation_id":"a521df6e-f4d7-4a4c-8ca6-0b1822edfcdb","resolution":{"observed_at":"2026-08-01T22:43:00.312331Z","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":"2026-08-01T22:43:00.375851Z","title":"Modeling weakly- instrumented excavator arm dynamics with stacked-input LSTM,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.375851Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:a50d3df0392ff944b9538ef0c931fbf3ef0bbd184e05e53e94eb2612ccbfa19b","observation_id":"6d354633-17ea-4fea-98f1-b617a4ada61a","resolution":{"observed_at":"2026-08-01T22:43:00.375851Z","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":"2026-08-01T22:43:00.430330Z","title":"Examining the simulation-to-reality gap of a wheel loader digging in deformable terrain,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.430330Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:65122262aae02ebf8ade6c89752605866ec47ff2af9956e93b04bccb17b63a30","observation_id":"85c8ff42-3966-441e-804a-77c9268e8e4e","resolution":{"observed_at":"2026-08-01T22:43:00.430330Z","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":"2026-08-01T22:43:00.494873Z","title":"A position controller for hydraulic excavators with deadtime and regenerative pipelines,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.494873Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:0f1f11a87a6eaa2c432850dc804bd2b9b73d3d8d8306f17ae846e077b5023c5a","observation_id":"8b8b40b1-16bb-4af9-b9d2-e713557bc8a2","resolution":{"observed_at":"2026-08-01T22:43:00.494873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.03977","last_updated":"2025-03-05T23:58:59Z","snapshot_observed_at":"2026-08-07T17:26:14.840343Z","submitted_at":"2025-03-05T23:58:59Z","title":"Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.03977","snapshot_observed_at":"2026-08-01T22:43:00.551396Z","title":"Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and normaliz- ing flows,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.551396Z"},"links":{"cited_paper":"/paper/2503.03977","citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:a5754d39f2629e790d7c51b2a5e2db1a5b54fed33d91690a4d988921c82d0b23","observation_id":"1ea5376a-ece7-4ab2-b75e-98be37932b22","resolution":{"observed_at":"2026-08-01T22:43:00.551396Z","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":"2026-08-01T22:43:00.612122Z","title":"Smoothing and stationarity enforcement framework for deep learning time-series forecasting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.612122Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:45bb09f2c330690c8282da272d040047013fa8f93f2134e5f37189738552b7de","observation_id":"f80c7dee-d705-46ec-8ab2-2319e2da91b0","resolution":{"observed_at":"2026-08-01T22:43:00.612122Z","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":"2026-08-01T22:43:00.669938Z","title":"An enhanced adaptive Kalman filter for multibody model observation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.669938Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:14d89f9bb2d862f7d3e429b2e391ac713cd5838bd1f1e0faa4232625385906f4","observation_id":"19681f90-cb99-4384-b87a-fe35162c1e68","resolution":{"observed_at":"2026-08-01T22:43:00.669938Z","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":"2026-08-01T22:43:00.728792Z","title":"Mathematical modelling and virtual decomposition control of heavy-duty parallel−serial hydraulic manip- ulators,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.728792Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:f9631f632eadadf71c4c1c9b5dd78425d3ed3a1b26b25b7e760aedeebae830d7","observation_id":"e393f93f-5ef4-4587-8bf8-938746d4a0a8","resolution":{"observed_at":"2026-08-01T22:43:00.728792Z","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":"2026-08-01T22:43:00.819999Z","title":"MuJoCo: A physics engine for model-based control,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T22:43:00.819999Z"},"links":{"citing_paper":"/paper/2607.15656"},"observation_digest":"sha256:26f4777dfdf8babd57e081c9ed608e15bc3b868cb3ec4dca644499ac9cf68aed","observation_id":"560e06ea-7936-4f8e-87d7-05db2cd2a78e","resolution":{"observed_at":"2026-08-01T22:43:00.819999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.15656","last_updated":"2026-07-17T06:04:08Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-06T13:08:11.422359Z","submitted_at":"2026-07-17T06:04:08Z","title":"Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":17},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2607.15656."}