{"as_of":"2026-08-09T12:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4c49798e1a86116914e61b30ffcae410a8e5052649ede1e820a3838ef446932e","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T21:03:10.177694Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"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/2502.04928/citation-record","integrity":"/paper/2502.04928/integrity","json":"/paper/2502.04928/citation-record.json","paper":"/paper/2502.04928"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:11.094679Z","title":"Martello and P","venue":null,"work_id":"89849959-8920-4856-b029-03451baa9adc","year":1990},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.962717Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:ef780275f28814ecb506250f32adf5ee4b083ac12ed66d1a179ba66cef624831","observation_id":"c7406da1-4a35-48b0-88e8-ff1c1594e6cc","resolution":{"observed_at":"2026-08-08T21:03:11.099293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"1411.4028","last_updated":"2014-11-14T19:57:57Z","snapshot_observed_at":"2026-07-06T04:00:38.324755Z","submitted_at":"2014-11-14T19:57:57Z","title":"A Quantum Approximate Optimization Algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.4028","snapshot_observed_at":"2026-08-08T21:03:09.968063Z","title":"A quantum approximate optimization algorithm,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.968063Z"},"links":{"cited_paper":"/paper/1411.4028","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:3147a8b899eb692a90af4cb7cd43b5f3e3142c566d59f36346b507fc28f0b130","observation_id":"44c5fca7-5b67-4fd9-b236-3ba8ab0a1fb4","resolution":{"observed_at":"2026-08-08T21:03:09.968063Z","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":"2026-08-08T21:03:11.079897Z","title":"Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem,","venue":null,"work_id":"d8e2cd2c-fdac-4d55-96ce-e227f82f0280","year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.973211Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:adbe48fb0fb375e8e587c093c93f403879bde25eca9465457a6ab57765c1f270","observation_id":"26ca0bdb-3eda-4120-9118-702e67fc44dc","resolution":{"observed_at":"2026-08-08T21:03:11.084687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2411.04979","last_updated":"2025-02-24T10:25:45Z","snapshot_observed_at":"2026-07-06T19:46:54.707852Z","submitted_at":"2024-11-07T18:51:44Z","title":"Quantum speedups in solving near-symmetric optimization problems by low-depth QAOA","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04979","snapshot_observed_at":"2026-08-08T21:03:09.983074Z","title":"Quantum speedups in solving near- symmetric optimization problems by low-depth qaoa,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.983074Z"},"links":{"cited_paper":"/paper/2411.04979","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:45de4a1a9ba3d6ec62b63db6e3ecd4b4c24a1ff3858de96bb426636224f84c01","observation_id":"822302cf-478a-4d87-9a04-aa440a440170","resolution":{"observed_at":"2026-08-08T21:03:09.983074Z","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":"2026-08-08T21:03:09.988069Z","title":"A fast quantum mechanical algorithm for database search,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.988069Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:9791b24655b84d6eb11f80fb316beda0c58615ab98a20bb15c511fe91c5c4892","observation_id":"02597a4f-ba6c-43fc-b83a-db31fcbc0293","resolution":{"observed_at":"2026-08-08T21:03:09.988069Z","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":"2026-08-08T21:03:11.048924Z","title":"Opening the black box inside grover’s algorithm,","venue":null,"work_id":"7f604469-91ab-4b4d-8b74-221228404d58","year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.992951Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:62264b252bbede603244fe54054cb8f23beb2ae4586bfc2e26db75f5256436b4","observation_id":"6d97f60c-914c-4730-b213-0ba1f2609dcb","resolution":{"observed_at":"2026-08-08T21:03:11.054088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:11.033122Z","title":"Quantum computing in the nisq era and beyond,","venue":null,"work_id":"5e61c46b-3e5b-40f3-a6d1-2d191726df94","year":2018},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.003202Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:8e850159cc6f39ddb0addd99c98564167276f4745464e4db142155825e534219","observation_id":"c167b99b-c176-47e3-ad15-531118412efa","resolution":{"observed_at":"2026-08-08T21:03:11.038206Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-08-08T21:03:09.997861Z","title":"Available: https://link.aps.org/doi/10.1103/PhysRevX","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.997861Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:c5e2411890c1941a140dff8c5f769a0fd4bf77efde11891e64f2ce4439172682","observation_id":"2eb5d988-5ba5-474a-bac0-552046320054","resolution":{"observed_at":"2026-08-08T21:03:09.997861Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-08T21:03:11.016496Z","title":"Quantum fourier transform has small entanglement,","venue":null,"work_id":"9a8f194b-62bb-440a-b80c-3dcd2b3c6817","year":2023},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.013935Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:2ee4484caf3a7fee268c898a98ec7bf25997e6d66fc578e8bb4f67f9aefcc7e7","observation_id":"63d9107b-0cb7-4569-b13a-5a2b14961cb2","resolution":{"observed_at":"2026-08-08T21:03:11.021700Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-08-08T21:03:10.008322Z","title":"A quantum-inspired approach to exploit turbulence structures,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.008322Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:ed8da6255addc9fa016ebce71ee6a6ca93afb3261baf8a39a38c913b9fa46994","observation_id":"9d75b87b-bbf7-46e7-adfa-9648e95891c4","resolution":{"observed_at":"2026-08-08T21:03:10.008322Z","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":"2026-08-08T21:03:10.023226Z","title":"Enhancing combinatorial optimization with classical and quantum generative models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.023226Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:a658580411345442433fc52e8509283740899316c3c37723f5d0c1f4a9a85dc6","observation_id":"f4c9c479-27f7-4205-ba59-716f00371a1b","resolution":{"observed_at":"2026-08-08T21:03:10.023226Z","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":"2026-08-08T21:03:10.018637Z","title":"CMA-ES/pycma on Github,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.018637Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:a203a9c0643e0628e1145aa182e28ea44451feb3d326b979ad4228ff8803eea2","observation_id":"0003a5df-910e-4b53-b2f8-58df6cda9b85","resolution":{"observed_at":"2026-08-08T21:03:10.018637Z","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":"2026-08-08T21:03:10.999482Z","title":"Cinelli, M","venue":null,"work_id":"f04e279e-2cc5-4de9-b130-4ac620d09cc1","year":2021},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.032612Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:58f0676200bd662cd8e3da4608efd53f4502acd56942a80eb1b2b24df36c02a3","observation_id":"3fedeef6-5a74-4ffe-a149-a3274e838eab","resolution":{"observed_at":"2026-08-08T21:03:11.004889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2301.12162","last_updated":"2023-05-22T14:10:27Z","snapshot_observed_at":"2026-07-06T14:45:33.278327Z","submitted_at":"2023-01-28T11:18:54Z","title":"PROTES: Probabilistic Optimization with Tensor Sampling","version":2},"cited_work":{"arxiv_id":"2301.12162","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.12162","snapshot_observed_at":"2026-08-08T21:03:10.568034Z","title":"PROTES: Probabilistic Optimization with Tensor Sampling","venue":"math.NA","work_id":"d68a40a9-77a8-45b9-a33e-cf2b591f5d1d","year":2023},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.027810Z"},"links":{"cited_paper":"/paper/2301.12162","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:d406d7e3b7818e6f424084a65f52ec47e3c212429b2f20b7bb2a1c7c0ead050e","observation_id":"4acad383-a60a-4102-9d67-9ca23a7eb1a9","resolution":{"observed_at":"2026-08-08T21:03:10.573081Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.041976Z","title":"Variational quantum algorithms,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.041976Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:ad0d456a1dde75053e16f93f1607857e8007b62a422196e54c7954c4ed0e78ce","observation_id":"3c7de65c-d542-446e-8183-20edd8d0c742","resolution":{"observed_at":"2026-08-08T21:03:10.041976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.2661","last_updated":"2014-06-10T18:58:17Z","snapshot_observed_at":"2026-07-06T03:46:00.791740Z","submitted_at":"2014-06-10T18:58:17Z","title":"Generative Adversarial Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.2661","snapshot_observed_at":"2026-08-08T21:03:10.037076Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.037076Z"},"links":{"cited_paper":"/paper/1406.2661","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:b39bcefc6610460e7c4f537194cd9a4f6671f0938aac68632e3ec380bb0e7faf","observation_id":"7814e244-e93d-49e6-bea9-899c19e14807","resolution":{"observed_at":"2026-08-08T21:03:10.037076Z","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":"2026-08-08T21:03:10.983916Z","title":"Practical overview of image classification with tensor-network quantum circuits,","venue":null,"work_id":"1895efe2-8c22-44c2-a41a-3fbc1359b501","year":2023},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.051208Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:5aeb2121c04d33a4d6a45dd2fae6fe98888f3b6e6940b016430e30c8df6bbbd6","observation_id":"9b7f872d-523e-4f4b-a1e9-ae32dd05eade","resolution":{"observed_at":"2026-08-08T21:03:10.989017Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-08-08T21:03:10.046580Z","title":"Tensor networks for complex quantum systems,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.046580Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:a6c76b7a8c84dfc80106b331b7e542c32b8509aee6e462a52299f26c23d02d67","observation_id":"87463a10-0efd-4a06-8085-ee1bdeee975e","resolution":{"observed_at":"2026-08-08T21:03:10.046580Z","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":"2026-08-08T21:03:10.952112Z","title":"Compactifai: Extreme compression of large language models using quantum-inspired tensor networks,","venue":null,"work_id":"76d73550-0a9d-4975-b520-5e07478be4dd","year":2024},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.060445Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:3e90ae4f92423c94d91ab9c33d18c78f0ecfbd538d0b849ee08019a5910e5437","observation_id":"dedc8592-e452-496c-b9a1-b31b7e455ecc","resolution":{"observed_at":"2026-08-08T21:03:10.957424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.968066Z","title":"Efficient mps representations and quantum circuits from the fourier modes of classical image data,","venue":null,"work_id":"a887cc8a-1e0f-438a-9997-6537a55e5431","year":2023},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.055917Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:9d6e98e16b5990f6f21eb9b32e99e2561144ea961d5549bfdd67433ff4bab966","observation_id":"50b8d029-fab8-4e37-bd23-c5a2bd302f1c","resolution":{"observed_at":"2026-08-08T21:03:10.972990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-08T21:03:10.074735Z","title":"The density-matrix renormalization group,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.074735Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:73f5c0e2d1046d7bf357718e49f83c5673da7570cd8ec1d1f88909147be6cc4f","observation_id":"e843aa14-f0d5-4f0f-980f-812fca7bb647","resolution":{"observed_at":"2026-08-08T21:03:10.074735Z","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":"2026-08-08T21:03:10.936286Z","title":"Unsupervised generative modeling using matrix product states,","venue":null,"work_id":"60e44cc9-f593-4d79-a46c-e27709ec1921","year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.064821Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:afcf1a9a2798d33b9354d331b21c6c2f9ad6a9bc62b18a21c1daeab68fe6d362","observation_id":"e13d9b49-2544-4572-b44a-f8c080183444","resolution":{"observed_at":"2026-08-08T21:03:10.941255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.918567Z","title":"Symmetric tensor networks for generative modeling and constrained combinatorial opti- mization,","venue":null,"work_id":"18476aab-95a4-4d58-8980-06244b73a4ef","year":2023},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.084893Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:5b4e1156f128ed7a2e693ea2c075d86c10393f9d637f38c66c7aee520cf0b871","observation_id":"29352e67-58b7-4462-b9c0-9b2975927455","resolution":{"observed_at":"2026-08-08T21:03:10.924064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2405.09005","last_updated":"2025-07-09T00:36:21Z","snapshot_observed_at":"2026-07-06T18:14:29.262608Z","submitted_at":"2024-05-15T00:13:18Z","title":"Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints","version":5},"cited_work":{"arxiv_id":"2405.09005","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.09005","snapshot_observed_at":"2026-08-08T21:03:10.422063Z","title":"Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints","venue":"math.NA","work_id":"eb3c76e7-6912-458a-aa32-ba5d99d0bef0","year":2024},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.089304Z"},"links":{"cited_paper":"/paper/2405.09005","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:a8e2a8311ba62b78eef57d9c08462c7b238924d7146b33c20296aff9e058f9b2","observation_id":"ba89ccde-f366-4727-91d4-81c689fa7ee4","resolution":{"observed_at":"2026-08-08T21:03:10.427263Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.079514Z","title":"Perfect sampling with unitary tensor networks,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.079514Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:c2fd8c98b68e2dfb1742a2818412cea5dcd85e5e5e6b262eaef72baa945d68a6","observation_id":"17c02599-e190-4b0b-b756-f323b4ede547","resolution":{"observed_at":"2026-08-08T21:03:10.079514Z","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":"2026-08-08T21:03:10.899601Z","title":"A practical introduction to tensor networks: Matrix product states and projected entangled pair states,","venue":null,"work_id":"c774e7f0-6f41-4ed7-b483-c6b1e2d7d126","year":2014},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.098970Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:f35f6904d7bfeda6681183069f9d138ced6f30346571af19ab5d23b37570e28b","observation_id":"51a9d765-2e32-4d87-9b27-f55ab2ff5da5","resolution":{"observed_at":"2026-08-08T21:03:10.905528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2305.02179","last_updated":"2023-05-03T15:19:36Z","snapshot_observed_at":"2026-07-06T15:22:47.702437Z","submitted_at":"2023-05-03T15:19:36Z","title":"Quantum Inspired Optimization for Industrial Scale Problems","version":1},"cited_work":{"arxiv_id":"2305.02179","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.02179","snapshot_observed_at":"2026-08-08T21:03:10.398067Z","title":"Quantum Inspired Optimization for Industrial Scale Problems","venue":"quant-ph","work_id":"0c32585e-c14c-4666-9358-f5a8c705d94f","year":2023},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.103451Z"},"links":{"cited_paper":"/paper/2305.02179","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:d9d7676d7716d77baac7e0589068a0f5f3634dd95f3eed2f80fecb932f6eb9de","observation_id":"4623e5b4-e95c-4bfc-aff0-cc5ffb14ae9d","resolution":{"observed_at":"2026-08-08T21:03:10.404846Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.094270Z","title":"Tensor network states and algorithms in the presence of a global u(1) symmetry,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.094270Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:93a6e734d68b91836eb6bc9e1a63499a8ce27947c4a5eaf5f3e412e732a335e0","observation_id":"22072df8-67cf-43de-b01b-d5787d3584dd","resolution":{"observed_at":"2026-08-08T21:03:10.094270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"cond-mat/0407066","last_updated":"2004-07-02T13:09:17Z","snapshot_observed_at":"2026-07-07T02:10:07.168634Z","submitted_at":"2004-07-02T13:09:17Z","title":"Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"cond-mat/0407066","snapshot_observed_at":"2026-08-08T21:03:10.113160Z","title":"Renormalization algorithms for quantum- many body systems in two and higher dimensions,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.113160Z"},"links":{"cited_paper":"/paper/cond-mat/0407066","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:15d4121ec512c0e1bb8d22dd816d42563f5ec7b59ca2ad5f3196529dfb094666","observation_id":"a78fc2ed-2c4c-4623-bdeb-06b50733b8fb","resolution":{"observed_at":"2026-08-08T21:03:10.113160Z","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":"2026-08-08T21:03:10.117972Z","title":"Classical simulation of quantum many-body systems with a tree tensor network,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.117972Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:f4127c6bcbd10ab355a2d6418f9e7c39b981ccde96ff5ee51c69e2c2e7eb9b80","observation_id":"ebfbe2ee-5a42-4881-8f1c-507b5785fdba","resolution":{"observed_at":"2026-08-08T21:03:10.117972Z","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":"2026-08-08T21:03:10.880391Z","title":"Deep generative modelling: A com- parative review of vaes, gans, normalizing flows, energy-based and autoregressive models,","venue":null,"work_id":"c1769472-ddb3-4595-8cbe-a565668013c4","year":2022},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.108623Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:7e719495502a9ff34ca6eec899880caed6e7a0f4ffa890c446c2f515ab358c3e","observation_id":"211803bb-2d1a-4fdb-bed2-f136da7cb461","resolution":{"observed_at":"2026-08-08T21:03:10.886898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.858216Z","title":"Applications of negative dimensional tensors,","venue":null,"work_id":"92933f05-67f7-4325-8fd0-796a1b645aae","year":1971},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.127167Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:3544943e5b6d7b83d4ad1fb850ce10bfd9c785526476c8bdb942153073b23240","observation_id":"fe2e42c4-5645-42f8-887c-ab886fff73e0","resolution":{"observed_at":"2026-08-08T21:03:10.863408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T21:03:10.840185Z","title":"The density-matrix renormalization group in the age of matrix product states,","venue":null,"work_id":"89c5ca2f-27d1-4f91-acdd-f2ed71fe280e","year":2011},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.131926Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:f1cb8f3d3e500aca10c9f6167eb4c9582838c91dcb8f1b0ee119f87df42347de","observation_id":"7eb2c298-594c-4fd9-84f7-6a22e0bbeb6f","resolution":{"observed_at":"2026-08-08T21:03:10.845160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-08T21:03:10.122545Z","title":"Tensor-train decomposition,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.122545Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:5e9ad4a7964e1d7bc43785356f66ac85165972ddc2d0bd0acdfdc91d90fa9c8c","observation_id":"f473dde9-6ae9-408b-87a4-75eb925b85c1","resolution":{"observed_at":"2026-08-08T21:03:10.122545Z","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":"2026-08-08T21:03:10.824195Z","title":"Supervised learning with tensor networks,","venue":null,"work_id":"209ff8b0-bc04-430b-8ec6-ebed084ab634","year":2016},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.140806Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:4011ce55850301d0e29d0e7884f29094960a425d12b1db7160faa2f95d63046b","observation_id":"59ff5008-88d6-48dc-8768-f0758d7f5543","resolution":{"observed_at":"2026-08-08T21:03:10.829099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-08T21:03:10.145188Z","title":"Density matrix renormalization group algorithms with a single center site,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.145188Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:51fb56e2222a0302c62f980bb9ede6c33aa4b920c86c50788aa3d3eb4bc8af5b","observation_id":"cfee0109-3a58-4dbd-a0d8-54c56607f4d1","resolution":{"observed_at":"2026-08-08T21:03:10.145188Z","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":"2026-08-08T21:03:10.136167Z","title":"Density-matrix algorithms for quantum renormalization groups,","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.136167Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:f1d0ddb2f565e704967e5c636006e3ad60de4670afda9898857af7486188d244","observation_id":"220bda17-244d-4db4-9589-a2058f4452c8","resolution":{"observed_at":"2026-08-08T21:03:10.136167Z","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":"2026-08-08T21:03:10.779224Z","title":"Facts, conjectures, and improve- ments for simulated annealing,","venue":null,"work_id":"80b7e286-a5bc-4fb8-8560-06aa67fbefd1","year":2002},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.158195Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:1b95fc226cad30c4c92ffb0a86c62217f8f9ca444077bca370ffd285be6baa6b","observation_id":"deb33285-4212-48d1-add9-c4af4fe96e34","resolution":{"observed_at":"2026-08-08T21:03:10.784266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2412.19780","last_updated":"2026-04-23T18:11:12Z","snapshot_observed_at":"2026-07-06T20:13:49.797517Z","submitted_at":"2024-12-27T18:22:47Z","title":"Tensor Network Estimation of Distribution Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19780","snapshot_observed_at":"2026-08-08T21:03:10.162508Z","title":"Tensor network estimation of distribution algorithms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.162508Z"},"links":{"cited_paper":"/paper/2412.19780","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:fd5c55fa4806240922e3c329dfeb969232eea3b8f56fded90caa2675766fd2d8","observation_id":"47fbe3f7-6163-4a7c-8a90-45f72fe4fab6","resolution":{"observed_at":"2026-08-08T21:03:10.162508Z","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":"2026-08-08T21:03:10.149522Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.149522Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:709150e76d560771d82e84035e02fdc4fc6e116529e8f68a812fd5b1de09c597","observation_id":"96386828-050a-4694-bbab-099afd63d1f6","resolution":{"observed_at":"2026-08-08T21:03:10.149522Z","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":"2026-08-08T21:03:10.795461Z","title":"Available: https://www.gurobi.com/documentation/ current/refman/index.html","venue":null,"work_id":"a8664b13-7304-4781-9586-260a858c2b90","year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.153834Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:fd0a0633b79f784f73c7093b5c495ba1919c73cd91821ac55a9edb6a5060101a","observation_id":"fd844aff-9419-4f0c-a814-a968add78d55","resolution":{"observed_at":"2026-08-08T21:03:10.800910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-08T21:03:10.177694Z","title":"Comb tensor networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.177694Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:da07739edbcb92497c1a860dd9fe315a4abed99b7146d93f2f2049f15e60a45e","observation_id":"e58918b8-c1de-41eb-901c-9c4215da7b61","resolution":{"observed_at":"2026-08-08T21:03:10.177694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.05775","last_updated":"2017-05-18T18:03:45Z","snapshot_observed_at":"2026-07-06T04:56:48.245701Z","submitted_at":"2016-05-18T22:20:35Z","title":"Supervised Learning with Quantum-Inspired Tensor Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.05775","snapshot_observed_at":"2026-08-08T21:03:10.167432Z","title":"Supervised learning with quantum-inspired tensor networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.167432Z"},"links":{"cited_paper":"/paper/1605.05775","citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:a1dcb601878120f865dad88e2ca546f5990906850257beb88aba7acd9200d415","observation_id":"105d308f-4aa6-409d-b71b-03f0c7dd22e0","resolution":{"observed_at":"2026-08-08T21:03:10.167432Z","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":"2026-08-08T21:03:10.761202Z","title":"Generative learning of continuous data by tensor networks,","venue":null,"work_id":"6a52106b-dc42-4816-bd87-b36d358ba2dc","year":2024},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.173019Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:a1b17028c9e4eba227fedb1abcb2c6b71ab98dea0e358729ce6f1d3419199b11","observation_id":"de79ca54-951f-4c62-b5b3-7d18b1d3eca7","resolution":{"observed_at":"2026-08-08T21:03:10.766663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-08T21:03:10.069315Z","title":"Available: https://link.aps.org/doi/10.1103/PhysRevX.8","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:10.069315Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:e7d1747115e2718cb1f64b64737741336fa88ac7ecdbc74e9a7ae7e08c77d1f8","observation_id":"98f8f532-cf04-427c-8c38-39ae33425298","resolution":{"observed_at":"2026-08-08T21:03:10.069315Z","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":"2026-08-08T21:03:11.064405Z","title":"Available: https://www.science.org/doi/abs/10.1126/ sciadv.adm6761","venue":null,"work_id":"14a3de03-c769-4db0-8955-ea4637e5fcaf","year":null},"citing_paper":{"arxiv_id":"2502.04928","last_updated":"2025-02-07T13:47:23Z","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-08T21:03:09.978374Z"},"links":{"citing_paper":"/paper/2502.04928"},"observation_digest":"sha256:90a5da4076ac99c6dcb14c0bc3bb63b0d25a7caf6b90d75e037ea9e0141e313a","observation_id":"a96ff851-e92a-4cd2-b0ec-b2b1a6a7f26d","resolution":{"observed_at":"2026-08-08T21:03:11.069257Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2502.04928","last_updated":"2025-02-07T13:47:23Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T20:55:10.652101Z","submitted_at":"2025-02-07T13:47:23Z","title":"Generative-enhanced optimization for knapsack problems: an industry-relevant study"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":5,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":3,"verified_fuzzy":16},"total_outbound_references":46},"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 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2502.04928."}