{"as_of":"2026-08-14T09:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a06eb191f1799fed5f4ca3d357647f78acc09aafa74facbedd517f020ff7f4ae","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T23:58:34.995892Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T13:59:51.927035Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.15196","last_updated":"2025-06-24T14:48:12Z","snapshot_observed_at":"2026-08-06T23:59:42.057407Z","submitted_at":"2025-06-18T07:20:01Z","title":"HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges","version":2},"cited_work":{"arxiv_id":"2506.15196","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.15196","snapshot_observed_at":"2026-07-04T13:59:51.927035Z","title":"arXiv preprint arXiv:2506.15196 (2025)","venue":null,"work_id":"e545b85d-261f-4a82-97b7-6a0f2ee5670a","year":2025},"citing_paper":{"arxiv_id":"2605.09186","last_updated":"2026-05-09T21:53:28Z","snapshot_observed_at":"2026-08-14T01:13:51.607597Z","submitted_at":"2026-05-09T21:53:28Z","title":"Agentic MIP Research: Accelerated Constraint Handler Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-12T02:12:27.170669Z"},"links":{"cited_paper":"/paper/2506.15196","citing_paper":"/paper/2605.09186"},"observation_digest":"sha256:ca2c1c440cb5775cf85f5554a6c31bd214c73f1c1a31baffdbb0596eb7098a6f","observation_id":"246d992a-2ad3-4ab3-b631-c9676a72a028","resolution":{"observed_at":"2026-05-12T02:16:16.325929Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15196","last_updated":"2025-06-24T14:48:12Z","snapshot_observed_at":"2026-08-06T23:59:42.057407Z","submitted_at":"2025-06-18T07:20:01Z","title":"HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges","version":2},"cited_work":{"arxiv_id":"2506.15196","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.15196","snapshot_observed_at":"2026-07-04T13:59:51.927035Z","title":"arXiv preprint arXiv:2506.15196 (2025)","venue":null,"work_id":"e545b85d-261f-4a82-97b7-6a0f2ee5670a","year":2025},"citing_paper":{"arxiv_id":"2605.20849","last_updated":"2026-05-20T07:40:05Z","snapshot_observed_at":"2026-08-14T06:24:24.052688Z","submitted_at":"2026-05-20T07:40:05Z","title":"Large Language Models for Operations Research: A Comprehensive Survey","version":1},"reference_index":124,"source":"pdf_text","source_observed_at":"2026-05-21T03:56:29.983335Z"},"links":{"cited_paper":"/paper/2506.15196","citing_paper":"/paper/2605.20849"},"observation_digest":"sha256:8f60e7c675a447ab52629b4f8dafef714e4a3f5099956580998c3d67784d50b9","observation_id":"aae8baaf-9fb6-4dfd-aae6-ded229d65982","resolution":{"observed_at":"2026-05-21T03:59:32.542518Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15196","last_updated":"2025-06-24T14:48:12Z","snapshot_observed_at":"2026-08-06T23:59:42.057407Z","submitted_at":"2025-06-18T07:20:01Z","title":"HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges","version":2},"cited_work":{"arxiv_id":"2506.15196","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.15196","snapshot_observed_at":"2026-07-04T13:59:51.927035Z","title":"arXiv preprint arXiv:2506.15196 (2025)","venue":null,"work_id":"e545b85d-261f-4a82-97b7-6a0f2ee5670a","year":2025},"citing_paper":{"arxiv_id":"2606.27369","last_updated":"2026-06-25T17:59:36Z","snapshot_observed_at":"2026-08-14T02:54:31.610293Z","submitted_at":"2026-06-25T17:59:36Z","title":"Reinforcement Learning without Ground-Truth Solutions can Improve LLMs","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-06-26T04:47:47.691913Z"},"links":{"cited_paper":"/paper/2506.15196","citing_paper":"/paper/2606.27369"},"observation_digest":"sha256:61d0e572a4186efb0585a38a845e2b72e28e58723b3261509110f58774d52f47","observation_id":"1d4b51d8-043c-4c0b-96f5-c9dfa9934226","resolution":{"observed_at":"2026-07-04T13:59:51.928373Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15196","last_updated":"2025-06-24T14:48:12Z","snapshot_observed_at":"2026-08-06T23:59:42.057407Z","submitted_at":"2025-06-18T07:20:01Z","title":"HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges","version":2},"cited_work":{"arxiv_id":"2506.15196","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.15196","snapshot_observed_at":"2026-07-04T13:59:51.927035Z","title":"arXiv preprint arXiv:2506.15196 (2025)","venue":null,"work_id":"e545b85d-261f-4a82-97b7-6a0f2ee5670a","year":2025},"citing_paper":{"arxiv_id":"2607.00604","last_updated":"2026-07-01T08:30:20Z","snapshot_observed_at":"2026-07-07T00:06:14.968413Z","submitted_at":"2026-07-01T08:30:20Z","title":"Vehicle Routing Problem Meets Large Language Models: An Overview and Perspectives","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-02T08:45:04.024510Z"},"links":{"cited_paper":"/paper/2506.15196","citing_paper":"/paper/2607.00604"},"observation_digest":"sha256:56dceb6f2a75d234a934a0a1ca7872d17251611595c40010f775e72e744bc522","observation_id":"04d41dc0-3c39-47b1-be74-97c226c0c6a0","resolution":{"observed_at":"2026-07-02T08:46:48.572147Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15196","last_updated":"2025-06-24T14:48:12Z","snapshot_observed_at":"2026-08-06T23:59:42.057407Z","submitted_at":"2025-06-18T07:20:01Z","title":"HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.15196","snapshot_observed_at":"2026-07-31T23:58:34.995892Z","title":"arXiv preprint arXiv:2506.15196 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23286","last_updated":"2026-07-25T16:45:00Z","snapshot_observed_at":"2026-08-13T06:51:51.859367Z","submitted_at":"2026-07-25T16:45:00Z","title":"TopoFE: topology-aware LLM-guided Automated Feature Engineering","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-07-31T23:58:34.995892Z"},"links":{"cited_paper":"/paper/2506.15196","citing_paper":"/paper/2607.23286"},"observation_digest":"sha256:ce16765565235b327344316951f950bf13dd7096e0afc99f6b1be241b59928a1","observation_id":"ed80e201-27da-44de-949d-3e3872e8b77b","resolution":{"observed_at":"2026-07-31T23:58:34.995892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.15196/citation-record","integrity":"/paper/2506.15196/integrity","json":"/paper/2506.15196/citation-record.json","paper":"/paper/2506.15196"},"outbound":[],"paper":{"arxiv_id":"2506.15196","last_updated":"2025-06-24T14:48:12Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-06T23:59:42.057407Z","submitted_at":"2025-06-18T07:20:01Z","title":"HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2506.15196."}