{"as_of":"2026-08-24T02:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ce1bc88b55cb4744af7cd1579d088c056e5ed8104c27c10d0e4a05bd4599ba45","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:50:12.039317Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+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/2507.09854/citation-record","integrity":"/paper/2507.09854/integrity","json":"/paper/2507.09854/citation-record.json","paper":"/paper/2507.09854"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:50:11.961980Z","title":"Blank and Steven T","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.961980Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:a5108d3db72ffcfeee99c73506201bfc7555d96b1c2e0ee14d6d9e7de6b3dd0e","observation_id":"af126a6b-0a88-443e-a31d-60f1899d6ffb","resolution":{"observed_at":"2026-08-06T17:50:11.961980Z","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":"2026-08-06T17:50:11.966845Z","title":"A framework for neurosymbolic robot action planning using large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.966845Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:d425fd97caf698d160f7a30d601f1d3bb860718dbcf1b536d223a786607d4450","observation_id":"a1dc03fa-7c63-4f5e-ac4e-ebbbceeff9ae","resolution":{"observed_at":"2026-08-06T17:50:11.966845Z","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":"2026-08-06T17:50:12.573752Z","title":"An overview of using large language models for the symbol grounding problem","venue":null,"work_id":"f5bce338-ed95-4c7b-b5f2-c2dc25c83f70","year":2023},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.971520Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:10940f7e034c20d164b9d51dab558440157c89757ce2370af5fb9612e0b4a133","observation_id":"2b4b2959-84e2-4045-b727-afff3cbf19a6","resolution":{"observed_at":"2026-08-06T17:50:12.578983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T17:50:12.558665Z","title":"Colelough and William Regli","venue":null,"work_id":"cf1a7707-58f0-4911-a5eb-dc0a39c32333","year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.976942Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:12da462bbcf8edc7f9033d820cb8506f659bdad462a87b8c459b76b6d2605ce1","observation_id":"f014746a-5e3a-4ff9-a4b2-e78245577e1e","resolution":{"observed_at":"2026-08-06T17:50:12.562675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T17:50:11.981448Z","title":"Large language models are neurosymbolic reasoners","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.981448Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:74d465a33ed238b3fd9ad06c246ddc3c99f90ac0fe3e7b8fb7284a2c5bbc6583","observation_id":"bf66adbe-31b4-4eee-82f7-4222cef4c478","resolution":{"observed_at":"2026-08-06T17:50:11.981448Z","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":"2026-08-06T17:50:12.544127Z","title":"Automated curriculum learning for neural networks","venue":null,"work_id":"60efd456-ed53-434e-9f11-5de402a6da3a","year":2017},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.986446Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:ae9b60b18e541f5a08f72ca6c4870a3f9afa5b3f81cd1a9279ea115ca4930c64","observation_id":"6011dc6d-cd0d-4934-91b5-a25ea2740279","resolution":{"observed_at":"2026-08-06T17:50:12.548453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T17:50:11.990675Z","title":"Pragmatic norms are all you need -- why the symbol grounding problem does not apply to LLM s","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.990675Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:34a49762d0fbdf0d71431377f08ded9314aa947ada2a4a12fd7128f3e8ac4a45","observation_id":"7fa04d64-c992-45b5-82fb-465b5ceb4cf1","resolution":{"observed_at":"2026-08-06T17:50:11.990675Z","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":"2026-08-06T17:50:11.994972Z","title":"The symbol grounding problem","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.994972Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:5f4017d2d107c048f4cbd8011074b725926df399c8ab6beea34bccdf814c0a9b","observation_id":"02946afa-dcd7-471f-ab1a-4c98f41e5cb8","resolution":{"observed_at":"2026-08-06T17:50:11.994972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.14115","last_updated":"2024-12-22T20:38:32Z","snapshot_observed_at":"2026-08-16T18:36:15.616604Z","submitted_at":"2021-03-25T20:13:53Z","title":"Negative Feedback System as Optimizer for Machine Learning Systems","version":2},"cited_work":{"arxiv_id":"2103.14115","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.14115","snapshot_observed_at":"2026-08-06T17:50:12.322876Z","title":"Negative Feedback System as Optimizer for Machine Learning Systems","venue":"cs.LG","work_id":"7ccb5a08-2484-4f7e-9872-577ab022595f","year":2021},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:11.999075Z"},"links":{"cited_paper":"/paper/2103.14115","citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:7bf2d23713a865647b7ec58c1ffc05a0ddf13750e224f8bb719e1f90a135c6bd","observation_id":"4ce2bedc-8b22-4f59-9c72-fea06c80d3e9","resolution":{"observed_at":"2026-08-06T17:50:12.327929Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11851","last_updated":"2019-09-26T02:33:07Z","snapshot_observed_at":"2026-08-15T02:01:52.436845Z","submitted_at":"2019-09-26T02:33:07Z","title":"Mathematical Reasoning in Latent Space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11851","snapshot_observed_at":"2026-08-06T17:50:12.003600Z","title":"Rabe, Sarah M","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.003600Z"},"links":{"cited_paper":"/paper/1909.11851","citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:310a12758977752c17e9cf88c242cbc0f743f53bb96256c462513864164b37da","observation_id":"9d819630-296c-40e2-891a-fe253758e98b","resolution":{"observed_at":"2026-08-06T17:50:12.003600Z","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":"2026-08-06T17:50:12.008546Z","title":"Krakauer","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.008546Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:c18d710939fb2f2063a4ffae4c1b668114222a8c0749c4f9ac97db15384cc7a4","observation_id":"98d1e05f-96f4-420f-acf1-1a11dab346c1","resolution":{"observed_at":"2026-08-06T17:50:12.008546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.09014","last_updated":"2023-03-16T01:04:45Z","snapshot_observed_at":"2026-08-18T04:54:46.080762Z","submitted_at":"2023-03-16T01:04:45Z","title":"ART: Automatic multi-step reasoning and tool-use for large language models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.09014","snapshot_observed_at":"2026-08-06T17:50:12.013529Z","title":"ART : Automatic multi-step reasoning and tool-use for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.013529Z"},"links":{"cited_paper":"/paper/2303.09014","citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:f2cf95a7dad72dfb27b5edf8eaf22e2a8b4acff767062127106312f734016096","observation_id":"107d2398-f4bc-4642-9c41-23e30f3107a1","resolution":{"observed_at":"2026-08-06T17:50:12.013529Z","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":"2026-08-06T17:50:12.528996Z","title":"ToolLLM : Facilitating large language models to master 16000+ real-world APIs","venue":null,"work_id":"fc5ca4ec-d280-432d-87dc-458f7a8f4b9e","year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.017895Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:65651490279e8034fc2cd6347eac9a119106e49b3da59e0778a6da0860ea3cf3","observation_id":"ff2e6f1e-4a21-4e5a-ba9d-21fa9f28d567","resolution":{"observed_at":"2026-08-06T17:50:12.533739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T17:50:12.514069Z","title":"Neurosymbolic ai for enhancing instructability in generative ai","venue":null,"work_id":"b7009e9b-bc27-4893-9afc-c13cfd6319f9","year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.022155Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:7a7ab9524e70184aecd571892d0b2aecbc89db878217bf5ece4b5944fe550da9","observation_id":"b577fa00-4967-45ae-8d1b-0954bd99b9f1","resolution":{"observed_at":"2026-08-06T17:50:12.518670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-06T17:50:12.498634Z","title":"Curriculum learning: A survey","venue":null,"work_id":"0fdbdc9a-ec02-459e-972e-1a6e7a3b2dd9","year":2021},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.026611Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:dab7435cce821ed21d8eefef949e1960ee504cfde87b060ab6db24b168382758","observation_id":"6fdc271b-d86e-48d8-b154-b81e0e84d46f","resolution":{"observed_at":"2026-08-06T17:50:12.503248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3233/nai-240729","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Wagner and Artur d'Avila Garcez","venue":"Neurosymbolic Artificial Intelligence","work_id":"56a72f73-60ba-4729-b3e2-45f018e8dc49","year":2024},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.031000Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:c58ebac76352899fd6ea9c29d1e730ae1bf2e74cb7c9b4cc4aa1817906738ae4","observation_id":"74b380d9-54de-425e-8a9c-9de4976c0c63","resolution":{"observed_at":"2026-08-06T17:50:12.095954Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/ai3020049","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:50:12.070650Z","title":"Large language models and logical reasoning","venue":null,"work_id":"64a17ae2-4703-48e6-aadc-c484cf7df493","year":2022},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.035281Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:660e57327b0a63f90dec7b970979da4db65717aaf93cbc7217f7edd62d74b19b","observation_id":"3d2cf19d-7798-41ee-880e-0efdfc42081c","resolution":{"observed_at":"2026-08-06T17:50:12.081204Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.31422","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:50:12.264849Z","title":"Neurosymbolic reinforcement learning and planning: A survey","venue":null,"work_id":"655eecb8-fa29-40a4-99be-d492f5ff71ae","year":2022},"citing_paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T17:50:12.039317Z"},"links":{"citing_paper":"/paper/2507.09854"},"observation_digest":"sha256:3495d0a25b5151f44a532ce8ae79f94ef82a33ccca1df450054c5613303ab66e","observation_id":"edb01e7e-d36d-4ac3-b711-4716f395c88c","resolution":{"observed_at":"2026-08-06T17:50:12.272387Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.09854","last_updated":"2025-07-14T01:34:05Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T06:59:47.049299Z","submitted_at":"2025-07-14T01:34:05Z","title":"Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":8,"verified_exact":3,"verified_fuzzy":6},"total_outbound_references":18},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 24 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2507.09854."}