{"as_of":"2026-08-13T17:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0befda547da8aad9b6eb82ced57404ddc2271906dbc7ba93f0e9af74a75152f5","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T04:27:33.694814Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.01417/citation-record","integrity":"/paper/2412.01417/integrity","json":"/paper/2412.01417/citation-record.json","paper":"/paper/2412.01417"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:33.527788Z","title":"Learning to reason with llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.527788Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:791204f7e7d10395df76fc20fafb4bee12e0e75d672ad2439cc0d17211acefb7","observation_id":"1b952f01-70ce-485f-844f-7ae6c0053235","resolution":{"observed_at":"2026-08-12T04:27:33.527788Z","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":"2026-08-12T04:27:33.533212Z","title":"Faith and fate: Limits of transformers on compositionality","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.533212Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:7d6b0abe4b09dbdfdcbcbef17d3563cce0cf65a4fabc1173b2f2183bdf0114d6","observation_id":"e7c74e89-b831-434b-a610-efb054451b35","resolution":{"observed_at":"2026-08-12T04:27:33.533212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00757","last_updated":"2024-10-08T14:34:37Z","snapshot_observed_at":"2026-08-13T04:50:29.785271Z","submitted_at":"2024-01-01T13:53:53Z","title":"LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00757","snapshot_observed_at":"2026-08-12T04:27:33.538092Z","title":"A & b== b & a: Triggering logical reasoning failures in large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.538092Z"},"links":{"cited_paper":"/paper/2401.00757","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:a595cf80c309070e27ab34339a9644f79fa25cf22fa950ad954f0c604f5d852a","observation_id":"3f93f98d-05f0-43ee-9e88-30e24ee0a716","resolution":{"observed_at":"2026-08-12T04:27:33.538092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17169","last_updated":"2024-10-13T11:08:15Z","snapshot_observed_at":"2026-08-13T04:30:58.170704Z","submitted_at":"2024-01-30T16:56:54Z","title":"Conditional and Modal Reasoning in Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17169","snapshot_observed_at":"2026-08-12T04:27:33.543555Z","title":"Conditional and modal reasoning in large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.543555Z"},"links":{"cited_paper":"/paper/2401.17169","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:7173c0d765499856987f50913febaeb0b85c912ceb8ac05d8a82d73e74f3b757","observation_id":"b96b3f47-6c40-4000-b62d-fba51ec6580b","resolution":{"observed_at":"2026-08-12T04:27:33.543555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16779","last_updated":"2025-01-14T14:26:03Z","snapshot_observed_at":"2026-08-12T23:03:03.113961Z","submitted_at":"2024-08-15T16:41:00Z","title":"Inductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16779","snapshot_observed_at":"2026-08-12T04:27:33.548838Z","title":"Inductive learning of logical theories with llms: A complexity-graded analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.548838Z"},"links":{"cited_paper":"/paper/2408.16779","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:50663e295261bdfddcf6e42bf4cef3d79a2dbfe8e5f136026c685e5f4fa80185","observation_id":"fd67d47e-bdc7-4f3f-84fd-d59615ca9845","resolution":{"observed_at":"2026-08-12T04:27:33.548838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12546","last_updated":"2024-10-07T19:07:37Z","snapshot_observed_at":"2026-08-12T23:40:09.654702Z","submitted_at":"2024-06-18T12:24:22Z","title":"Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12546","snapshot_observed_at":"2026-08-12T04:27:33.554033Z","title":"Liar, liar, logical mire: A benchmark for suppositional reasoning in large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.554033Z"},"links":{"cited_paper":"/paper/2406.12546","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:3f3cccdc1d907b975aa2ad1390262d5bc154bb1fa82ddd65dc0a6b8aa3c80969","observation_id":"503d58db-f912-4555-8245-8818d895c830","resolution":{"observed_at":"2026-08-12T04:27:33.554033Z","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":"2026-08-12T04:27:34.420706Z","title":"Llms still can’t plan; can lrms? a preliminary evaluation of openai’s o1 on planbench, 2024","venue":null,"work_id":"654f31b0-cea3-4db4-82a7-dba0c0d40925","year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.559438Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:58557441d3d229e922bfd8b002267592b0e8de4c7faceb9a2b8f465881a37626","observation_id":"d279f28a-95cc-4e03-8fe1-11ac98c8230e","resolution":{"observed_at":"2026-08-12T04:27:34.426011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.404827Z","title":"Attention is All you Need","venue":null,"work_id":"d288e89e-d631-4779-acd6-93f195306092","year":2017},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.564015Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:d850ab635f58d0c6b6d20add60ea0620b0972654e5c55ba90b113edee84e0918","observation_id":"d59e03af-e484-4af4-aac1-36a7d6e079a4","resolution":{"observed_at":"2026-08-12T04:27:34.409606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.389434Z","title":"Approximations by superpositions of a sigmoidal function","venue":null,"work_id":"e4ca03ea-a965-4238-b8b1-8437a1961846","year":1989},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.568693Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:dbdc21ca38a48170f98f37d3225975dec96233c5a5b4e3e0686cd2c756be4b4a","observation_id":"8b281bd1-3071-44e9-b299-67e2035c949b","resolution":{"observed_at":"2026-08-12T04:27:34.394330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:33.574298Z","title":"Multilayer feedforward networks are universal approximators","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.574298Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:3e036b2b0d90fb05082b8645af95c46406ee040efe164a131f930d876e7f29d8","observation_id":"d32bc398-426d-4bea-b357-bd65e661ee49","resolution":{"observed_at":"2026-08-12T04:27:33.574298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.10077","last_updated":"2020-02-25T03:12:57Z","snapshot_observed_at":"2026-08-10T18:49:22.100973Z","submitted_at":"2019-12-20T19:49:32Z","title":"Are Transformers universal approximators of sequence-to-sequence functions?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.10077","snapshot_observed_at":"2026-08-12T04:27:33.578985Z","title":"Are transformers universal approximators of sequence-to-sequence functions? arXiv preprint arXiv:1912.10077, 2019","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.578985Z"},"links":{"cited_paper":"/paper/1912.10077","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:f6096411978cbfdd6c1832547a912b68882258334a32fa6b4a9f864abe4b3099","observation_id":"cb614542-6b76-4fc1-a6f8-a55fb35bd9f5","resolution":{"observed_at":"2026-08-12T04:27:33.578985Z","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":"2026-08-12T04:27:34.362635Z","title":"Representational strengths and limitations of transformers","venue":null,"work_id":"0dc38eaa-dc50-45d7-a297-60a2519b0466","year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.584006Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:129781c48a74118f93c0afa063a7646c86ee5446fd8913cfba259ac372471599","observation_id":"58f4bba4-6865-4dba-be74-ae36a8ea0cb7","resolution":{"observed_at":"2026-08-12T04:27:34.367354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.348020Z","title":"Uni- versal transformers","venue":null,"work_id":"12638666-c55d-4bb1-8e58-2231f5642bf0","year":2019},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.588390Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:73aa798b083b71a8d55eea5ca02f1f90a61f90beb1cfebe17bda9e37537617ab","observation_id":"eb5794ee-ce39-4e09-af3a-571f3b6ef403","resolution":{"observed_at":"2026-08-12T04:27:34.352840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09286","last_updated":"2020-10-10T13:34:20Z","snapshot_observed_at":"2026-08-13T13:00:15.822430Z","submitted_at":"2020-06-16T16:27:56Z","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09286","snapshot_observed_at":"2026-08-12T04:27:33.593229Z","title":"On the computational power of transformers and its implications in sequence modeling","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.593229Z"},"links":{"cited_paper":"/paper/2006.09286","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:34a036bf1f96a280cee7603072ba247ff4c1bb0c40fcba63c6454bd7f738021a","observation_id":"9be132c5-0d99-4745-b608-25468cad540b","resolution":{"observed_at":"2026-08-12T04:27:33.593229Z","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":"2026-08-12T04:27:34.330094Z","title":"Attention is turing-complete","venue":null,"work_id":"2299c0e9-6b45-43dc-abce-8ddeb01d35ce","year":2021},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.598239Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:35497f76ee05cb672efc89a5f0dca17c328a1272f4d03e5f378dd72db3ecb6f5","observation_id":"c4b59e39-3568-4c7a-8638-08d7324d584f","resolution":{"observed_at":"2026-08-12T04:27:34.336415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00208","last_updated":"2024-09-04T11:48:04Z","snapshot_observed_at":"2026-08-13T05:35:19.333453Z","submitted_at":"2023-11-01T00:38:26Z","title":"What Formal Languages Can Transformers Express? A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00208","snapshot_observed_at":"2026-08-12T04:27:33.602672Z","title":"Transformers as recognizers of formal languages: A survey on expressivity","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.602672Z"},"links":{"cited_paper":"/paper/2311.00208","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:0d080a2caa96d0b1894ac603272507173e05938c815e8410ba705573bcf36588","observation_id":"3b5fceb4-cc64-4918-9a6b-9ac1f439a8df","resolution":{"observed_at":"2026-08-12T04:27:33.602672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01412","last_updated":"2019-12-02T15:05:24Z","snapshot_observed_at":"2026-08-07T08:07:25.563461Z","submitted_at":"2019-12-02T15:05:24Z","title":"Deep Learning for Symbolic Mathematics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01412","snapshot_observed_at":"2026-08-12T04:27:33.607454Z","title":"Deep learning for symbolic mathematics","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.607454Z"},"links":{"cited_paper":"/paper/1912.01412","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:bbac26ab8d0232ae4f14967183fe26c2ff1d3a26a8f3850e3473ad75465c9d21","observation_id":"82a08b74-7c91-45d6-a822-5596ea922997","resolution":{"observed_at":"2026-08-12T04:27:33.607454Z","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":"2026-08-12T04:27:34.277202Z","title":"End-to-end symbolic regression with transformers","venue":null,"work_id":"51c3cad8-1696-4893-b9e0-792520dc56b5","year":2022},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.611833Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:df8eb74327310cd45ee9e068069f570e4e93fb27b404f29625f23616d7c9b5d8","observation_id":"80bca968-db99-4e38-8899-4e8f54497e01","resolution":{"observed_at":"2026-08-12T04:27:34.298848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.04600","last_updated":"2022-06-28T14:15:04Z","snapshot_observed_at":"2026-08-09T03:14:40.191770Z","submitted_at":"2022-01-12T17:53:50Z","title":"Deep Symbolic Regression for Recurrent Sequences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.04600","snapshot_observed_at":"2026-08-12T04:27:33.615939Z","title":"Deep symbolic regression for recurrent sequences","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.615939Z"},"links":{"cited_paper":"/paper/2201.04600","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:abf20152a5edd0a7597cce1d60ba3a3c180cb051ebd5ea88fe0e26f329ee9589","observation_id":"425a7b6f-5d05-43e0-b566-b570ddba0f1f","resolution":{"observed_at":"2026-08-12T04:27:33.615939Z","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":"2026-08-12T04:27:33.620588Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.620588Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:6373657e7b9126df570586cbc4d49dedc7bcd1abfb17e2831e59aaa9417ff853","observation_id":"5a4eb420-6838-4845-8fb1-c535daba2b86","resolution":{"observed_at":"2026-08-12T04:27:33.620588Z","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":"2026-08-12T04:27:34.261593Z","title":"Learning cellular automaton dynamics with neural networks","venue":null,"work_id":"daafd236-eac2-4542-8e10-ee51150f83d1","year":1992},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.624567Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:03ca9d1f6b600452107e417de87b6538d4d29d1da3c3ba313d77a30f7b9c82cc","observation_id":"e0b28e52-8256-40e8-b80c-499c72dfda35","resolution":{"observed_at":"2026-08-12T04:27:34.266803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.246202Z","title":"Cellular automata as convolutional neural networks","venue":null,"work_id":"4209b39b-f183-47a3-90ba-4ad08f68d465","year":2019},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.628469Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:5ac76df69363701728ab41b0242d403e2f78869d42e1a7b80486e37e5f957072","observation_id":"68507808-6180-4647-8b6c-04885b38a388","resolution":{"observed_at":"2026-08-12T04:27:34.251254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.116021Z","title":"Generalization over different cellular automata rules learned by a deep feed-forward neural network, 2021","venue":null,"work_id":"e693fba4-019f-41d6-9112-681224cbe337","year":2021},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.632826Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:bb643d3e765b4d16191e81b283878afedff85e25c4aa95ffe6d9eb5a9c892e2e","observation_id":"99a49fd3-d140-4479-983b-58ad426a1997","resolution":{"observed_at":"2026-08-12T04:27:34.234475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.097312Z","title":"Growing neural cellular automata","venue":null,"work_id":"f891b533-cfef-46b3-8497-d9734e88e169","year":2020},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.637312Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:45335e56e3efdf0c5cc082115dc653c7c54be9ef9302bf5bd33711e5ce2e6ccf","observation_id":"3aa46dd3-679e-43e5-9fc2-06cf0d42c730","resolution":{"observed_at":"2026-08-12T04:27:34.104190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.076625Z","title":"Hierarchical neural cellular automata","venue":null,"work_id":"cc0946fe-e0e8-4448-a713-7844c250eecb","year":2023},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.641841Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:82023093bc313a49319a9be64df7846ebf76eddd6731188a0fdf942e9c642038","observation_id":"3a117c51-bcc8-41f9-bf0d-26cddf304c1a","resolution":{"observed_at":"2026-08-12T04:27:34.082576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05259","last_updated":"2024-04-08T07:49:52Z","snapshot_observed_at":"2026-08-13T00:35:15.192537Z","submitted_at":"2024-04-08T07:49:52Z","title":"Cellular automata, many-valued logic, and deep neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05259","snapshot_observed_at":"2026-08-12T04:27:33.646326Z","title":"Cellular automata, many-valued logic, and deep neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.646326Z"},"links":{"cited_paper":"/paper/2404.05259","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:1199a7d6ef66e4d69deaa1fe453eecfef1e8c091cdf60ebad8203d8798b405c8","observation_id":"4e335b31-9112-41bf-8a42-642932c34725","resolution":{"observed_at":"2026-08-12T04:27:33.646326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10497","last_updated":"2025-02-03T16:51:32Z","snapshot_observed_at":"2026-08-13T12:58:30.614810Z","submitted_at":"2023-01-25T10:17:07Z","title":"E(n)-equivariant Graph Neural Cellular Automata","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.10497","snapshot_observed_at":"2026-08-12T04:27:33.651191Z","title":"E (n)-equivariant graph neural cellular automata","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.651191Z"},"links":{"cited_paper":"/paper/2301.10497","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:7221e4e285f822f4d57fdf082c11d43dfdcf3e4095732be09d317fbca6d897b2","observation_id":"16bb56b6-4340-45c4-8c58-03c7d501bd87","resolution":{"observed_at":"2026-08-12T04:27:33.651191Z","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":"2026-08-12T04:27:34.058920Z","title":"Attention-based neural cellular au- tomata","venue":null,"work_id":"5052b023-07d4-4475-8c14-f8e770d3c16a","year":2022},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.656274Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:e0fa0df075c2591a6e50bf804013ca952788c4d4f462f65e4d301647b21743f4","observation_id":"415311c7-e3a2-4b86-a734-7fd91e3c9ec9","resolution":{"observed_at":"2026-08-12T04:27:34.064186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.040600Z","title":"Learning graph cellular automata","venue":null,"work_id":"2293a3b5-954e-47f4-a424-53f52d315161","year":2021},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.661655Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:7eda06413f8742fc8e63572bc58c90b64d205d73a5aca4ef0f40f01702c66b77","observation_id":"57145220-aeaa-4244-81c5-8f6b34dc3965","resolution":{"observed_at":"2026-08-12T04:27:34.046762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:34.019050Z","title":"Learning spatio- temporal patterns with neural cellular automata","venue":null,"work_id":"8baab7af-60c3-44f9-9de3-0986f106dba2","year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.666412Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:aebee83337eceefd5f01f5b28d98f4b91bdfbef9718348c305663c5bb8dc769f","observation_id":"1034acef-605e-4321-9473-dcea7b259bb8","resolution":{"observed_at":"2026-08-12T04:27:34.026711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:33.992518Z","title":"Learning locally interacting discrete dynamical systems: Towards data-efficient and scalable prediction","venue":null,"work_id":"028e3b37-a685-495b-80cc-4cb64afde637","year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.670918Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:6c1c6532ce05631846a2dddc7702f087f50814b83485a351df187c94867b30b8","observation_id":"6469ac0b-d071-49ad-bdfa-423ab4d8da56","resolution":{"observed_at":"2026-08-12T04:27:33.999011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:27:33.977265Z","title":"It’s hard for neural networks to learn the game of life","venue":null,"work_id":"c713d63a-146f-4a79-bef5-5ee2a5531058","year":2021},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.675913Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:1be96502db22f91afeec3d785e717f9c03872d186a826a612bd0dae26ba6f459","observation_id":"5bf69565-4864-4782-9ebc-4fd714cce07c","resolution":{"observed_at":"2026-08-12T04:27:33.981732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12778","last_updated":"2024-08-23T00:56:34Z","snapshot_observed_at":"2026-08-12T22:58:56.058149Z","submitted_at":"2024-08-23T00:56:34Z","title":"Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12778","snapshot_observed_at":"2026-08-12T04:27:33.680360Z","title":"Data-centric approach to constrained machine learning: A case study on conway’s game of life","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.680360Z"},"links":{"cited_paper":"/paper/2408.12778","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:b10b949d7a35fc2ea00090a9b2354c0c55778543b6ba5572bcf2e952702e9e65","observation_id":"168d163f-715a-4f06-a6ea-ef04eef68ddf","resolution":{"observed_at":"2026-08-12T04:27:33.680360Z","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":"2026-08-12T04:27:33.960368Z","title":"Reconstructing cellular automata rules from observations at nonconsecutive times","venue":null,"work_id":"b1acd742-79e1-40db-ac50-a846e84cea05","year":2021},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.685267Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:09f71fa51820be4ddcb81939723d7c4193d69508e7ebe657825f4cf550f6b811","observation_id":"07153d5b-8b79-439d-b9b1-15102e2977e2","resolution":{"observed_at":"2026-08-12T04:27:33.965861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12182","last_updated":"2024-10-17T16:55:02Z","snapshot_observed_at":"2026-08-12T22:52:25.996610Z","submitted_at":"2024-09-03T11:43:16Z","title":"LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12182","snapshot_observed_at":"2026-08-12T04:27:33.689916Z","title":"Lifegpt: Topology-agnostic generative pretrained transformer model for cellular automata","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.689916Z"},"links":{"cited_paper":"/paper/2409.12182","citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:3e731eb7c1cce65b1755091c787274846c206ce8db2e0e33cf323d18d96ce6cf","observation_id":"07d0d9fd-9205-4375-a605-fb56e1ba4866","resolution":{"observed_at":"2026-08-12T04:27:33.689916Z","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":"2026-08-12T04:27:33.943727Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":"0a592829-de9e-4ff3-a7c0-f65d83ec48ec","year":2022},"citing_paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T04:27:33.694814Z"},"links":{"citing_paper":"/paper/2412.01417"},"observation_digest":"sha256:e43dafb89cf6228c335776ce129c5a90be4499c85eca8620f72360125c651c07","observation_id":"97198ba9-ace4-41fc-b58c-238a5972ada1","resolution":{"observed_at":"2026-08-12T04:27:33.950026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.01417","last_updated":"2024-12-02T11:57:49Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-12T20:11:44.998047Z","submitted_at":"2024-12-02T11:57:49Z","title":"Learning Elementary Cellular Automata with Transformers"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.01417."}