{"as_of":"2026-08-21T05:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:da8e952046d4034168573ea0f386f64ec982c662cd2ca928b64ca86d639a20e4","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:21:02.060803Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T20:57:10.558525Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.19531","snapshot_observed_at":"2026-08-03T20:57:10.558525Z","title":"Minimalist softmax attention provably learns constrained boolean functions, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.17852","last_updated":"2026-08-06T09:30:47Z","snapshot_observed_at":"2026-08-09T23:09:22.568411Z","submitted_at":"2025-11-22T00:38:43Z","title":"Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-03T20:57:10.558525Z"},"links":{"cited_paper":"/paper/2505.19531","citing_paper":"/paper/2511.17852"},"observation_digest":"sha256:38f151cd6bca701269439bd1e158dc288029283dfd179c32a66d1e94b4db4c20","observation_id":"11a05383-c11a-49d3-8e5a-321ce04f849a","resolution":{"observed_at":"2026-08-03T20:57:10.558525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.19531/citation-record","integrity":"/paper/2505.19531/integrity","json":"/paper/2505.19531/citation-record.json","paper":"/paper/2505.19531"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.11892","last_updated":"2025-05-17T08:03:50Z","snapshot_observed_at":"2026-08-20T02:24:18.882401Z","submitted_at":"2025-05-17T08:03:50Z","title":"Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11892","snapshot_observed_at":"2026-08-07T14:21:00.129826Z","title":"Fast rope attention: Combining the polynomial method and fast fourier transform.arXiv preprint arXiv:2505.11892,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.129826Z"},"links":{"cited_paper":"/paper/2505.11892","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:3f393cc25e3f30fa783c3e366bd4ec39b94c7aa0f9b29bef3c97eec824a60e47","observation_id":"dfeadc09-5c90-4514-a781-34d65330052a","resolution":{"observed_at":"2026-08-07T14:21:00.129826Z","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":"2026-08-07T14:21:00.303448Z","title":"Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.303448Z"},"links":{"citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:de3088d52cb8eda17b7c707479f1fd16d4480dc6201ff83a7fb384f529e6c4fe","observation_id":"3613ede7-6236-4cd7-b96b-3bef5ecbc367","resolution":{"observed_at":"2026-08-07T14:21:00.303448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.07602","last_updated":"2024-12-01T06:39:41Z","snapshot_observed_at":"2026-08-19T18:04:02.969244Z","submitted_at":"2024-11-12T07:24:41Z","title":"Circuit Complexity Bounds for RoPE-based Transformer Architecture","version":2},"cited_work":{"arxiv_id":"2411.07602","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.07602","snapshot_observed_at":"2026-08-07T14:21:03.022225Z","title":"Circuit Complexity Bounds for RoPE-based Transformer Architecture","venue":"cs.LG","work_id":"66308e79-3c87-4486-999a-77f6ee06e4b0","year":2024},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.473856Z"},"links":{"cited_paper":"/paper/2411.07602","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:0a35fe7b8297f3a7b7767361a234c2bf5659da4c215d14d1bec6f949e8de7e1b","observation_id":"048926cc-308b-46de-867a-170a7ef15673","resolution":{"observed_at":"2026-08-07T14:21:03.091343Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04702","last_updated":"2025-04-07T03:08:12Z","snapshot_observed_at":"2026-08-17T23:06:05.864990Z","submitted_at":"2025-04-07T03:08:12Z","title":"Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent","version":1},"cited_work":{"arxiv_id":"2504.04702","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.04702","snapshot_observed_at":"2026-08-07T14:21:02.819073Z","title":"Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent","venue":"cs.LG","work_id":"1e763385-e246-4d05-b111-718a81659da2","year":2025},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.530502Z"},"links":{"cited_paper":"/paper/2504.04702","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:2792abc60a0ea2029e99ed1982f6fe9357f7c1b31f28e7a93e5363d52b9c51da","observation_id":"b244969a-2e56-4356-b7ec-223e8ae722a6","resolution":{"observed_at":"2026-08-07T14:21:02.912542Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T14:21:00.641603Z","title":"Bert: Pre- training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.641603Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:0fee12379063e4b73dbdd78543886d9bae60c8b4bee14d12417fa62defab56e3","observation_id":"d2393cb8-fdb7-4118-bac0-38c4439db750","resolution":{"observed_at":"2026-08-07T14:21:00.641603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.00337","last_updated":"2025-05-01T06:34:55Z","snapshot_observed_at":"2026-08-16T04:42:37.134626Z","submitted_at":"2025-05-01T06:34:55Z","title":"T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.00337","snapshot_observed_at":"2026-08-07T14:21:00.809131Z","title":"T2vphysbench: A first-principles benchmark for physical consistency in text-to-video generation.arXiv preprint arXiv:2505.00337,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.809131Z"},"links":{"cited_paper":"/paper/2505.00337","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:fb06affbd33cf8269a67d244da5cbf9d2a4c2da0e5cd3b41a9bac9e7512ac261","observation_id":"98c36a3c-2de1-4115-adcb-e61fcb878086","resolution":{"observed_at":"2026-08-07T14:21:00.809131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14840","last_updated":"2025-05-20T19:12:43Z","snapshot_observed_at":"2026-08-19T08:36:12.763365Z","submitted_at":"2025-05-20T19:12:43Z","title":"Subquadratic Algorithms and Hardness for Attention with Any Temperature","version":1},"cited_work":{"arxiv_id":"2505.14840","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.14840","snapshot_observed_at":"2026-08-07T14:21:02.595394Z","title":"Subquadratic Algorithms and Hardness for Attention with Any Temperature","venue":"cs.LG","work_id":"adfeef27-5a91-446a-b0f8-9874a6f023f4","year":2025},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.874738Z"},"links":{"cited_paper":"/paper/2505.14840","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:9960440ca53c63b535054aada1f57b3f6b39d5d86a4a5ab91619eec147e223e9","observation_id":"063b1293-4e99-4560-8953-4c1d2dd07be4","resolution":{"observed_at":"2026-08-07T14:21:02.673064Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05249","last_updated":"2023-10-08T17:55:33Z","snapshot_observed_at":"2026-08-17T09:47:37.660340Z","submitted_at":"2023-10-08T17:55:33Z","title":"In-Context Convergence of Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05249","snapshot_observed_at":"2026-08-07T14:21:00.978836Z","title":"In-context convergence of transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.978836Z"},"links":{"cited_paper":"/paper/2310.05249","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:0ff62057cd0293cb312440b2a0d8865f0b570c19b881652dba44ff8caf80c49c","observation_id":"56df5c5a-39b4-4178-b882-02195e1018e8","resolution":{"observed_at":"2026-08-07T14:21:00.978836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01258","last_updated":"2024-06-02T06:31:43Z","snapshot_observed_at":"2026-08-20T15:42:09.979675Z","submitted_at":"2024-02-02T09:29:40Z","title":"Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01258","snapshot_observed_at":"2026-08-07T14:21:01.293029Z","title":"Transformers learn nonlinear features in context: Noncon- vex mean-field dynamics on the attention landscape.arXiv preprint arXiv:2402.01258,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.293029Z"},"links":{"cited_paper":"/paper/2402.01258","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:f944f6cf0fe7b68b824e4ee7a16055e56f9d2503edb109721083dca8963df0af","observation_id":"f226550d-9ed6-48ea-858b-4d98beffd732","resolution":{"observed_at":"2026-08-07T14:21:01.293029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18040","last_updated":"2024-12-23T23:26:07Z","snapshot_observed_at":"2026-08-17T18:35:40.174622Z","submitted_at":"2024-12-23T23:26:07Z","title":"Theoretical Constraints on the Expressive Power of $\\mathsf{RoPE}$-based Tensor Attention Transformers","version":1},"cited_work":{"arxiv_id":"2412.18040","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.18040","snapshot_observed_at":"2026-08-07T14:21:02.361586Z","title":"Theoretical Constraints on the Expressive Power of $\\mathsf{RoPE}$-based Tensor Attention Transformers","venue":"cs.LG","work_id":"05c14c14-3cdb-41ac-8586-4c0eaabe1dfb","year":2024},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.340668Z"},"links":{"cited_paper":"/paper/2412.18040","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:35859285ce1825ef0e6a5386c77d00cab0c11f1bb021bf739c38446696e92647","observation_id":"e4caa9f6-825f-4547-9a72-b008be475c2d","resolution":{"observed_at":"2026-08-07T14:21:02.435527Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06444","last_updated":"2025-01-11T05:54:10Z","snapshot_observed_at":"2026-08-16T12:59:12.829800Z","submitted_at":"2025-01-11T05:54:10Z","title":"On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06444","snapshot_observed_at":"2026-08-07T14:21:01.419305Z","title":"On the computational capability of graph neural networks: A circuit complexity bound perspective.arXiv preprint arXiv:2501.06444,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.419305Z"},"links":{"cited_paper":"/paper/2501.06444","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:0a3e68224a285ff7e2f28c9e6e1c8424e05db999b0864b059e3823ca0baa255f","observation_id":"15190e46-7116-43bb-bada-dbb6eb711a62","resolution":{"observed_at":"2026-08-07T14:21:01.419305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03576","last_updated":"2023-07-07T13:09:18Z","snapshot_observed_at":"2026-08-20T14:15:07.919397Z","submitted_at":"2023-07-07T13:09:18Z","title":"One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03576","snapshot_observed_at":"2026-08-07T14:21:01.467347Z","title":"One step of gradient descent is provably the optimal in-context learner with one layer of linear self-attention","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.467347Z"},"links":{"cited_paper":"/paper/2307.03576","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:200e51cff21c354943e4ec91d780a21373d4fd173c3b3eb1f927bb472dfa853c","observation_id":"1027cf28-3136-40e0-9c9b-44a8bdd5b271","resolution":{"observed_at":"2026-08-07T14:21:01.467347Z","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":"2026-08-07T14:21:03.475035Z","title":"Evalu- ating numeracy of language models as a natural language inference task","venue":null,"work_id":"64e3e677-d9c7-49f9-b9fa-bc535aab4d87","year":2025},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.519168Z"},"links":{"citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:b4b789780397db0b8caa6c3ab03296707b7d09c186f04ea4f71cb928c9fb7697","observation_id":"9cadd4b5-c628-4d76-9b40-56a422be704e","resolution":{"observed_at":"2026-08-07T14:21:03.534648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-07T14:21:01.699323Z","title":"Llama 2: Open foundation and fine-tuned chat models.arXiv preprint arXiv:2307.09288,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.699323Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:2e13343fcd1355c1cfb9b0fbed2ec239bd7ffaaf4c70d5f26f6ffce622de078c","observation_id":"a5275ba0-4a72-438a-b059-5c7f92ff82ec","resolution":{"observed_at":"2026-08-07T14:21:01.699323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06925","last_updated":"2025-02-13T18:58:58Z","snapshot_observed_at":"2026-08-16T14:10:53.067006Z","submitted_at":"2024-03-11T17:12:09Z","title":"Transformers Learn Low Sensitivity Functions: Investigations and Implications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06925","snapshot_observed_at":"2026-08-07T14:21:01.743928Z","title":"Simplicity bias of transformers to learn low sensitivity functions.arXiv preprint arXiv:2403.06925,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.743928Z"},"links":{"cited_paper":"/paper/2403.06925","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:a918562888b3f2da68ccca8fde8eff41fe0c5f52345d9918b1ec19d9cadaa666","observation_id":"595b73b5-7fae-49f6-895d-3b764859bc8a","resolution":{"observed_at":"2026-08-07T14:21:01.743928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.02892","last_updated":"2023-02-15T09:50:30Z","snapshot_observed_at":"2026-08-21T03:31:32.049778Z","submitted_at":"2022-04-06T15:16:27Z","title":"Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02892","snapshot_observed_at":"2026-08-07T14:21:01.786208Z","title":"Sub-task decomposition enables learn- ing in sequence to sequence tasks.arXiv preprint arXiv:2204.02892,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.786208Z"},"links":{"cited_paper":"/paper/2204.02892","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:474485883d007e301a58198541beacf46ddd7c0277a721c6657a0a273439fe60","observation_id":"e3256b49-7b24-4451-9d9e-d7816e3bed41","resolution":{"observed_at":"2026-08-07T14:21:01.786208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05459","last_updated":"2025-03-05T13:57:56Z","snapshot_observed_at":"2026-08-16T13:11:45.819514Z","submitted_at":"2024-10-07T19:45:09Z","title":"From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05459","snapshot_observed_at":"2026-08-07T14:21:01.863974Z","title":"From sparse de- pendence to sparse attention: unveiling how chain-of-thought enhances transformer sample efficiency.arXiv preprint arXiv:2410.05459,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.863974Z"},"links":{"cited_paper":"/paper/2410.05459","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:10e871ec51627e7c8ee2235fb9fcdb8ddd439b456e9933f479e7039a63be799c","observation_id":"939fe7ff-cdb3-4f8f-88fc-e807cdd348f8","resolution":{"observed_at":"2026-08-07T14:21:01.863974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15006","last_updated":"2024-03-18T23:59:29Z","snapshot_observed_at":"2026-08-18T23:59:40.404245Z","submitted_at":"2023-06-26T18:43:46Z","title":"DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.15006","snapshot_observed_at":"2026-08-07T14:21:01.955998Z","title":"Dnabert-2: Efficient foundation model and benchmark for multi-species genome.arXiv preprint arXiv:2306.15006,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.955998Z"},"links":{"cited_paper":"/paper/2306.15006","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:26a6e0dd5694111ef0d29517a6739a7b2438f5f1b788e9f4d252bc7a935f9c90","observation_id":"700e4e8a-caf4-45ef-bfb2-347f4c249a7d","resolution":{"observed_at":"2026-08-07T14:21:01.955998Z","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":"2026-08-07T14:21:03.252014Z","title":"Genomeocean: An efficient genome foundation model trained on large-scale metagenomic assemblies","venue":null,"work_id":"f164d42b-74d1-421f-be8b-b8828c2a83a1","year":2025},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.993859Z"},"links":{"citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:9d9f21843972d3d759bf309befda32fb0f103bc3812dad1924e2353d9567f825","observation_id":"64b3c3ce-f5d0-49a0-bfd9-f86abd0ff6ae","resolution":{"observed_at":"2026-08-07T14:21:03.344204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08777","last_updated":"2024-10-22T04:14:08Z","snapshot_observed_at":"2026-08-19T00:02:06.140562Z","submitted_at":"2024-02-13T20:21:29Z","title":"DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.08777","snapshot_observed_at":"2026-08-07T14:21:02.060803Z","title":"Dnabert-s: Learning species-aware dna embedding with genome foundation models.arXiv preprint arXiv:2402.08777,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:02.060803Z"},"links":{"cited_paper":"/paper/2402.08777","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:7fb7d2999a785f0a35499bdfe08823cc8726acbc9cd7b5342883d10ec00c4fe9","observation_id":"fd78cc70-0225-422b-85bf-579607168ac3","resolution":{"observed_at":"2026-08-07T14:21:02.060803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13629","last_updated":"2025-01-02T19:19:20Z","snapshot_observed_at":"2026-08-16T13:16:48.347889Z","submitted_at":"2024-09-20T16:38:05Z","title":"Transformers in Uniform TC$^0$","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13629","snapshot_observed_at":"2026-08-07T14:21:00.402417Z","title":"Transformers in uniform tc0.arXiv preprint arXiv:2409.13629,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":1867,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.402417Z"},"links":{"cited_paper":"/paper/2409.13629","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:74042854c763efba9baa8537fbf0787b09bd0b608bb3113c9315d40eb787f74e","observation_id":"f0e4350d-26ba-4df3-b90e-ba5b1d39be14","resolution":{"observed_at":"2026-08-07T14:21:00.402417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09963","last_updated":"2024-05-27T17:01:29Z","snapshot_observed_at":"2026-08-16T14:18:18.310631Z","submitted_at":"2024-02-15T14:17:51Z","title":"Why are Sensitive Functions Hard for Transformers?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09963","snapshot_observed_at":"2026-08-07T14:21:01.050664Z","title":"Why are sensitive functions hard for transformers? arXiv preprint arXiv:2402.09963,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":1963,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.050664Z"},"links":{"cited_paper":"/paper/2402.09963","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:36d03720fd6e81158064ef45a80c2d193827a1e33932c2a75c429cc178c136cd","observation_id":"206e2655-941d-4e3c-a85b-05d3558c2bba","resolution":{"observed_at":"2026-08-07T14:21:01.050664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04051","last_updated":"2025-04-05T04:13:06Z","snapshot_observed_at":"2026-08-18T18:30:48.078667Z","submitted_at":"2025-04-05T04:13:06Z","title":"Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04051","snapshot_observed_at":"2026-08-07T14:21:00.742146Z","title":"Can you count to nine? a human evaluation benchmark for counting limits in modern text-to-video models.arXiv preprint arXiv:2504.04051,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":1993,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.742146Z"},"links":{"cited_paper":"/paper/2504.04051","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:597bdf891b1e7f09c91cd9040aa3605ced1ea2e5227bd9cb614ed6b22301a7a0","observation_id":"23287124-bca5-4cf0-8d95-87a8944e8a85","resolution":{"observed_at":"2026-08-07T14:21:00.742146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.14023","last_updated":"2024-01-29T10:16:41Z","snapshot_observed_at":"2026-08-19T18:58:08.946448Z","submitted_at":"2023-07-26T08:07:37Z","title":"Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.14023","snapshot_observed_at":"2026-08-07T14:21:01.122910Z","title":"Are transformers with one layer self-attention using low-rank weight matrices universal approximators?arXiv preprint arXiv:2307.14023,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.122910Z"},"links":{"cited_paper":"/paper/2307.14023","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:a80f28b8c5c75c6906170241f41974a8df2cf42b892a498ab66ee06ddd1f6bbc","observation_id":"826301cb-e573-4121-bba2-e1db4ad29938","resolution":{"observed_at":"2026-08-07T14:21:01.122910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-07T14:21:01.566777Z","title":"Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.566777Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:c8594adc5c05f8c92edce5262a2c513c1c0dcd2c1f9893bc7de9031836fa5990","observation_id":"ba15c0a1-cce9-4037-83c4-9e7db19ab1dc","resolution":{"observed_at":"2026-08-07T14:21:01.566777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17677","last_updated":"2025-02-27T08:19:55Z","snapshot_observed_at":"2026-08-20T10:58:55.149747Z","submitted_at":"2024-09-26T09:36:47Z","title":"On the Optimal Memorization Capacity of Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.17677","snapshot_observed_at":"2026-08-07T14:21:01.197868Z","title":"Optimal memorization capacity of transformers.arXiv preprint arXiv:2409.17677,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:01.197868Z"},"links":{"cited_paper":"/paper/2409.17677","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:4367f7ccdadf50c3c32caae67bd03b282d124674c9e8c4a13776a9a1b6da8e26","observation_id":"2db79da7-9150-4c5c-b483-19a604299ca4","resolution":{"observed_at":"2026-08-07T14:21:01.197868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.04064","last_updated":"2023-10-06T07:42:39Z","snapshot_observed_at":"2026-08-16T14:54:34.279132Z","submitted_at":"2023-10-06T07:42:39Z","title":"How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04064","snapshot_observed_at":"2026-08-07T14:21:00.090765Z","title":"How to capture higher-order correlations? generaliz- ing matrix softmax attention to kronecker computation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.090765Z"},"links":{"cited_paper":"/paper/2310.04064","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:d78ad820ae9347e5889093620828d1bf7584e2fd5770bb2a4c2600e13c323231","observation_id":"83bfdf52-d4cc-4c50-bed9-d0b40e8dba71","resolution":{"observed_at":"2026-08-07T14:21:00.090765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12712","last_updated":"2023-04-13T20:41:31Z","snapshot_observed_at":"2026-08-18T22:17:47.865607Z","submitted_at":"2023-03-22T16:51:28Z","title":"Sparks of Artificial General Intelligence: Early experiments with GPT-4","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12712","snapshot_observed_at":"2026-08-07T14:21:00.215985Z","title":"Sparks of artificial general intelligence: Early experiments with gpt-4.arXiv preprint arXiv:2303.12712,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T14:21:00.215985Z"},"links":{"cited_paper":"/paper/2303.12712","citing_paper":"/paper/2505.19531"},"observation_digest":"sha256:ea9337fc218117f40e6c6ba2a72052f049611f7c9a96e05db0f86b615770841e","observation_id":"51d5d997-6339-4246-adba-6ef2c4f6b0e1","resolution":{"observed_at":"2026-08-07T14:21:00.215985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.19531","last_updated":"2025-05-26T05:33:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T17:33:34.940404Z","submitted_at":"2025-05-26T05:33:26Z","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":4,"verified_fuzzy":2},"total_outbound_references":28},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2505.19531."}