{"as_of":"2026-08-16T20:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1bee4a96626cf09c815de09f5441ae9799ac088e089f880541338446fe60bdb6","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:49:39.652481Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2504.19901/citation-record","integrity":"/paper/2504.19901/integrity","json":"/paper/2504.19901/citation-record.json","paper":"/paper/2504.19901"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-16T19:40:28.523700Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-16T05:49:39.563435Z","title":"Gpt-4 tech- nical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.563435Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:f81c90a44e025c00547f0a150fb6ec4d52a9cbf037e8ba3dd4bd0492a9ff7e8e","observation_id":"b507613a-4654-4f7a-a466-b54a2cea6533","resolution":{"observed_at":"2026-08-16T05:49:39.563435Z","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":"2026-08-16T05:49:39.592971Z","title":"Superiority of softmax: Unveiling the performance edge over linear attention","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.592971Z"},"links":{"citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:43ca8508973cf92f9a380b84419182fdad5b7c5faa2183f060abe2ebfa96b349","observation_id":"2be047a2-6e9d-4a15-85fa-95bf7424e1e5","resolution":{"observed_at":"2026-08-16T05:49:39.592971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-16T05:49:39.597272Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.597272Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:f206907bbb7631e15537ef7c30cd689cf06da4f610a9d8f2d9313dcdf0c3c855","observation_id":"239a4c30-fddb-4f79-a561-9d1bf322860a","resolution":{"observed_at":"2026-08-16T05:49:39.597272Z","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":"2026-08-16T15:13:12.386910Z","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":"2026-08-16T05:49:39.606729Z","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":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.606729Z"},"links":{"cited_paper":"/paper/2307.14023","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:59b314d57f90ac4605388598ae2581ae4d599344c1df8b3048fe3790e0bca9e6","observation_id":"0a54420d-e062-4547-a14d-f7cd99503ed5","resolution":{"observed_at":"2026-08-16T05:49:39.606729Z","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":"2026-08-16T13:15:12.076331Z","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":"2026-08-16T05:49:39.611166Z","title":"Optimal memorization capacity of transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.611166Z"},"links":{"cited_paper":"/paper/2409.17677","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:fd10388a3a3271b939819b61e675275ce5df550b40d0119b62011ca661abf547","observation_id":"ea2009c0-44ee-47e0-b72b-37f6bbe1b1bf","resolution":{"observed_at":"2026-08-16T05:49:39.611166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08633","last_updated":"2025-03-11T14:26:41Z","snapshot_observed_at":"2026-08-16T13:10:24.858485Z","submitted_at":"2024-10-11T08:55:17Z","title":"Transformers Provably Solve Parity Efficiently with Chain of Thought","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08633","snapshot_observed_at":"2026-08-16T05:49:39.615441Z","title":"Transformers provably solve parity efficiently with chain of thought","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.615441Z"},"links":{"cited_paper":"/paper/2410.08633","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:f14ad1a8c685e6ac5dec45d95ee2bf4af2c32cc494502b5ba94d9fbb74f39b92","observation_id":"d6f2e11b-9e34-421d-9f8c-524bb324f063","resolution":{"observed_at":"2026-08-16T05:49:39.615441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10469","last_updated":"2024-10-14T13:01:11Z","snapshot_observed_at":"2026-08-16T13:09:38.138983Z","submitted_at":"2024-10-14T13:01:11Z","title":"Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10469","snapshot_observed_at":"2026-08-16T05:49:39.619918Z","title":"Moirai-moe: Empowering time series foundation models with sparse mixture of experts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.619918Z"},"links":{"cited_paper":"/paper/2410.10469","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:4fee02c944f912ae9e662d7fac3fea8bd5d86a4fb9fd998ef6f23caea0f44ec9","observation_id":"7a1922f4-66b7-4a16-afdf-cf9a795ad73f","resolution":{"observed_at":"2026-08-16T05:49:39.619918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11895","last_updated":"2022-09-24T00:43:19Z","snapshot_observed_at":"2026-08-15T09:43:59.961298Z","submitted_at":"2022-09-24T00:43:19Z","title":"In-context Learning and Induction Heads","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11895","snapshot_observed_at":"2026-08-16T05:49:39.629262Z","title":"In-context learning and induction heads","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.629262Z"},"links":{"cited_paper":"/paper/2209.11895","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:8cc9448fd1d51ac3a96b2381ea1d778448405f4b68a985173f7f1ce12da8b321","observation_id":"bde8a35c-b449-439a-88ba-253bf7ebf5f2","resolution":{"observed_at":"2026-08-16T05:49:39.629262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09268","last_updated":"2024-02-14T15:54:55Z","snapshot_observed_at":"2026-08-16T14:18:36.667442Z","submitted_at":"2024-02-14T15:54:55Z","title":"Transformers, parallel computation, and logarithmic depth","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09268","snapshot_observed_at":"2026-08-16T05:49:39.633644Z","title":"Transformers, parallel computation, and logarithmic depth","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.633644Z"},"links":{"cited_paper":"/paper/2402.09268","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:a9b62f5b0328da3cd43858615c8ce6e9f6e3ad9b0adb4508965515fb0405f556","observation_id":"0ba2cba4-d88c-45ee-9920-30b4ca025ece","resolution":{"observed_at":"2026-08-16T05:49:39.633644Z","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":"2026-08-16T05:49:39.638354Z","title":"doi: 10.1145/3530811","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.638354Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:50a423279dd92911cf0faff61bf749ce4b61c80486fa23fe98b3d4f3566337ca","observation_id":"cc945918-f96a-45da-8c6d-81d110d5c562","resolution":{"observed_at":"2026-08-16T05:49:39.638354Z","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":"2026-08-14T11:38:04.558916Z","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":"2026-08-16T05:49:39.647398Z","title":"Are transformers universal approximators of sequence-to-sequence functions? arXiv preprint arXiv:1912.10077,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.647398Z"},"links":{"cited_paper":"/paper/1912.10077","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:c2a297bcd5a20670154a976b0282b0dc975cea8787f77dce29d9a0bdbe830ff1","observation_id":"3bfb064f-9405-46f3-af0f-de6310a4f201","resolution":{"observed_at":"2026-08-16T05:49:39.647398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.04702","last_updated":"2025-04-07T03:08:12Z","snapshot_observed_at":"2026-08-16T12:43:31.172512Z","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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.04702","snapshot_observed_at":"2026-08-16T05:49:39.583561Z","title":"Provable failure of language models in learning majority boolean logic via gradient descent","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":1989,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.583561Z"},"links":{"cited_paper":"/paper/2504.04702","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:d49540eacc6134afd06e53ff4199d00bf7b226087903440dca5b9e33098139e4","observation_id":"1c4f421b-27cb-4979-82af-b5f27f6886f7","resolution":{"observed_at":"2026-08-16T05:49:39.583561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02592","last_updated":"2024-05-22T11:49:59Z","snapshot_observed_at":"2026-08-16T14:21:38.958196Z","submitted_at":"2024-02-04T20:00:45Z","title":"Unified Training of Universal Time Series Forecasting Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02592","snapshot_observed_at":"2026-08-16T05:49:39.642809Z","title":"Unified training of universal time series forecasting transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.642809Z"},"links":{"cited_paper":"/paper/2402.02592","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:afc940f8cbae6c59d09d5bc586553a6487b0853a17da103f282d1de60419acb8","observation_id":"f6e7943d-c231-4bdf-bf24-8c190f154d70","resolution":{"observed_at":"2026-08-16T05:49:39.642809Z","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":"2026-08-16T15:20:58.339539Z","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":"2026-08-16T05:49:39.652481Z","title":"Dnabert- 2: Efficient foundation model and benchmark for multi-species genome","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.652481Z"},"links":{"cited_paper":"/paper/2306.15006","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:f5acddcd1239e0812e358d949a27f443ff82cc6fc9ebc96de2489ae11358b82b","observation_id":"31ee2ee9-d86a-424e-943c-ca46ec15186d","resolution":{"observed_at":"2026-08-16T05:49:39.652481Z","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":"2026-08-16T05:49:40.020702Z","title":"Construction of neural nets using the radon transform","venue":null,"work_id":"f68de037-de46-4755-b7a8-ddc6efe64b0b","year":1989},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.578926Z"},"links":{"citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:4aea8afa0fe58b257a133391a203ffc4cbb364b7aaa0eb964852304caa226529","observation_id":"7c8e4ee2-65d2-4662-a941-dc4737d5bdc8","resolution":{"observed_at":"2026-08-16T05:49:40.028546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-16T05:49:39.602242Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.602242Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:97edc303dece12e8cd67070cdbf5fa3cf25609c4c1bcc7e069c273d8f32f8729","observation_id":"5fa68262-549b-45e8-9ad2-6af9e69e5ae0","resolution":{"observed_at":"2026-08-16T05:49:39.602242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02010","last_updated":"2024-03-02T07:50:37Z","snapshot_observed_at":"2026-08-16T15:26:57.006243Z","submitted_at":"2023-06-03T05:45:29Z","title":"Memorization Capacity of Multi-Head Attention in Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02010","snapshot_observed_at":"2026-08-16T05:49:39.624488Z","title":"Memorization capacity of multi-head attention in transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.624488Z"},"links":{"cited_paper":"/paper/2306.02010","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:d02c18de4e4ae8a3113b8c6f0d84a2c1c22b04c81991bdbb6f985c307965a065","observation_id":"289511ff-3ec4-438f-be27-4f0afc7f3bad","resolution":{"observed_at":"2026-08-16T05:49:39.624488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04271","last_updated":"2025-05-23T00:07:03Z","snapshot_observed_at":"2026-08-16T13:12:16.539038Z","submitted_at":"2024-10-05T19:21:13Z","title":"Fundamental Limitations on Subquadratic Alternatives to Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04271","snapshot_observed_at":"2026-08-16T05:49:39.568945Z","title":"Fundamental limitations on subquadratic alternatives to transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.568945Z"},"links":{"cited_paper":"/paper/2410.04271","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:d85663c1a0236b9c3531c1309ac7d6ae4c22d198ea816b6f25d71e1b3a4cf1e2","observation_id":"e6aae1c7-384d-41d5-b36d-978f6a39d766","resolution":{"observed_at":"2026-08-16T05:49:39.568945Z","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":"2026-08-16T05:49:40.041832Z","title":"Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Gireeja Ranade Sastry, Amanda Askell, et al","venue":null,"work_id":"f5012180-eb9a-46af-a623-2a225edea700","year":1901},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.573944Z"},"links":{"citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:55b75f219849b0e047d2bb5e113b9e2709149a69e5bba24a0a50bcade05be586","observation_id":"449140ae-de5b-49b3-b0e9-71b2f5e89f4e","resolution":{"observed_at":"2026-08-16T05:49:40.047596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04084","last_updated":"2024-02-06T15:39:09Z","snapshot_observed_at":"2026-08-16T14:20:58.371034Z","submitted_at":"2024-02-06T15:39:09Z","title":"Provably learning a multi-head attention layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04084","snapshot_observed_at":"2026-08-16T05:49:39.588423Z","title":"Provably learning a multi-head attention layer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-16T05:49:39.588423Z"},"links":{"cited_paper":"/paper/2402.04084","citing_paper":"/paper/2504.19901"},"observation_digest":"sha256:992d9a12d12d4d2be89b322b568d9850966a65ecedf82f43b00a9f48f3fd0fcf","observation_id":"26894e0e-f0a5-48e1-9329-2652a5012270","resolution":{"observed_at":"2026-08-16T05:49:39.588423Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.19901","last_updated":"2025-04-28T15:31:45Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T05:38:11.562387Z","submitted_at":"2025-04-28T15:31:45Z","title":"Attention Mechanism, Max-Affine Partition, and Universal Approximation"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":20},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2504.19901."}