{"as_of":"2026-08-10T03:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1152c1c94f08e7cc60191aba9b45faa1c10d7249c138beae7791d237aa7f606e","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T05:16:20.035595Z","state":"measured"},{"denominator":80,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":80,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T13:12:59.142441Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T17:51:09.301070Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03327","snapshot_observed_at":"2026-08-05T13:12:59.142441Z","title":"Is in-context universality enough? mlps are also universal in-context","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00924","last_updated":"2025-08-31T16:20:27Z","snapshot_observed_at":"2026-08-06T22:15:18.072877Z","submitted_at":"2025-08-31T16:20:27Z","title":"Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T13:12:59.142441Z"},"links":{"cited_paper":"/paper/2502.03327","citing_paper":"/paper/2509.00924"},"observation_digest":"sha256:c758062c0a99bffe1968ef28d2eea42bda63a598d00bf8b6aadf2fa1089ee323","observation_id":"176dbf16-26da-40ad-8009-8b4d397fc23b","resolution":{"observed_at":"2026-08-05T13:12:59.142441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03327","snapshot_observed_at":"2026-08-04T13:15:25.486024Z","title":"Is in-context universality enough? MLPs are also universal in-context","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.01163","last_updated":"2026-06-24T11:54:33Z","snapshot_observed_at":"2026-08-08T18:31:24.467184Z","submitted_at":"2025-10-01T17:52:29Z","title":"How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off","version":2},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.486024Z"},"links":{"cited_paper":"/paper/2502.03327","citing_paper":"/paper/2510.01163"},"observation_digest":"sha256:ba583700ca845f44f03e40dee7f8b4e80da5dd0f17cbbe1cca581ece6d83cae5","observation_id":"cb347cb6-80a6-497b-b45a-f7c4fa54372e","resolution":{"observed_at":"2026-08-04T13:15:25.486024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"cited_work":{"arxiv_id":"2502.03327","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.03327","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2502.03327(2025)","venue":null,"work_id":"a64759dd-b605-4881-9cda-808e2f1d323a","year":2025},"citing_paper":{"arxiv_id":"2605.04995","last_updated":"2026-05-06T14:53:55Z","snapshot_observed_at":"2026-07-06T23:17:43.402046Z","submitted_at":"2026-05-06T14:53:55Z","title":"Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T17:00:37.250246Z"},"links":{"cited_paper":"/paper/2502.03327","citing_paper":"/paper/2605.04995"},"observation_digest":"sha256:ba9a54b072b83641c258cf9bc479dcff850d828e31a061a390f7e9a9845a69d5","observation_id":"07a816b1-f254-4ad2-8a7d-725c13fe7c22","resolution":{"observed_at":"2026-05-11T17:51:09.303503Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.03327/citation-record","integrity":"/paper/2502.03327/integrity","json":"/paper/2502.03327/citation-record.json","paper":"/paper/2502.03327"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:16:21.815075Z","title":"Designing universal causal deep learning models: The geometric (hyper) transformer","venue":null,"work_id":"4adab047-06ac-4a44-b7c6-dc321e7d90e1","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.795337Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:1bc73ce2812ce0ed154ba5707804727d6d70e9d1d3d5d9333974c3c664263520","observation_id":"ecd161e9-20ed-4c02-a585-230745130e54","resolution":{"observed_at":"2026-08-09T05:16:21.819157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.805346Z","title":"What learning algorithm is in-context learning","venue":null,"work_id":"af7da70e-fd7e-4a64-8ff2-014478b7cd61","year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.799243Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:05c595c5035a3972f99a65727603c1c8084629d25f0709e0c6b8b8f9545d275c","observation_id":"2e5cce64-3d67-4e93-83ca-b598f340edde","resolution":{"observed_at":"2026-08-09T05:16:21.808759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.795718Z","title":"Linear extension operators between spaces of lipschitz maps and optimal transport","venue":null,"work_id":"2a980ac7-9381-407e-ae79-84af6dc04503","year":2020},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.802588Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:61f6fbcd7d9038c70fab111264a59661e44e11df8fd3f8e2890c62f10af7bdc5","observation_id":"8fba3083-7a91-4e99-b703-c71fc1540334","resolution":{"observed_at":"2026-08-09T05:16:21.799372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.0473","last_updated":"2016-05-19T21:53:22Z","snapshot_observed_at":"2026-07-06T03:53:10.336430Z","submitted_at":"2014-09-01T16:33:02Z","title":"Neural Machine Translation by Jointly Learning to Align and Translate","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.0473","snapshot_observed_at":"2026-08-09T05:16:19.806166Z","title":"Neural machine translation by jointly learning to align and translate","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.806166Z"},"links":{"cited_paper":"/paper/1409.0473","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:decf56e227f7ddd6cacb8367c29852fcbe27ad0bfbd7a04307862877ae12077e","observation_id":"9fa33873-5f97-4cd6-9068-f66fb392769d","resolution":{"observed_at":"2026-08-09T05:16:19.806166Z","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":"2026-08-09T05:16:19.809865Z","title":"Transformers as statisticians: Provable in-context learning with in-context algorithm selection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.809865Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:d0387118d9975082ae0482ecd13d44cd24d25ed0c95c0504284e3f196bdf7e14","observation_id":"adf248e8-b3d7-41f4-987a-71c3c16af205","resolution":{"observed_at":"2026-08-09T05:16:19.809865Z","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":"2026-08-09T05:16:21.780570Z","title":"Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations","venue":null,"work_id":"b51d2f04-d356-4345-ac48-ec2f84f6abed","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.814038Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:628def919ff11a14238527baba6740d4bd44a3376cd7ea1081d84abf580377de","observation_id":"4cd71b23-f8dc-4404-a3d4-97898b6b37ff","resolution":{"observed_at":"2026-08-09T05:16:21.784267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.770958Z","title":"Introduction to linear optimization, volume 6","venue":null,"work_id":"68aa11c5-decf-4084-831c-bae1bbcdd353","year":1997},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.817693Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:e9a6753f12e763c92c7fd8b8d28408b63d497bf72946b0189e3da8c7b3a7f82f","observation_id":"feeabac3-24f9-4d28-9a86-8a1850822636","resolution":{"observed_at":"2026-08-09T05:16:21.774597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.761161Z","title":"Optimal approximation with sparsely connected deep neural networks","venue":null,"work_id":"20f090ec-00be-4aa5-b00e-b413a059835a","year":2019},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.820860Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:16cdf6710c3f5a1c206d479b8575bfc609e91178ae499512819ed757f704b5a4","observation_id":"0e2fad36-95a0-485d-9258-41c165a87a77","resolution":{"observed_at":"2026-08-09T05:16:21.764993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13885","last_updated":"2025-03-09T17:33:35Z","snapshot_observed_at":"2026-08-07T15:07:28.074313Z","submitted_at":"2024-08-25T16:26:55Z","title":"Neural Spacetimes for DAG Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13885","snapshot_observed_at":"2026-08-09T05:16:19.823871Z","title":"Neural spacetimes for dag representation learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.823871Z"},"links":{"cited_paper":"/paper/2408.13885","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:25ca1478021e17b45893c7673c7dd9df1d8e04f23a0a6b91b4a429a72609450b","observation_id":"dfdbcd61-8efb-4286-a046-20056e333561","resolution":{"observed_at":"2026-08-09T05:16:19.823871Z","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":"2411.00835","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:16:21.315795Z","title":"Scalable message passing neural networks: No need for attention in large graph representation learning","venue":null,"work_id":"9a2b74b4-51c6-4cb6-babd-6a6d56f6784b","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.827180Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:74fa2bab939bc3100666640b0cfe024e5d69fef7d984032248e2c69b16dca38f","observation_id":"bde4a029-d612-4d49-bf3f-51c9fd72862a","resolution":{"observed_at":"2026-08-09T05:16:21.320430Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:19.830006Z","title":"Bridson and Andr\\'e Haefliger","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.830006Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:525358fc75c6e02af1123c07f7678ce93e9026abf931ca82ec5c467b621e6f22","observation_id":"892765f6-02ea-48fa-b67e-3b80ad089871","resolution":{"observed_at":"2026-08-09T05:16:19.830006Z","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":"10.1017/cbo9780511721182","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:16:20.130974Z","title":null,"venue":null,"work_id":"6de9d798-b5f4-423c-8db1-d8b817e590bd","year":2006},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.833228Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:c86595842d99f895e071d368e8023d7100735fa8ab99a457878cc8afadbfb139","observation_id":"42031319-43a2-4125-a996-d1e5ef36b978","resolution":{"observed_at":"2026-08-09T05:16:20.134647Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.752020Z","title":"How smooth is attention? In ICML 2024, 2024","venue":null,"work_id":"5a641851-d71c-4a70-8cd5-c30ea91a5693","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.836329Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:e9c0691570d137df55ea7967317452167b1c43d3a3279072798a2bd58e4bd0c6","observation_id":"0f94750a-5847-4d67-8bf1-9d3e708cc52c","resolution":{"observed_at":"2026-08-09T05:16:21.755302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19442","last_updated":"2024-06-10T17:18:07Z","snapshot_observed_at":"2026-08-07T21:58:57.595600Z","submitted_at":"2024-02-29T18:43:52Z","title":"Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19442","snapshot_observed_at":"2026-08-09T05:16:19.839437Z","title":"Training dynamics of multi-head softmax attention for in-context learning: Emergence, convergence, and optimality","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.839437Z"},"links":{"cited_paper":"/paper/2402.19442","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:1afc3896086322078d9733978aa458b11c86aace9e528cb13f20627627e51cf0","observation_id":"a7ddefc3-7877-40e9-98f9-c5866659a0df","resolution":{"observed_at":"2026-08-09T05:16:19.839437Z","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":"2026-08-09T05:16:21.741631Z","title":"Efficient approximation of high-dimensional functions with neural networks","venue":null,"work_id":"fc84fb48-3ef4-4a17-b5dc-482f24a9ef32","year":2021},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.842352Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:dfa360e5505ff324b6b00eb9596d382d137214029503c4ac27f879489f6babac","observation_id":"c0e48b21-816d-487d-89a0-2bc734aa533c","resolution":{"observed_at":"2026-08-09T05:16:21.745626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.732014Z","title":"Efficient approximation of high-dimensional functions with neural networks","venue":null,"work_id":"c5d4beec-00f5-45e2-aa13-203e53a23c32","year":2021},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.845468Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:15f475baef02f3e8a62513880fc07bedbb75a3187f0813ffba219e546674a50d","observation_id":"7890448d-ed45-46c6-81f9-056bbde011c7","resolution":{"observed_at":"2026-08-09T05:16:21.735121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.723273Z","title":"Tighter bounds on the expressivity of transformer encoders","venue":null,"work_id":"a662a389-766e-477c-9516-fcb52dab2592","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.848147Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:98dc99ff74991203c2c1f2a1b0edfb487f3d84c7d6ae5e4a59bb953942ee65fc","observation_id":"c30dd528-9bc7-45e6-99cc-eeb3aacf4200","resolution":{"observed_at":"2026-08-09T05:16:21.726168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.714295Z","title":"Conditional positional encodings for vision transformers","venue":null,"work_id":"e8090da0-e0fe-4dd8-a4c0-1b18fed064d9","year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.851339Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:3b1ba8c398090667f69928de753b9699136116a103adc13c124fabb641618bc3","observation_id":"654f77b9-665a-465e-92b0-ce0acc3388ed","resolution":{"observed_at":"2026-08-09T05:16:21.717383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:19.854213Z","title":"Global universal approximation of functional input maps on weighted spaces","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.854213Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:e6199b3d1f4aab416f558f931c0d119a6db486d99afb86cc7f2891400b172092","observation_id":"41e8b8ed-08b1-4230-bd01-1bd8ee1a9f46","resolution":{"observed_at":"2026-08-09T05:16:19.854213Z","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":"2026-08-09T05:16:21.705230Z","title":"The density theorem and hausdorff inequality for packing measure in general metric spaces","venue":null,"work_id":"d717420e-a6e5-4ab7-8472-8c91491a278e","year":1995},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.857009Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:17fc699c099fdd1bb36e23e01c7a3f01cc126ae7f9e8d1d22e19fbb7ea36231e","observation_id":"8ef7685d-2ea1-4c50-bc62-296db87fbc89","resolution":{"observed_at":"2026-08-09T05:16:21.708952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.695218Z","title":"Neural snowflakes: Universal latent graph inference via trainable latent geometries","venue":null,"work_id":"429d39cc-ad6c-4eab-96a8-52588f93efac","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.860154Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:8935443cc9a9ab72972de84df0348c356c6e80d4b1eaf331fca25451cd4b88a6","observation_id":"7acec2cb-a88b-4fad-b2ed-924cf48f165a","resolution":{"observed_at":"2026-08-09T05:16:21.699002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.02462","last_updated":"2024-05-09T21:29:03Z","snapshot_observed_at":"2026-08-09T23:33:09.197783Z","submitted_at":"2024-05-03T19:52:07Z","title":"Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression","version":2},"cited_work":{"arxiv_id":"2405.02462","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.02462","snapshot_observed_at":"2026-08-09T05:16:20.961317Z","title":"Finite Sample Analysis and Bounds of Generalization Error of Gradient Descent in In-Context Linear Regression","venue":"math.ST","work_id":"fc224315-7221-4c66-8da0-10f9d2da29db","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.863290Z"},"links":{"cited_paper":"/paper/2405.02462","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:c6a91556bcde556602fe724df5de75591ecebbf34789105f7ea6aa35b9596e96","observation_id":"ae0ca787-76c9-4307-ab80-230dc6ab1875","resolution":{"observed_at":"2026-08-09T05:16:20.965615Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12166","last_updated":"2022-02-24T16:06:01Z","snapshot_observed_at":"2026-08-08T02:05:53.205992Z","submitted_at":"2022-02-24T16:06:01Z","title":"Attention Enables Zero Approximation Error","version":1},"cited_work":{"arxiv_id":"2202.12166","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.12166","snapshot_observed_at":"2026-08-09T05:16:20.948490Z","title":"Attention Enables Zero Approximation Error","venue":"cs.LG","work_id":"1aea0c02-e8f0-4672-aa66-44ef07471b22","year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.866088Z"},"links":{"cited_paper":"/paper/2202.12166","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:3dab2417d8363d12e071950baf16e1ab854f9c20472c183ce9e0e4cf0439b640","observation_id":"ae49969e-d74e-4443-8975-fc6694ab48df","resolution":{"observed_at":"2026-08-09T05:16:20.952402Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.14788","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:16:20.933481Z","title":"Simultaneously solving fbsdes with neural operators of logarithmic depth, constant width, and sub-linear rank","venue":null,"work_id":"614017dc-a76b-44bb-ad2f-8935d11bf3ca","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.870098Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:e957d023323e453bbdf0fa8fcf9559feb175017db3737ae1fc03357d04ed0091","observation_id":"763f1c62-3955-45bc-8fc5-a24c1d2fc811","resolution":{"observed_at":"2026-08-09T05:16:20.938514Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03982","last_updated":"2023-06-06T19:35:09Z","snapshot_observed_at":"2026-08-04T12:29:17.330553Z","submitted_at":"2023-06-06T19:35:09Z","title":"Globally injective and bijective neural operators","version":1},"cited_work":{"arxiv_id":"2306.03982","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.03982","snapshot_observed_at":"2026-08-09T05:16:20.697099Z","title":"Globally injective and bijective neural operators","venue":"cs.LG","work_id":"be222622-ceb5-407d-8a21-f88e5f6e5703","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.873240Z"},"links":{"cited_paper":"/paper/2306.03982","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:903ccedd862759a741a83dda37ea0631bc843ba2127fa8d9e7822168c28064ec","observation_id":"ef6a8941-78ac-46b6-9612-a0f811e67e68","resolution":{"observed_at":"2026-08-09T05:16:20.750418Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.01367","last_updated":"2024-10-03T02:43:59Z","snapshot_observed_at":"2026-08-06T11:28:59.629862Z","submitted_at":"2024-08-02T16:21:48Z","title":"Transformers are Universal In-context Learners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.01367","snapshot_observed_at":"2026-08-09T05:16:19.876202Z","title":"Transformers are universal in-context learners","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.876202Z"},"links":{"cited_paper":"/paper/2408.01367","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:ab689befe707d056fcca8346bdfdec9885ec7b2f9b58a132d212bcf324609cb7","observation_id":"70cbcf87-ee94-4be2-a11c-3c090bc819ec","resolution":{"observed_at":"2026-08-09T05:16:19.876202Z","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":"2026-08-09T05:16:19.879073Z","title":"What can transformers learn in-context? a case study of simple function classes","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.879073Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:a98ed45b8653e4aaedbd27c698c0ce04887fe8e6f0a04e7ae84e06046e2fc5ea","observation_id":"9fdb0272-03fc-433a-920a-561efe06fa05","resolution":{"observed_at":"2026-08-09T05:16:19.879073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03974","last_updated":"2025-04-14T04:16:25Z","snapshot_observed_at":"2026-07-06T19:28:12.480864Z","submitted_at":"2024-10-04T23:27:33Z","title":"Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03974","snapshot_observed_at":"2026-08-09T05:16:19.882193Z","title":"Robust barycenter estimation using semi-unbalanced neural optimal transport","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.882193Z"},"links":{"cited_paper":"/paper/2410.03974","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:5ef0359b08c6a0470035ceda6cf227c8c9bc9edafec5c907608711bf687c5b86","observation_id":"ca50433c-27a1-46e9-9f54-7c53c8047e6b","resolution":{"observed_at":"2026-08-09T05:16:19.882193Z","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":"2026-08-09T05:16:21.680142Z","title":"A survey on lipschitz-free banach spaces","venue":null,"work_id":"bf962883-4607-4fe4-84a1-c69848fb548e","year":2015},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.885277Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:b9e638fc58a0edb388911ab27e17e4dfd7d9abf167b0c5f9c410fe86ab569f73","observation_id":"6309ba7d-88d5-4133-98aa-54665326a09b","resolution":{"observed_at":"2026-08-09T05:16:21.683727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.669602Z","title":"Can a transformer represent a kalman filter? In 6th Annual Learning for Dynamics & Control Conference, pages 1502--1512","venue":null,"work_id":"be151db2-a72b-4ec9-b125-466be5951901","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.889817Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:73eb442e4c678702c6749a8af05c665a9f007faec26ac490208739420fb81dc4","observation_id":"73a2262f-1616-499a-b2b4-6658d8fb5883","resolution":{"observed_at":"2026-08-09T05:16:21.673484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:19.893083Z","title":"Multilayer feedforward networks are universal approximators","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.893083Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:d4e7691914f9ae49a475a37d093664dc94cc039cc1517abbd047f77fda7e29e8","observation_id":"1cd76a4b-0353-4272-9d09-2262fd606861","resolution":{"observed_at":"2026-08-09T05:16:19.893083Z","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":"2026-08-09T05:16:19.896042Z","title":"Addressing common misinterpretations of kart and uat in neural network literature","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.896042Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:88f785656029161279db8aa49c98342f2c1fc275016ad8aeede361d01d643793","observation_id":"4ae1ce36-5e72-4925-9045-3e7307451acc","resolution":{"observed_at":"2026-08-09T05:16:19.896042Z","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":"2026-08-09T05:16:19.899075Z","title":"Neural tangent kernel: Convergence and generalization in neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.899075Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:8af70a36717896e885e4d2ed4cbbbd0c4437e24e7afc283b3b15053f22cf49e5","observation_id":"503268a5-baf7-433c-8b82-1f2dc3d21549","resolution":{"observed_at":"2026-08-09T05:16:19.899075Z","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":"10.1007/s00209-009-0555-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:16:20.120894Z","title":"arvenp\\\"a\\","venue":null,"work_id":"ca4fc8d9-ed52-4e57-92f3-180dc5194746","year":2010},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.901963Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:11c08b3d2f64ac27a8f75545ce3430c99085af3c232a8decef2d12bf831e33f4","observation_id":"f9610677-c401-4ecb-be63-c32f987129fe","resolution":{"observed_at":"2026-08-09T05:16:20.124856Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.648385Z","title":"Universal approximation with deep narrow networks","venue":null,"work_id":"cf461989-de79-425b-8959-ac4d19d808c2","year":2020},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.905317Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:73159f43d603a16a6cd196509a7044783c8be0f1171612dd2d4b47448626c624","observation_id":"a120df26-5b1d-4234-8641-31796ed7966a","resolution":{"observed_at":"2026-08-09T05:16:21.651911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.638903Z","title":"Transformers provably solve parity efficiently with chain of thought","venue":null,"work_id":"defa24c0-e608-432d-9e94-96bf99da83cf","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.908281Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:6bd4d017e7331509cc5c1f54f57a5b3debd12ea0c7e1647aebc87b6e99a93618","observation_id":"cc5daf97-93f1-4978-b8e5-b220328d06ff","resolution":{"observed_at":"2026-08-09T05:16:21.642513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.629272Z","title":"Transformers learn nonlinear features in context","venue":null,"work_id":"cbf62dbb-294c-44b4-93ed-11d0a40e5c7b","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.911575Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:eaa44a5d8c07d460a0dea9b965fb8437eb1699ac8fe36b44b4040ba7d824c5cf","observation_id":"a86836f6-562c-4349-84bc-b59962c9e274","resolution":{"observed_at":"2026-08-09T05:16:21.633115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.619584Z","title":"Transformers are minimax optimal nonparametric in-context learners","venue":null,"work_id":"28cfd757-c798-4d9b-9d78-c3d04e7073c3","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.915264Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:8526d515b4cb9e0dd162abf64ea911760dffd04b360ca636ff9a0486e504eca3","observation_id":"017484ad-b5de-47df-a479-80afe0fe4ebb","resolution":{"observed_at":"2026-08-09T05:16:21.623175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.13082","last_updated":"2020-12-10T10:53:46Z","snapshot_observed_at":"2026-08-09T11:13:52.803054Z","submitted_at":"2019-09-28T12:42:12Z","title":"Wasserstein-2 Generative Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.13082","snapshot_observed_at":"2026-08-09T05:16:19.918262Z","title":"Wasserstein-2 generative networks","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.918262Z"},"links":{"cited_paper":"/paper/1909.13082","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:d2738479cfff3cfe40d790c9a16572b6879d14f2bb8491b004ea0013d414b0bb","observation_id":"730d4adf-5091-4045-b56b-80320bf4be82","resolution":{"observed_at":"2026-08-09T05:16:19.918262Z","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":"2026-08-09T05:16:21.610650Z","title":"Neural optimal transport","venue":null,"work_id":"40a4d848-9c72-4d9e-b517-c3871ef11782","year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.922189Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:d09439faf90f13fe1f988d2bef3218a8a4ef0248935c059e10f21120afea0bfc","observation_id":"43eb5b28-bbe7-41c8-87a2-f9dfc56d72a1","resolution":{"observed_at":"2026-08-09T05:16:21.613866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.600550Z","title":"Universal approximation theorems for differentiable geometric deep learning","venue":null,"work_id":"fda3bd11-88ec-498f-9a74-87bb5c6e21dd","year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.925118Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:b6b4f609507ff06605eb25655b46140221d9797ba325f5317f5710601037651c","observation_id":"fc6a46ec-65a4-4e79-afa7-a6a649eeb39c","resolution":{"observed_at":"2026-08-09T05:16:21.604277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.590862Z","title":"Universal approximation under constraints is possible with transformers","venue":null,"work_id":"7ca6e6bc-3631-4ce4-9516-7ba7fcfdfcbf","year":2021},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.928502Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:c2470bc48dfa2626cae253f2159c5e026c942e3b8bc81ccab884781d423a1b8b","observation_id":"6a2d62d8-7e82-40e9-b2e0-f8254c245e8c","resolution":{"observed_at":"2026-08-09T05:16:21.594315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12231","last_updated":"2023-07-24T16:00:37Z","snapshot_observed_at":"2026-08-06T10:08:50.175343Z","submitted_at":"2023-04-24T16:18:22Z","title":"An Approximation Theory for Metric Space-Valued Functions With A View Towards Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12231","snapshot_observed_at":"2026-08-09T05:16:19.931454Z","title":"An approximation theory for metric space-valued functions with a view towards deep learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.931454Z"},"links":{"cited_paper":"/paper/2304.12231","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:6c65a3a7cecf80ed6c5578be2b91d564e8df0a8d6bdd2809f5189135582cef27","observation_id":"6a3a28d0-cb4b-4915-a578-df76a177c2be","resolution":{"observed_at":"2026-08-09T05:16:19.931454Z","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":"2026-08-09T05:16:21.582313Z","title":"Learnable fourier features for multi-dimensional spatial positional encoding","venue":null,"work_id":"d1b496d5-9ecd-4f3f-a582-11f48a0045b6","year":2021},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.934876Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:dbedd4c7db9db87cdbd1791352b4d8e189a4246460a3cbafd995e7e17117cee8","observation_id":"24ae4e24-6654-4c0c-9e6c-c61e8574efed","resolution":{"observed_at":"2026-08-09T05:16:21.585087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.574145Z","title":"Transformers as algorithms: Generalization and stability in in-context learning","venue":null,"work_id":"1a84a51a-1c9f-4954-8359-be96fa71f620","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.937448Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:69045d7a76756f8a635fc2d545b15784468136fcbc14a580a7e7d0892893f895","observation_id":"c5928e98-64fd-4828-bbbd-916d34fe444c","resolution":{"observed_at":"2026-08-09T05:16:21.577097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16563","last_updated":"2025-02-06T12:02:36Z","snapshot_observed_at":"2026-08-05T07:00:48.859508Z","submitted_at":"2024-05-26T13:19:32Z","title":"Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16563","snapshot_observed_at":"2026-08-09T05:16:19.939842Z","title":"Reality only happens once: Single-path generalization bounds for transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.939842Z"},"links":{"cited_paper":"/paper/2405.16563","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:8438699fcb56e027fea43a4514659c34e11858740ea436a26403ae2dffb49c65","observation_id":"8edd86e1-bb9b-4c92-b7ad-66d400f53683","resolution":{"observed_at":"2026-08-09T05:16:19.939842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19756","last_updated":"2025-02-09T21:09:09Z","snapshot_observed_at":"2026-07-06T18:07:47.744531Z","submitted_at":"2024-04-30T17:58:29Z","title":"KAN: Kolmogorov-Arnold Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19756","snapshot_observed_at":"2026-08-09T05:16:19.942568Z","title":"Kan: Kolmogorov-arnold networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.942568Z"},"links":{"cited_paper":"/paper/2404.19756","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:25f3e767167353533ac2e9484e0122bd97c3978a2af8e0ccd472e6189b3d0e2e","observation_id":"b935a897-753c-4941-87bd-7e7d205f5be1","resolution":{"observed_at":"2026-08-09T05:16:19.942568Z","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":"2026-08-09T05:16:19.945962Z","title":"Asymptotic theory of in-context learning by linear attention","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.945962Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:f98ccf31ac834caa0c5b8db8162c0b4711dfcf65afebe15cf8f4afef672ef9d3","observation_id":"38d62c41-b84f-41fd-a94b-abc8fe94f379","resolution":{"observed_at":"2026-08-09T05:16:19.945962Z","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":"2026-08-09T05:16:21.566004Z","title":"Your transformer may not be as powerful as you expect","venue":null,"work_id":"0fa8aacc-0a2d-49b8-a998-5047f67a24d7","year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.949828Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:82f5eb61aabb9972d31f836b3fdab44dcfc4a17eadfa0255bdfcb44e25a89bef","observation_id":"72120011-5fe1-45af-a491-9d8d0d8752cc","resolution":{"observed_at":"2026-08-09T05:16:21.568838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1090/s0002-9939-98-04201-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:16:20.076969Z","title":"Every complete doubling metric space carries a doubling measure","venue":null,"work_id":"0ae914a6-6249-49df-8e88-46a44d6f9722","year":1998},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.952949Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:d24f2896a80bc69c8a34d526b0cd3dcef14754f55cce17b1da7dfd712f869f78","observation_id":"20a5e2fa-22a0-4f2e-b3df-9aafc6e9a2b1","resolution":{"observed_at":"2026-08-09T05:16:20.113744Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07923","last_updated":"2024-04-11T18:03:53Z","snapshot_observed_at":"2026-08-02T23:51:22.170619Z","submitted_at":"2023-10-11T22:35:18Z","title":"The Expressive Power of Transformers with Chain of Thought","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07923","snapshot_observed_at":"2026-08-09T05:16:19.956712Z","title":"The expresssive power of transformers with chain of thought","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.956712Z"},"links":{"cited_paper":"/paper/2310.07923","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:4f2f03b001b1746bf1c195c41e7a502f11c4d425c749f32e21ff0c0f4b78c8cf","observation_id":"2f6a6133-168b-46fa-91d4-bf4fafd15447","resolution":{"observed_at":"2026-08-09T05:16:19.956712Z","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":"2026-08-09T05:16:21.557214Z","title":"Length independent pac-bayes bounds for simple rnns","venue":null,"work_id":"266d6303-ae25-4cdb-b4d1-8726012c0290","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.959711Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:4816836b1aff3c52a3548f95941f9b2213515e65e85fc262968e5feaf2e9c1e4","observation_id":"f001226e-3fb4-4c26-a24c-ca1f71fe6bdb","resolution":{"observed_at":"2026-08-09T05:16:21.560918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11895","last_updated":"2022-09-24T00:43:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2026-08-09T05:16:19.962376Z","title":"In-context learning and induction heads","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.962376Z"},"links":{"cited_paper":"/paper/2209.11895","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:831fbe069298034bf0f13b8aff97b2af119355da89ed067b6a97ae6d28ec3064","observation_id":"cd91476d-b53a-48d2-adde-ec661e5f586f","resolution":{"observed_at":"2026-08-09T05:16:19.962376Z","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":"2026-08-09T05:16:21.547273Z","title":"Equivalence of approximation by convolutional neural networks and fully-connected networks","venue":null,"work_id":"cdb5ce16-6189-4715-8623-08cfea12fa52","year":2020},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.965079Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:3e10bc1729ebac1b25d21178ed7f4814530d303972875e77067df3a515a4c292","observation_id":"a4874534-0991-4286-8350-d962373f4ffc","resolution":{"observed_at":"2026-08-09T05:16:21.550874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:19.967807Z","title":"Mathematical theory of deep learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.967807Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:718a61dfa9f37178a5b58a7be76ea3010058bd2c77b8e648e3b11ac8cf211836","observation_id":"0cb4c701-24ff-407c-8ead-2e7a46585d73","resolution":{"observed_at":"2026-08-09T05:16:19.967807Z","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":"2026-08-09T05:16:21.536905Z","title":"Universal in-context approximation by prompting fully recurrent models","venue":null,"work_id":"827acebb-095b-4255-805d-db80e9685c70","year":2025},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.970582Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:dd68581212750a678df76db98ae886c112cb33a29215b681276fcf68d203aeeb","observation_id":"3b6a6ac9-f566-472d-b88a-d6fc4b47704b","resolution":{"observed_at":"2026-08-09T05:16:21.541237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.526397Z","title":"Computational optimal transport: With applications to data science","venue":null,"work_id":"8fee42c4-5ad6-4599-8f49-29e004f84076","year":2019},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.973767Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:915ac4efaa90ef1f3d48a85e5b9c17140fbb5e2123990db7881848bf32b57e81","observation_id":"0d42cb49-e56d-4671-bfc0-74e0cf90fdcd","resolution":{"observed_at":"2026-08-09T05:16:21.530362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.516771Z","title":"Computational optimal transport: With applications to data science","venue":null,"work_id":"5ea3a1c4-d3e4-476b-8fd2-49eb2e028b90","year":2019},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.976785Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:aa767979d1925820fd5f91ef771bd6c23cfb6d7bd2b36f6f88ab7a608e4927e5","observation_id":"8e6e71c5-3e4b-4944-aec1-bb395100c4eb","resolution":{"observed_at":"2026-08-09T05:16:21.520140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05941","last_updated":"2017-10-27T17:45:21Z","snapshot_observed_at":"2026-08-08T18:23:31.977872Z","submitted_at":"2017-10-16T18:05:45Z","title":"Searching for Activation Functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05941","snapshot_observed_at":"2026-08-09T05:16:19.979626Z","title":"Searching for activation functions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.979626Z"},"links":{"cited_paper":"/paper/1710.05941","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:18648842dfd62457e61876de81134f9fa3dbba8fa6cfcbc9fb69b1547b48a306","observation_id":"b6828b66-1d34-44a2-b68a-592eeee36696","resolution":{"observed_at":"2026-08-09T05:16:19.979626Z","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":"2026-08-09T05:16:21.506307Z","title":"The mechanistic basis of data dependence and abrupt learning in an in-context classification task","venue":null,"work_id":"5befeabd-0b35-4fc2-b887-dbb3271e19d9","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.986374Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:36349ad0f535a50675ee3429b96047fd63939f651b16a962bb55115f3816cb2a","observation_id":"576ece54-d6d1-4102-a301-efc2767c9d30","resolution":{"observed_at":"2026-08-09T05:16:21.510196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.494568Z","title":"Singular value perturbation and deep network optimization","venue":null,"work_id":"ef8d0f1f-a64b-463b-9ad5-437467d7ccc4","year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.989514Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:6dc842daa4a545a78011304c88d7700f0b9a6212c6aaeb1fccf47271b253206b","observation_id":"df979d68-0b12-4bb0-b3bb-f5d44cd9fca2","resolution":{"observed_at":"2026-08-09T05:16:21.498449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05202","last_updated":"2020-02-12T19:57:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-12T19:57:13Z","title":"GLU Variants Improve Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.05202","snapshot_observed_at":"2026-08-09T05:16:19.992077Z","title":"Glu variants improve transformer","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.992077Z"},"links":{"cited_paper":"/paper/2002.05202","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:a79c9dc08c2ddf67498aab05fc3920ef9e6f3b71a1137983efc1bc89cbf5981f","observation_id":"c37f7e34-d5b8-4939-8923-ea02a711f7c7","resolution":{"observed_at":"2026-08-09T05:16:19.992077Z","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":"2026-08-09T05:16:21.483375Z","title":"Nonparametric estimation of non-crossing quantile regression process with deep requ neural networks","venue":null,"work_id":"5d97b496-6203-4c95-a80e-95e302d1a570","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.994919Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:c35490c87cfb03d3482483f023b31dae7d0fa22ae03d68cdc6e6ce5ea6c2808d","observation_id":"660c996d-274f-4b18-bf08-86700d39d9d7","resolution":{"observed_at":"2026-08-09T05:16:21.487880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:19.997348Z","title":"Optimal approximation rate of R e LU networks in terms of width and depth","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:19.997348Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:6ad8482427f978c8469c26755f8ef96407674439825c5f9f8aba49cb5659bfc7","observation_id":"9794ae5b-a703-4c69-be27-8f5e0d9fd6f8","resolution":{"observed_at":"2026-08-09T05:16:19.997348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08218","last_updated":"2024-03-15T13:50:58Z","snapshot_observed_at":"2026-07-06T16:06:46.953622Z","submitted_at":"2023-08-16T08:45:53Z","title":"Expressivity of Spiking Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08218","snapshot_observed_at":"2026-08-09T05:16:20.000075Z","title":"Expressivity of spiking neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.000075Z"},"links":{"cited_paper":"/paper/2308.08218","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:c10c53cdb512a1ae7607886c4d1ac91c8223a68c72e3c7013c198fa5b25fe43e","observation_id":"132396c5-5537-4aeb-aca4-731d31a94432","resolution":{"observed_at":"2026-08-09T05:16:20.000075Z","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":"2026-08-09T05:16:21.471337Z","title":"Training dynamics of multi-head softmax attention for in-context learning: Emergence, convergence, and optimality","venue":null,"work_id":"1da89356-a4b1-44fa-9dee-eba771a74dc7","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.003385Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:51b666b29350ba1811497e8df34328af2707333135d1300c21b81c3791dfb214","observation_id":"6fbf6c71-5356-48ba-9775-66a27610e31e","resolution":{"observed_at":"2026-08-09T05:16:21.476539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.460452Z","title":"What formal languages can transformers express? a survey","venue":null,"work_id":"2b21e39a-1356-4557-bc5a-a845782abd4f","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.006386Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:e8657377845aef8e871de6b37a494decbfd8f8ce6055ee1cec99e453fad4b2bb","observation_id":"0bea5d22-66d0-4bc5-84f7-7ebf5cae3563","resolution":{"observed_at":"2026-08-09T05:16:21.464500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.449315Z","title":"Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality","venue":null,"work_id":"8da77957-1d42-4a49-8285-5e8a288e5800","year":2018},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.009814Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:b351002c6e663a43ff8260290bdb816cd01c7b04a86af7eeea34cd24ad21e782","observation_id":"316656c7-333d-43f9-a7d4-e444ff40f0af","resolution":{"observed_at":"2026-08-09T05:16:21.453428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:20.012867Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.012867Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:204ee50cd90b12c8c6c6c2d85939fc8f3f2e3504e5a7141b9ab8640527c9a7b2","observation_id":"30d5222c-6ab3-4ac8-8861-a8483b674e97","resolution":{"observed_at":"2026-08-09T05:16:20.012867Z","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":"2026-08-09T05:16:20.015931Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.015931Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:255bba016de197eb861a4d9f252bc7cb6786f2d34e652cfb9535710ef32ba564","observation_id":"f2625e20-e81b-4c6c-945e-627c2856885f","resolution":{"observed_at":"2026-08-09T05:16:20.015931Z","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":"2026-08-09T05:16:20.018499Z","title":"Optimal transport, volume 338 of Grundlehren der mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences]","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.018499Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:97221fff7fa6a61dfae54871b3d5df17098250191906f3658aad0e37c1c1dd53","observation_id":"24995316-9db7-483e-81c8-3def793f395a","resolution":{"observed_at":"2026-08-09T05:16:20.018499Z","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":"2026-08-09T05:16:21.434174Z","title":"Distance-based classification with lipschitz functions","venue":null,"work_id":"601b450c-6240-4d25-aa9e-4792241fd418","year":2004},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.021340Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:cc5b8ea0b8b65f5c22a0cb605ceb4d0871fbe6b8d275328f290e7129fc9a9f14","observation_id":"427b4ef2-0682-4cf6-a99d-c50dba969da0","resolution":{"observed_at":"2026-08-09T05:16:21.437598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:20.024110Z","title":"Transformers learn in-context by gradient descent","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.024110Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:6040ea76425f736f7350af268e99e208df6511ff6671419fe97c0cc1a5ccdb7e","observation_id":"dbddc119-e52e-4b17-aec2-3e6fc19cdbbd","resolution":{"observed_at":"2026-08-09T05:16:20.024110Z","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":"2026-08-09T05:16:20.026715Z","title":"Lipschitz algebras","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.026715Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:4f8c72cbb0c5d0d537a971a4115c09fefb43e6a277094f495fb22a590007770d","observation_id":"711b7090-9459-4e12-bcd5-669dc2b9e48f","resolution":{"observed_at":"2026-08-09T05:16:20.026715Z","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":"2026-08-09T05:16:21.406104Z","title":"Optimal approximation of continuous functions by very deep relu networks","venue":null,"work_id":"2d272be8-58ea-4d22-9b5d-09b0a39151da","year":2018},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.029568Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:e4d73565cf038b166d93c0357da197c3089eac490faf3ecdb5feb0e80d609b91","observation_id":"b3b20c69-2960-4595-b1f8-9f2e8cb91a65","resolution":{"observed_at":"2026-08-09T05:16:21.409922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T05:16:21.343833Z","title":"Trained transformers learn linear models in-context","venue":null,"work_id":"1501480c-634b-4ebe-b562-96dfe00ee676","year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.032213Z"},"links":{"citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:83264e60d969201125bdb87b4453afb7785730eb4eee58eeda81119ef8879f61","observation_id":"c428d000-6644-46e0-8aca-40f216afa3d1","resolution":{"observed_at":"2026-08-09T05:16:21.388303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14951","last_updated":"2024-02-22T20:26:08Z","snapshot_observed_at":"2026-07-06T17:34:16.065501Z","submitted_at":"2024-02-22T20:26:08Z","title":"In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14951","snapshot_observed_at":"2026-08-09T05:16:20.035595Z","title":"In-context learning of a linear transformer block: benefits of the mlp component and one-step gd initialization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-09T05:16:20.035595Z"},"links":{"cited_paper":"/paper/2402.14951","citing_paper":"/paper/2502.03327"},"observation_digest":"sha256:dc1a0643e8de131735506efd0052a6dedf8fc6eeab87e53a66474564c0bcb434","observation_id":"70821f6a-6323-4e15-b203-24cd5a429a5c","resolution":{"observed_at":"2026-08-09T05:16:20.035595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.03327","last_updated":"2025-02-05T16:22:46Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-09T11:14:21.353958Z","submitted_at":"2025-02-05T16:22:46Z","title":"Is In-Context Universality Enough? MLPs are Also Universal In-Context"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":8,"verified_fuzzy":39},"total_outbound_references":77},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 3 inbound Pith citation observations for arXiv:2502.03327."}