Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Is In-Context Universality Enough? MLPs are Also Universal In-Context
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.03327.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:12:59.142441Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T17:51:09.301070Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 176dbf16-26da-40ad-8009-8b4d397fc23b · inbound
Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data cites this paper.
Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data Is In-Context Universality Enough? MLPs are Also Universal In-Context
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:12:59.142441Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb347cb6-80a6-497b-b45a-f7c4fa54372e · inbound
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off cites this paper.
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off Is In-Context Universality Enough? MLPs are Also Universal In-Context
Reference 2008
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T13:15:25.486024Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07a816b1-f254-4ad2-8a7d-725c13fe7c22 · inbound
Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning cites this paper.
Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning Is In-Context Universality Enough? MLPs are Also Universal In-Context
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:51:09.303503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.