Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.00795.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:41.978539Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T10:37:15.095733Z
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 141c497c-ec6f-4839-9517-2789defe32bc · inbound
Pre-trained Large Language Models Learn Hidden Markov Models In-context cites this paper.
Pre-trained Large Language Models Learn Hidden Markov Models In-context LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T10:37:15.097352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation dbc18ce7-6eab-44d1-8575-1b3dc37799d9 · inbound
Large Language Models and Emergence: A Complex Systems Perspective cites this paper.
Large Language Models and Emergence: A Complex Systems Perspective LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:57:41.978539Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20c55531-18ea-4d61-98ff-0dae234b7f9d · inbound
Deficiency of equation-finding approach to data-driven modeling of dynamical systems cites this paper.
Deficiency of equation-finding approach to data-driven modeling of dynamical systems LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:45:10.483065Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9a2bb86-e98f-46d8-884c-b3bd740b5f24 · inbound
Can Transformers predict system collapse in dynamical systems? cites this paper.
Can Transformers predict system collapse in dynamical systems? LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
Reference 64
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T10:41:31.526949Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a6f458f0-213d-4e3b-bccf-f23dc8936976 · inbound
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space cites this paper.
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T05:27:19.346230Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.