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Paper Citation Record · LEDGER

Transformer Dynamics: A neuroscientific approach to interpretability of large language models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.12131.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.12131 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:18:48.467880Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T02:38:35.899177Z

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 01c0ca09-42ff-4c1f-9dd9-2516a431498e · inbound

Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space cites this paper.

Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space Transformer Dynamics: A neuroscientific approach to interpretability of large language models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:26:01.708868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:01:25.309345Z digest=sha256:0738349eab4e1dd53db1a857137aa2e17b15d961be19bb4d7b3ff3af73bee68c

Observation cd15ccd1-ded0-419b-9989-bc888f0a4a33 · inbound

Dynamics of the Transformer Residual Stream: Coupling Spectral Geometry to Network Topology cites this paper.

Dynamics of the Transformer Residual Stream: Coupling Spectral Geometry to Network Topology Transformer Dynamics: A neuroscientific approach to interpretability of large language models

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:38:35.903497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-15T02:37:19.354739Z digest=sha256:fde190650ea5842628c135dcb2f7deb93e389a53aba59ebc631838ca0fc48639

Observation 6c7d9b7b-acdd-4df4-bb8b-42cb9ba56a58 · inbound

An Analysis of Residual-Stream Geometry Across Transformer Depth cites this paper.

An Analysis of Residual-Stream Geometry Across Transformer Depth Transformer Dynamics: A neuroscientific approach to interpretability of large language models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-01T17:18:48.467880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:18:48.467880Z digest=sha256:021b8fabecc60c0e110ac7fd4d2c10c1cb92db1fc75af6b0c14e3c92b47f955e