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Paper Citation Record · LEDGER

Transformers generalize differently from information stored in context vs in weights

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2210.05675.

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

pith.paper-citation-record.v1
2210.05675 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:57:22.619625Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:39:05.233326Z

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 3191c054-6843-42be-a1a1-84be8dbd1f98 · inbound

Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer cites this paper.

Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer Transformers generalize differently from information stored in context vs in weights

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T11:57:22.619625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:57:22.619625Z digest=sha256:5f42ac87e63bf1856781b1ca3c7063c67da8fe88bbd4bce5ff1bc95cd8ba15d9

Observation f2dc8e27-f690-4c76-9182-6213ac895bc4 · inbound

In-Context Iterative Policy Improvement for Dynamic Manipulation cites this paper.

In-Context Iterative Policy Improvement for Dynamic Manipulation Transformers generalize differently from information stored in context vs in weights

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T18:15:50.527032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:15:50.527032Z digest=sha256:4c4ecd65496eb7d748c9bef7ac0e4eebc8ca44b4f984abdbe08c0edf78a41944

Observation 07f225cd-e82c-419e-a5bb-cd98247736e9 · inbound

To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning cites this paper.

To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning Transformers generalize differently from information stored in context vs in weights

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T22:49:15.793055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-09T22:18:14.788882Z digest=sha256:8d67cbc2db0978d52b42188434bc57097189c5377da7e84807d94aca3f890807

Observation 0fe43193-b1fc-4672-9961-1f3eb17f24f6 · inbound

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior cites this paper.

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior Transformers generalize differently from information stored in context vs in weights

Reference 300

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:16:06.563360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-08T17:47:09.591001Z digest=sha256:71ba623d320a79c88d5bed3ab13dbd017bd45ebeed2cd9939d184b0a1b765f23

Observation c6e1149b-f689-4092-a9b9-723fef9295b9 · 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 Transformers generalize differently from information stored in context vs in weights

Reference 127

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:27:19.337224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-13T05:17:34.283917Z digest=sha256:8dede17245fb4a7c84d3cc18ae7501150d10a498b0831da57537986c60b5a93e

Observation 095840ca-8fd3-4a4d-ac67-ad66039fe297 · inbound

Learning Dynamics of Chain-of-Thought State Tracking in a Solvable Transformer Model cites this paper.

Learning Dynamics of Chain-of-Thought State Tracking in a Solvable Transformer Model Transformers generalize differently from information stored in context vs in weights

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:39:05.236068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T21:49:03.896740Z digest=sha256:8458acbe435f4f867c8ce138671ba69817ec9a70406024da61d75fa30abf81ab

Observation 9279d21e-b780-4e6c-845b-cf9e0849f244 · inbound

Input Pathways Shape Few-Shot, Not Zero-Shot, Binding in Tiny Transformers: A Fully-Enumerable Study cites this paper.

Input Pathways Shape Few-Shot, Not Zero-Shot, Binding in Tiny Transformers: A Fully-Enumerable Study Transformers generalize differently from information stored in context vs in weights

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-11T11:22:48.469230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T11:22:48.469230Z digest=sha256:f6d7a878c5c1daa1795884c555720af175868703f5afcc43a5ca920e79d5c2cc

Observation bfb0a00d-e7eb-4a97-a324-00c9d77f9b10 · inbound

Can a Language Model Learn Facts Continually in Its Weights? cites this paper.

Can a Language Model Learn Facts Continually in Its Weights? Transformers generalize differently from information stored in context vs in weights

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-14T07:36:43.499258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T07:36:43.499258Z digest=sha256:b8e0e3c2c1acfb9858f378862fa2c69dd3adf690682e11ba01af628e1a4bd143