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Paper Citation Record · LEDGER

Quantifying Context Mixing in Transformers

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2301.12971.

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

pith.paper-citation-record.v1
2301.12971 v2

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 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:08:44.446206Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:02:17.757215Z

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 6415500a-423c-4bb5-a8d1-30dc0664fdb0 · inbound

What are you sinking? A geometric approach on attention sink cites this paper.

What are you sinking? A geometric approach on attention sink Quantifying Context Mixing in Transformers

Reference 2024

Resolution
verified exact
local_arxiv, observed 2026-08-06T05:02:17.761767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T05:02:17.462027Z digest=sha256:a41de67e5201ce312aa27a50139d6bf6c1fddd4fb62e1c742b0eaa34fc49ecb5

Observation f925204b-5347-4b71-ab0d-0109b80b9c82 · inbound

Do All Autoregressive Transformers Remember Facts the Same Way? A Cross-Architecture Analysis of Recall Mechanisms cites this paper.

Do All Autoregressive Transformers Remember Facts the Same Way? A Cross-Architecture Analysis of Recall Mechanisms Quantifying Context Mixing in Transformers

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T16:08:44.446206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:08:44.446206Z digest=sha256:2f11a9d891b3784ed39186cd76cae5da36298a6a75f1e4af03aeb607e25a097b

Observation cad39e25-6743-4b57-b5a0-c76f5dcc6e43 · inbound

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization cites this paper.

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization Quantifying Context Mixing in Transformers

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-04T09:48:07.726871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T09:48:07.726871Z digest=sha256:15ac361127047c236e1c776a16c9425f8d22be9defc4974bac773726288fc37d