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Paper Citation Record · LEDGER

Flowformer: Linearizing Transformers with Conservation Flows

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.06258.

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

pith.paper-citation-record.v1
2202.06258 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 2026-08-04T06:34:03.388597+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T23:44:26.734234Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:42:49.737714Z

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 ff9b370d-d580-427c-b507-8bbbfbea847f · inbound

RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics cites this paper.

RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics Flowformer: Linearizing Transformers with Conservation Flows

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:11:04.306747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-09T20:38:39.281863Z digest=sha256:4334d2ac15bd9db9457a352b9fbf92c3745b85f58f070fb54f774113e1d6728b

Observation f7b9d6c0-05db-43e9-8a86-5c6786d3f387 · inbound

ChurnNet: A Optimized Modern AI for Churn Prediction cites this paper.

ChurnNet: A Optimized Modern AI for Churn Prediction Flowformer: Linearizing Transformers with Conservation Flows

Reference 36

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T23:42:49.739027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-06-28T23:35:17.504844Z digest=sha256:5d1bfe286b282ed31d4da8835fefbea447380d62c0d7d7cc90064e19f2cf4508

Observation 4997c824-a770-41a5-bdb0-8121d1c20bae · inbound

LLT: Local Linear Transformer for PDE Operator Learning cites this paper.

LLT: Local Linear Transformer for PDE Operator Learning Flowformer: Linearizing Transformers with Conservation Flows

Reference 58

Resolution
unresolved
no resolver link, observed 2026-07-11T23:44:26.734234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T23:44:26.734234Z digest=sha256:13a0b4f4c2446ffb83f775379f7b4b8254527853581059fd7bc05f3d2f6bd180