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Paper Citation Record · LEDGER

Contextual Position Encoding: Learning to Count What's Important

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.18719.

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

pith.paper-citation-record.v1
2405.18719 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:06:51.778667Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4ba504fe-728d-40fd-8a7d-1db0f76bff5d · inbound

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data cites this paper.

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data Contextual Position Encoding: Learning to Count What's Important

Reference 187

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:35:02.177667Z

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=2026-05-20T13:35:02.018244Z digest=sha256:1b39b7ebe91b96c678b123da53f972d472006c862a87f6b4b8392502ded1ab49

Observation 74ec3da2-2372-435f-abca-c679708f39b8 · inbound

A Contextual-Aware Position Encoding for Sequential Recommendation cites this paper.

A Contextual-Aware Position Encoding for Sequential Recommendation Contextual Position Encoding: Learning to Count What's Important

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T22:56:55.667281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T22:56:55.667281Z digest=sha256:c4814818decd94f0d366d76002ec16637d98eb56d85b3bc8b1efb4dca2041ad4

Observation 723c52f5-ffcc-491a-8f30-bc2fb20c0fbf · inbound

Sample Complexity and Representation Ability of Test-time Scaling Paradigms cites this paper.

Sample Complexity and Representation Ability of Test-time Scaling Paradigms Contextual Position Encoding: Learning to Count What's Important

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T10:35:33.813693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:35:33.813693Z digest=sha256:5956f01497870d36a3b52a7e1823e95e5236f8e3c185bc35c2a5b3cb2d95fc4a

Observation 0210458f-18aa-4a5f-afe7-dc7ce4437b7b · inbound

Latent Multi-Head Attention for Small Language Models cites this paper.

Latent Multi-Head Attention for Small Language Models Contextual Position Encoding: Learning to Count What's Important

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T04:57:07.482951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:57:07.482951Z digest=sha256:0edc43a4cf04aa959b60f48dbedf7bca71cc10023fe00e51eb9431e26e29513e

Observation 2ca71b20-706f-4a85-a497-7f6496e1cb11 · inbound

HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models cites this paper.

HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models Contextual Position Encoding: Learning to Count What's Important

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T05:36:53.380335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:36:53.380335Z digest=sha256:ed152e24484cd29c669edf77eb92a5c2348b701be08612f9e425f029132af2b8

Observation 38119b0b-e579-46c4-8659-be7f48197928 · inbound

Group Representational Position Encoding cites this paper.

Group Representational Position Encoding Contextual Position Encoding: Learning to Count What's Important

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-17T00:08:43.762670Z

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=2026-05-17T00:04:13.707931Z digest=sha256:ac9d69c1af58b351995ff66c5ea9bb9998cbcaa2f1aafb493a44e77b03c45fda

Observation 20a862ee-16be-45eb-8d83-5f3304c5e473 · inbound

Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings cites this paper.

Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings Contextual Position Encoding: Learning to Count What's Important

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T21:20:51.877914Z

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=2026-05-10T16:50:43.716813Z digest=sha256:72a3b4ea6e2d51397d7bb33e8bab4ac641c749d71d535a0fcaf22bb985f5f102

Observation bd9e3a89-5a5c-4d6a-91de-e68dbff3f2f5 · inbound

Hypothesis generation and updating in large language models cites this paper.

Hypothesis generation and updating in large language models Contextual Position Encoding: Learning to Count What's Important

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-08T21:14:12.639564Z

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=2026-05-08T14:43:56.125546Z digest=sha256:8e2af651757092a03efc233a17e32b0990caec25f48a4421dfa4201a1a7e87f8

Observation a4b91b6c-4f1e-496b-9b3d-cc275594ff55 · inbound

Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders cites this paper.

Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders Contextual Position Encoding: Learning to Count What's Important

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-06-29T07:33:13.050739Z

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=2026-06-29T07:24:41.424801Z digest=sha256:c54861b170f33ba2e33991b768564c2c6493b38d80aca295f7c017099581e93d

Observation e3736da0-d508-4182-964d-a81e8e3f08e5 · inbound

Addressable Memory for Video World Models cites this paper.

Addressable Memory for Video World Models Contextual Position Encoding: Learning to Count What's Important

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T05:06:51.778667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:06:51.778667Z digest=sha256:a99f0ad683fa42507b5fe919b1eda1d1e8d93f6d1727684423b1970d8a657d9c