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Paper Citation Record · LEDGER

Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.01868.

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

pith.paper-citation-record.v1
2503.01868 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:29:02.110682Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:46:31.161003Z

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 f68bc3b7-2d58-4537-b05b-2049462507a3 · inbound

Exploring Diffusion Transformer Designs via Grafting cites this paper.

Exploring Diffusion Transformer Designs via Grafting Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T10:29:02.110682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:29:02.110682Z digest=sha256:0efd2a50232d53d476cd4d3a0e27b571c9d8a7fe7675048f10fcb3d59c85d6ae

Observation 0a45b6e6-2076-4106-a871-08beef6aac9b · inbound

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences cites this paper.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T04:36:20.539070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:36:20.539070Z digest=sha256:01a7d7f3079ce40c4d6b47c6393f408f85a9eb12a8921e469ddb807e9309f8d0

Observation ec345752-9761-472f-9b57-9f0a80de8e03 · inbound

In Search of Lost DNA Sequence Pretraining cites this paper.

In Search of Lost DNA Sequence Pretraining Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-10T08:53:03.590468Z

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=2026-05-10T08:52:47.515343Z digest=sha256:626a9fb68e248176a56a347070af818fea0d82df8151c3e55801e42911af1862

Observation 991bbd09-2d3e-47df-98c6-f11256a270bc · inbound

Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation cites this paper.

Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:46:31.257232Z

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=2026-05-12T04:01:05.492969Z digest=sha256:d2a12be1403aaf240acce648a3cd5cf341e8a971ef389bb69a9d0a2fba2000f0

Observation dc47cc7e-0ac2-41f7-8052-6854f104bda1 · inbound

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions cites this paper.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-02T09:17:43.968620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:17:43.968620Z digest=sha256:6f1827b3871faff7fabc8c3fbdcd69abd0426133622cf9e475ccc222e8e9d9a4

Observation 84566fb7-414f-4f06-bbdf-6b75fe2f65fa · inbound

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions cites this paper.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-03T02:08:16.871787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:08:16.871787Z digest=sha256:e2bd186c03676574acd8499498fc546cbac536f5e81256ce62e2ba717160014a