Paper Citation Record · LEDGER

Dynamic Linear Attention

As of 15 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2606.10650.

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

pith.paper-citation-record.v1
2606.10650 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T13:18:18.331031Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact8
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf4af32d-1f4e-4508-8baf-1ea91200213a · outbound

This paper cites Efficient Large Language Models: A Survey.

Dynamic Linear Attention Efficient Large Language Models: A Survey

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.795371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6bd91c5c-615c-4edd-a806-dfd06e9a4ac7 · outbound

This paper cites The Internet of Things in the Era of Generative AI: Vision and Challenges.

Dynamic Linear Attention The Internet of Things in the Era of Generative AI: Vision and Challenges

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.803407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a77c7bc3-5ca6-49e7-bf5f-1bfed029da4d · outbound

This paper cites D2O: dynamic discriminative operations for efficient long-context inference of large language models.

Dynamic Linear Attention D2O: dynamic discriminative operations for efficient long-context inference of large language models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36fbe125-92d3-45c4-b5cd-8fae184dd0c1 · outbound

This paper cites Parallelizing linear transformers with the delta rule over sequence length.

Dynamic Linear Attention Parallelizing linear transformers with the delta rule over sequence length

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 625553e2-6a4d-4c38-90ce-896250d76c28 · outbound

This paper cites Gated delta networks: Improving mamba2 with delta rule.

Dynamic Linear Attention Gated delta networks: Improving mamba2 with delta rule

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90562a5c-5c05-45f2-9c6c-b8e5dfd096e9 · outbound

This paper cites Log-linear attention.

Dynamic Linear Attention Log-linear attention

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.805845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff44463c-5200-4e82-9e62-0fdd9a95fc3a · outbound

This paper cites Rat: Bridging rnn efficiency and attention accuracy via chunk-based sequence modeling.arXiv preprint arXiv:2507.04416.

Dynamic Linear Attention Rat: Bridging rnn efficiency and attention accuracy via chunk-based sequence modeling.arXiv preprint arXiv:2507.04416

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.808242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54974d42-43be-4111-bf3a-8fdeb80fc1b2 · outbound

This paper cites Generalizing the Cauchy-Schwarz inequality: Hadamard powers and tensor products.

Dynamic Linear Attention Generalizing the Cauchy-Schwarz inequality: Hadamard powers and tensor products

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.811665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4773cfe9-f931-43a1-a442-9665796f4bfd · outbound

This paper cites Kandula, S.

Dynamic Linear Attention Kandula, S

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T13:20:56.498542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 016d5bc2-a160-47d3-b5e8-d97d950a6ea2 · outbound

This paper cites Transformers are ssms: Generalized models and efficient algorithms through structured state space duality.

Dynamic Linear Attention Transformers are ssms: Generalized models and efficient algorithms through structured state space duality

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90d8747d-add1-4450-8852-d819d4bc853f · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

Dynamic Linear Attention The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fff127f-341d-4211-91a6-71ebb0b55dfb · outbound

This paper cites PIQA: reasoning about physical commonsense in natural language.

Dynamic Linear Attention PIQA: reasoning about physical commonsense in natural language

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84aabda0-5d25-4bef-80aa-bebdb09d8771 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InACL (1), pages 4791–4800.

Dynamic Linear Attention Hellaswag: Can a machine really finish your sentence? InACL (1), pages 4791–4800

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06cfc6b1-ffe1-490e-8a13-5b680f82e7e3 · outbound

This paper cites Winogrande: an adversarial winograd schema challenge at scale.Commun.

Dynamic Linear Attention Winogrande: an adversarial winograd schema challenge at scale.Commun

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adfc1108-4f06-4c79-8f2c-de0b5bff0f16 · outbound

This paper cites Can a suit of armor conduct electricity? A new dataset for open book question answering.

Dynamic Linear Attention Can a suit of armor conduct electricity? A new dataset for open book question answering

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 053248d3-f82e-4270-adb1-324ff19527fc · outbound

This paper cites Commonsenseqa: A question answering challenge targeting commonsense knowledge.

Dynamic Linear Attention Commonsenseqa: A question answering challenge targeting commonsense knowledge

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation badf0a94-f75a-4475-b5d9-a3f734903f01 · outbound

This paper cites Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge.

Dynamic Linear Attention Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.814497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6bf6d3b0-eaf7-446c-81c3-bee219b26ccd · outbound

This paper cites Openceres: When open information extraction meets the semi-structured web.

Dynamic Linear Attention Openceres: When open information extraction meets the semi-structured web

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f905f97-44c3-448a-9fce-3133cf7905a8 · outbound

This paper cites Know what you don’t know: Unanswerable questions for squad.

Dynamic Linear Attention Know what you don’t know: Unanswerable questions for squad

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cc1bd67-358b-40c3-9db4-da6f16386fab · outbound

This paper cites Language models enable simple systems for generating structured views of heterogeneous data lakes.

Dynamic Linear Attention Language models enable simple systems for generating structured views of heterogeneous data lakes

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3937c890-a687-4f70-a02c-8708638c0379 · outbound

This paper cites Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension.

Dynamic Linear Attention Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f65e2be-a459-4b7e-810f-645333d6221f · outbound

This paper cites Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs.

Dynamic Linear Attention Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5938956f-fd71-4e2e-9b68-2d2a1a3a9879 · outbound

This paper cites Natural questions: a benchmark for question answering research.

Dynamic Linear Attention Natural questions: a benchmark for question answering research

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd79f933-d1d7-46da-b0f9-7c3e2fb5e1b8 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Dynamic Linear Attention RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-03T05:27:39.809094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 447853f9-fd8a-48e1-a7f1-9952f0184784 · outbound

This paper cites Longbench: A bilingual, multitask benchmark for long context understanding.

Dynamic Linear Attention Longbench: A bilingual, multitask benchmark for long context understanding

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 583c6f59-8681-4a98-a4e0-24f35b1a4312 · outbound

This paper cites He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C.

Dynamic Linear Attention He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:27:39.813686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b64b2ac2-527e-4c50-9608-7eccaacb0934 · outbound

This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Dynamic Linear Attention Simple linear attention language models balance the recall-throughput tradeoff

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T13:18:18.331031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe41f40b-c4eb-4e33-9c66-88efee403781 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Dynamic Linear Attention Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-03T05:27:39.806518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.