Paper Citation Record · LEDGER

Quantifying Memory Utilization with Effective State-Size

As of 22 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 2 inbound Pith citation observations for arXiv:2504.19561.

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

pith.paper-citation-record.v1
2504.19561 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:58:23.967907Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T23:29:02.457697Z

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

79 of 79 outbound references displayed

verified exact6
verified fuzzy8
unresolved65
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation f3865c88-51f1-4e6c-b53b-6da7537a7bab · outbound

This paper cites write newline.

Quantifying Memory Utilization with Effective State-Size write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:21.806841Z

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Observation 5f31c8a7-7e4c-40e9-b9d9-8c6f4db37630 · outbound

This paper cites Quantifying Attention Flow in Transformers.

Quantifying Memory Utilization with Effective State-Size Quantifying Attention Flow in Transformers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:21.872146Z

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Observation 298aed03-ac8c-4dc4-889e-54ede1686ef4 · outbound

This paper cites Stochastic theory of minimal realization.

Quantifying Memory Utilization with Effective State-Size Stochastic theory of minimal realization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:21.973861Z

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Observation 9064800c-cd64-4f69-9002-fdc7e583b64a · outbound

This paper cites In-Context Language Learning: Architectures and Algorithms.

Quantifying Memory Utilization with Effective State-Size In-Context Language Learning: Architectures and Algorithms

Reference 4

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Observation f5f02e5e-d6c3-4e62-9b56-de612b6fdc71 · outbound

This paper cites The Hidden Attention of Mamba Models.

Quantifying Memory Utilization with Effective State-Size The Hidden Attention of Mamba Models

Reference 5

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Observation d5f50f86-bf7c-487d-8c0e-88e89bcdd8e6 · outbound

This paper cites Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws.

Quantifying Memory Utilization with Effective State-Size Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.011204Z

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Observation 3d6a5638-e8f3-4e0e-9d76-0c8b5cbfff94 · outbound

This paper cites When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards.

Quantifying Memory Utilization with Effective State-Size When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.018300Z

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Observation 18ce4659-008c-4b74-92c1-2df2f6a37e25 · outbound

This paper cites Zoology: Measuring and Improving Recall in Efficient Language Models.

Quantifying Memory Utilization with Effective State-Size Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.026407Z

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Observation 3a105404-ab9c-4a06-9e11-d843e60f241c · outbound

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

Quantifying Memory Utilization with Effective State-Size Simple linear attention language models balance the recall-throughput tradeoff

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.103593Z

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Observation 65e9fe45-6fdd-4cd8-b920-fc0bb3957590 · outbound

This paper cites Using Fast Weights to Attend to the Recent Past.

Quantifying Memory Utilization with Effective State-Size Using Fast Weights to Attend to the Recent Past

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.177814Z

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Observation ff4d40ff-54d4-429e-8e32-aed83ab148d3 · outbound

This paper cites an unresolved cited work.

Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 11

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a2c7354-354e-4573-b690-7d0f7a6de6e3 · outbound

This paper cites Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions.

Quantifying Memory Utilization with Effective State-Size Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.219962Z

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Observation 96bec6c7-381b-4510-b8ec-d540c0c4ab36 · outbound

This paper cites Low-Rank Bottleneck in Multi-head Attention Models.

Quantifying Memory Utilization with Effective State-Size Low-Rank Bottleneck in Multi-head Attention Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.225846Z

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Observation 508a8e6e-da07-422b-b029-35fcfc7f24c2 · outbound

This paper cites Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models.

Quantifying Memory Utilization with Effective State-Size Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.232550Z

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Observation fb8effd7-8e51-4462-96de-8dd11e260ddb · outbound

This paper cites an unresolved cited work.

Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 15

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Observation 3c48da62-3147-417e-9876-502b6b805360 · outbound

This paper cites Linear System Theory and Design.

Quantifying Memory Utilization with Effective State-Size Linear System Theory and Design

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:58:26.494105Z

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Observation 261229f5-e88b-448d-ba28-b325083b57c1 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Quantifying Memory Utilization with Effective State-Size Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 17

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Observation 0fa4d082-08dd-4cc4-90f1-f21da8c1df1d · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Quantifying Memory Utilization with Effective State-Size Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 18

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Observation a498db1f-0208-4a72-8b46-31452e6c3dfc · outbound

This paper cites and van der Veen, A.

Quantifying Memory Utilization with Effective State-Size and van der Veen, A

Reference 19

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad60ffc0-f7b3-41d0-92cd-98b69c53733d · outbound

This paper cites Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth.

Quantifying Memory Utilization with Effective State-Size Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.426444Z

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Observation a5a0b2d2-c0f0-49c4-9448-80fe8a58df4a · outbound

This paper cites Augmented Neural ODEs.

Quantifying Memory Utilization with Effective State-Size Augmented Neural ODEs

Reference 21

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Observation ce426964-76f1-42be-9e66-c9b7f49b228a · outbound

This paper cites A mathematical framework for transformer circuits.

Quantifying Memory Utilization with Effective State-Size A mathematical framework for transformer circuits

Reference 22

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Observation eca2b1a5-086e-49a4-8197-0b2c1877e5c8 · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

Quantifying Memory Utilization with Effective State-Size Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 23

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Observation f0efbf33-3db2-4fdc-8378-26176d198f94 · outbound

This paper cites and Juang, J.-N.

Quantifying Memory Utilization with Effective State-Size and Juang, J.-N

Reference 24

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Observation 3d0686c8-79b8-4cb1-ad91-0e6fd608ab52 · outbound

This paper cites Zamba: A Compact 7B SSM Hybrid Model.

Quantifying Memory Utilization with Effective State-Size Zamba: A Compact 7B SSM Hybrid Model

Reference 25

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Observation 8a8225d3-9185-4933-920d-6337f441e113 · outbound

This paper cites and Bengio, Y.

Quantifying Memory Utilization with Effective State-Size and Bengio, Y

Reference 26

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Observation 1475bf26-17eb-47ae-92fb-e5dfb8655a7b · outbound

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

Quantifying Memory Utilization with Effective State-Size Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.622868Z

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Observation 7fb5d34d-f589-4a61-bca9-853c28495e7b · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Quantifying Memory Utilization with Effective State-Size Efficiently Modeling Long Sequences with Structured State Spaces

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.673184Z

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Observation dc70b5e6-007d-49ff-8086-144bf1262750 · outbound

This paper cites On the Parameterization and Initialization of Diagonal State Space Models.

Quantifying Memory Utilization with Effective State-Size On the Parameterization and Initialization of Diagonal State Space Models

Reference 29

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Observation 8a9ab19c-dd64-46e9-8937-071553669d13 · outbound

This paper cites Liquid Structural State-Space Models.

Quantifying Memory Utilization with Effective State-Size Liquid Structural State-Space Models

Reference 30

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Observation b194dc0d-158c-49b2-bff5-b8bea726b8e6 · outbound

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Quantifying Memory Utilization with Effective State-Size Measuring Massive Multitask Language Understanding

Reference 31

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Observation bef55f7f-ad44-4685-8c89-8e8160ac18af · outbound

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Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 32

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Observation c9d1d56f-207f-4abc-a0cd-7e25b9afc12b · outbound

This paper cites and Schmidhuber, J.

Quantifying Memory Utilization with Effective State-Size and Schmidhuber, J

Reference 33

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Observation f84f8553-6cce-4013-b6d8-b7fef7d96a1c · outbound

This paper cites Training Compute-Optimal Large Language Models.

Quantifying Memory Utilization with Effective State-Size Training Compute-Optimal Large Language Models

Reference 34

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Observation ddc612ac-8d82-4907-b004-d9e130be441a · outbound

This paper cites Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention.

Quantifying Memory Utilization with Effective State-Size Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 35

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This paper cites 1D Convolutional Neural Networks and Applications: A Survey.

Quantifying Memory Utilization with Effective State-Size 1D Convolutional Neural Networks and Applications: A Survey

Reference 36

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This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Quantifying Memory Utilization with Effective State-Size Jamba: A Hybrid Transformer-Mamba Language Model

Reference 37

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Quantifying Memory Utilization with Effective State-Size and Picci, G

Reference 38

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 65af2bc4-31bc-49d6-a5a5-5c6669f14a24 · outbound

This paper cites System Identification: Theory for the User.

Quantifying Memory Utilization with Effective State-Size System Identification: Theory for the User

Reference 39

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Observation 6d394a66-5962-4f39-8feb-254a7b70d0b6 · outbound

This paper cites Decoupled Weight Decay Regularization.

Quantifying Memory Utilization with Effective State-Size Decoupled Weight Decay Regularization

Reference 40

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Observation c40e8716-c8c9-44ac-bbd8-a835db48af5f · outbound

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Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 41

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Observation e5299c07-5981-41fc-a822-bcdc62ff7054 · outbound

This paper cites Parallelizing Linear Recurrent Neural Nets Over Sequence Length.

Quantifying Memory Utilization with Effective State-Size Parallelizing Linear Recurrent Neural Nets Over Sequence Length

Reference 42

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Quantifying Memory Utilization with Effective State-Size Dissecting Neural ODEs

Reference 43

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Observation 0ca96cf8-82bc-4df1-b96d-593d8245269f · outbound

This paper cites Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions.

Quantifying Memory Utilization with Effective State-Size Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions

Reference 44

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Observation b701db1c-0598-449f-b425-c9eac04123b9 · outbound

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Quantifying Memory Utilization with Effective State-Size and Li, Z

Reference 45

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c02fdb83-5789-4a6c-a866-1ce6547e41cc · outbound

This paper cites In-context learning and induction heads.

Quantifying Memory Utilization with Effective State-Size In-context learning and induction heads

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:58:26.063376Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation deec9e76-8942-4e6b-9fd0-a9604f37eb5f · outbound

This paper cites In-context learning and induction heads.

Quantifying Memory Utilization with Effective State-Size In-context learning and induction heads

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:58:26.043474Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 44e62d16-26a3-4883-9bc1-55b03a56dc2c · outbound

This paper cites V., Willsky, A.

Quantifying Memory Utilization with Effective State-Size V., Willsky, A

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:58:26.023229Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f9f91c7-d205-4289-af5a-fff2cb27ab47 · outbound

This paper cites Resurrecting Recurrent Neural Networks for Long Sequences.

Quantifying Memory Utilization with Effective State-Size Resurrecting Recurrent Neural Networks for Long Sequences

Reference 49

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Observation cd2dbba4-bffb-4433-af02-39ecfc922253 · outbound

This paper cites State-Free Inference of State-Space Models: The Transfer Function Approach.

Quantifying Memory Utilization with Effective State-Size State-Free Inference of State-Space Models: The Transfer Function Approach

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.423047Z

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Observation 613a4d29-7125-4bc9-a04c-786f2a4ef442 · outbound

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Quantifying Memory Utilization with Effective State-Size On the difficulty of training Recurrent Neural Networks

Reference 51

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Observation 8e5da019-8ff4-471a-91e4-0ee41abf773d · outbound

This paper cites B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L.

Quantifying Memory Utilization with Effective State-Size B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:58:25.803767Z

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Observation bc1f9381-2996-4a02-ac97-a5b35ecc1a0d · outbound

This paper cites Hyena Hierarchy: Towards Larger Convolutional Language Models.

Quantifying Memory Utilization with Effective State-Size Hyena Hierarchy: Towards Larger Convolutional Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.487712Z

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Observation ffa4684d-7b11-429b-825f-1fcb7a786d67 · outbound

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Quantifying Memory Utilization with Effective State-Size Mechanistic Design and Scaling of Hybrid Architectures

Reference 54

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Quantifying Memory Utilization with Effective State-Size Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.501015Z

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Observation 2a336782-ea0f-49e6-ba49-e058633431ba · outbound

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Quantifying Memory Utilization with Effective State-Size Hopfield Networks is All You Need

Reference 56

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Observation def41f56-d776-49d4-9e1a-75cad3798479 · outbound

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Quantifying Memory Utilization with Effective State-Size CKConv: Continuous Kernel Convolution For Sequential Data

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.512479Z

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Observation fd95ceab-8711-48f3-8962-24d5af118457 · outbound

This paper cites and Vetterli, M.

Quantifying Memory Utilization with Effective State-Size and Vetterli, M

Reference 58

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Observation 7ee2914f-cd34-484c-9ce6-4f8c890b4bc6 · outbound

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Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:58:25.715553Z

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Observation 612338b2-607e-4530-b778-485fce9cfea5 · outbound

This paper cites GLU Variants Improve Transformer.

Quantifying Memory Utilization with Effective State-Size GLU Variants Improve Transformer

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.648977Z

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Observation 7a2383f9-cd7f-4fe5-81e8-32c8a9c2d700 · outbound

This paper cites Mutual Information Scaling and Expressive Power of Sequence Models.

Quantifying Memory Utilization with Effective State-Size Mutual Information Scaling and Expressive Power of Sequence Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.655825Z

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Observation 1ac35c73-3e6d-45d4-ad92-05a6c43fa1dc · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Quantifying Memory Utilization with Effective State-Size Simplified State Space Layers for Sequence Modeling

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.661406Z

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Observation 10b4f597-6b18-41f5-b30c-a52599f091cf · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Quantifying Memory Utilization with Effective State-Size RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.666605Z

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Observation eb2872e6-71dd-45b3-9642-d1d671307c1f · outbound

This paper cites Massive Activations in Large Language Models.

Quantifying Memory Utilization with Effective State-Size Massive Activations in Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.672217Z

Source-reported events for the cited work

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Observation 916e566a-e3a5-45fc-975c-95e5c438c179 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Quantifying Memory Utilization with Effective State-Size Retentive Network: A Successor to Transformer for Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.677916Z

Source-reported events for the cited work

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Observation 195ec2fb-f755-498c-a6fc-0fd18c5561f3 · outbound

This paper cites Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel.

Quantifying Memory Utilization with Effective State-Size Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.696291Z

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Observation 5230f357-d0be-4f57-9183-ecbcdc9edac3 · outbound

This paper cites Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control.

Quantifying Memory Utilization with Effective State-Size Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:58:24.551573Z

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Observation d7bfa6e8-c24e-4718-a1b3-c395e9039dc9 · outbound

This paper cites A note on the representation and definition of semiseparable matrices.

Quantifying Memory Utilization with Effective State-Size A note on the representation and definition of semiseparable matrices

Reference 68

Resolution: verified exact; doi, observed 2026-08-16T05:58:24.128224Z

Source-reported events for the cited work

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Observation 58be5d28-975b-4aef-b32c-de51288e90e3 · outbound

This paper cites Attention Is All You Need.

Quantifying Memory Utilization with Effective State-Size Attention Is All You Need

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.759783Z

Source-reported events for the cited work

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Observation eeed8cd6-1070-4933-8ec9-f1d2ee30fe35 · outbound

This paper cites A Multiscale Visualization of Attention in the Transformer Model.

Quantifying Memory Utilization with Effective State-Size A Multiscale Visualization of Attention in the Transformer Model

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.764858Z

Source-reported events for the cited work

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Observation 5c40f325-ab1c-43cd-b1cd-395552181fc6 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Quantifying Memory Utilization with Effective State-Size MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.770327Z

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Observation 0a16110f-ec29-4775-8915-00f9691f6cca · outbound

This paper cites an unresolved cited work.

Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 72

Resolution: verified exact; doi, observed 2026-08-16T05:58:24.081460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1250d4d3-a5cf-488c-abe9-9202463c615f · outbound

This paper cites On the Role of Attention Masks and LayerNorm in Transformers.

Quantifying Memory Utilization with Effective State-Size On the Role of Attention Masks and LayerNorm in Transformers

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.817295Z

Source-reported events for the cited work

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Observation a8963cd2-3dd8-42b7-b437-bbbf794c0e6c · outbound

This paper cites an unresolved cited work.

Quantifying Memory Utilization with Effective State-Size Unresolved cited work

Reference 74

Resolution: verified exact; doi, observed 2026-08-16T05:58:24.034342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 96c3ba13-cf54-4b5d-9691-ca18fa0fe6b7 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Quantifying Memory Utilization with Effective State-Size Efficient Streaming Language Models with Attention Sinks

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.937422Z

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Observation 8143ca89-bab3-4c22-8c0b-c0944d830e44 · outbound

This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

Quantifying Memory Utilization with Effective State-Size Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.950981Z

Source-reported events for the cited work

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Observation 362368b0-844d-49ac-a7cc-11f3c5848b16 · outbound

This paper cites Parallelizing Linear Transformers with the Delta Rule over Sequence Length.

Quantifying Memory Utilization with Effective State-Size Parallelizing Linear Transformers with the Delta Rule over Sequence Length

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.957318Z

Source-reported events for the cited work

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Observation 7b6012e4-1cf1-4de0-a39c-cc11c3230ad2 · outbound

This paper cites B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory.

Quantifying Memory Utilization with Effective State-Size B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.962479Z

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Observation 67b59a98-9901-4bee-9643-75bf500aef43 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Quantifying Memory Utilization with Effective State-Size HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:23.967907Z

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Pith citing papers

Observation 55afe2d5-25e1-4f90-87ab-9897891338d2 · inbound

Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences cites this paper.

Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences Quantifying Memory Utilization with Effective State-Size

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:19:46.619416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5350e92b-5a6f-44dd-b27b-93e712158e70 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Quantifying Memory Utilization with Effective State-Size

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:32:46.743562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.