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Paper Citation Record · LEDGER

Block-Biased Mamba for Long-Range Sequence Processing

As of 18 August 2026, this Paper Citation Record lists 100 of 108 outbound references and 3 inbound Pith citation observations for arXiv:2505.09022.

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

pith.paper-citation-record.v1
2505.09022 v1

Coverage vector

measured 100 of 108 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:49:30.598041Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:22:53.184571Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:56:26.787347Z

Reference resolution

100 of 108 outbound references displayed

  • verified exact5
  • verified fuzzy30
  • unresolved65
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89741f7e-8577-4a0e-a53f-0ffd725f82da · outbound

This paper cites Spectral State Space Models.

Block-Biased Mamba for Long-Range Sequence Processing Spectral State Space Models

Reference 1

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Observation e6bed80c-4277-4a5e-8700-9801a0cc4237 · outbound

This paper cites Learning and generalization in overpa- rameterized neural networks, going beyond two layers.

Block-Biased Mamba for Long-Range Sequence Processing Learning and generalization in overpa- rameterized neural networks, going beyond two layers

Reference 2

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Observation 6d7d45f0-c224-4568-965e-ad6856f08653 · outbound

This paper cites State Space Models as Foundation Models: A Control Theoretic Overview.

Block-Biased Mamba for Long-Range Sequence Processing State Space Models as Foundation Models: A Control Theoretic Overview

Reference 3

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Observation 5ad30b87-444c-4fb0-a5c0-f14cf332f477 · outbound

This paper cites Towards better understanding of gradient-based attribution methods for Deep Neural Networks.

Block-Biased Mamba for Long-Range Sequence Processing Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Reference 4

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Observation 7a95fe0e-2f97-415a-af82-7d9d396be232 · outbound

This paper cites Intrinsic dimension of data representations in deep neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Intrinsic dimension of data representations in deep neural networks

Reference 5

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Observation b1d0bb3f-9505-4578-a669-88c29fc43e7d · outbound

This paper cites Unitary evolution recurrent neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Unitary evolution recurrent neural networks

Reference 6

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Observation 1136295c-d80c-4bc5-a786-7914580cde1b · outbound

This paper cites Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks

Reference 7

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Observation 847d7662-8703-49c8-8b83-aa8a47328da2 · outbound

This paper cites How to explain individual classification decisions.

Block-Biased Mamba for Long-Range Sequence Processing How to explain individual classification decisions

Reference 8

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Observation 057a9632-dbe1-4a8b-8e8f-71f54da692cd · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Block-Biased Mamba for Long-Range Sequence Processing An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 9

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Observation 3159bb4a-4f79-4512-b9e8-1f66e7243223 · outbound

This paper cites Deep learning: a statistical viewpoint.

Block-Biased Mamba for Long-Range Sequence Processing Deep learning: a statistical viewpoint

Reference 10

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Observation a878fc1d-2b3d-499f-8407-289d1b880459 · outbound

This paper cites The convergence rate of neural networks for learned functions of different frequencies.

Block-Biased Mamba for Long-Range Sequence Processing The convergence rate of neural networks for learned functions of different frequencies

Reference 11

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Observation 2abb0996-3904-4825-b787-9dc39954fbce · outbound

This paper cites Fast convolution algorithm for state space models.

Block-Biased Mamba for Long-Range Sequence Processing Fast convolution algorithm for state space models

Reference 12

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Observation e4a50004-7131-45e5-a1cf-c40499f88f24 · outbound

This paper cites Antisymmetricrnn: A dynamical system view on recurrent neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Antisymmetricrnn: A dynamical system view on recurrent neural networks

Reference 13

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Observation c1350f73-d353-4a9b-9a57-3e09dc4c21a0 · outbound

This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

Block-Biased Mamba for Long-Range Sequence Processing Entropy-sgd: Biasing gradient descent into wide valleys

Reference 14

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Observation eec1d35d-1723-4fae-9cbc-90aaa5c703f0 · outbound

This paper cites Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.

Block-Biased Mamba for Long-Range Sequence Processing Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Reference 15

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Observation dcd418ef-973f-450d-aa16-34f0ddac6df9 · outbound

This paper cites The loss surfaces of multilayer networks.

Block-Biased Mamba for Long-Range Sequence Processing The loss surfaces of multilayer networks

Reference 16

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Observation 270139dc-70e4-4a43-b982-5f4c30a43950 · outbound

This paper cites Rethinking attention with performers.

Block-Biased Mamba for Long-Range Sequence Processing Rethinking attention with performers

Reference 17

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Observation 40eff04e-482b-4928-b65c-dd1dae845cb2 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

Block-Biased Mamba for Long-Range Sequence Processing Approximation by superpositions of a sigmoidal function

Reference 18

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Observation b9c2eb3a-926d-48ee-8cb5-54bd00a4d862 · outbound

This paper cites Self-stabilization: The implicit bias of gra- dient descent at the edge of stability.International Conference on Learning Representations, 2023.

Block-Biased Mamba for Long-Range Sequence Processing Self-stabilization: The implicit bias of gra- dient descent at the edge of stability.International Conference on Learning Representations, 2023

Reference 19

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Observation 741873d2-7cbd-437f-b909-1a1fd5219d90 · outbound

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

Block-Biased Mamba for Long-Range Sequence Processing Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 20

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Observation b497f41c-0f78-4a83-afb8-98f65d3d3ac5 · outbound

This paper cites Identifying and attacking the saddle point problem in high-dimensional non-convex optimization.

Block-Biased Mamba for Long-Range Sequence Processing Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

Reference 21

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Observation 4d6baecd-d998-44ec-9a2d-095bafead285 · outbound

This paper cites Fast fourier transforms for nonequispaced data.

Block-Biased Mamba for Long-Range Sequence Processing Fast fourier transforms for nonequispaced data

Reference 22

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Observation aef937b6-c8b0-45fc-bc4e-822731f6285a · outbound

This paper cites Lipschitz recurrent neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Lipschitz recurrent neural networks

Reference 23

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Observation fc673393-d64d-4f15-be83-8b72f706cf72 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Understanding the difficulty of training deep feedforward neural networks

Reference 24

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Observation db2f2bb4-96ca-47c0-83a0-af95e65b2e10 · outbound

This paper cites Unlocking state-tracking in linear rnns through negative eigenvalues.

Block-Biased Mamba for Long-Range Sequence Processing Unlocking state-tracking in linear rnns through negative eigenvalues

Reference 25

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Observation 8c7f0265-346c-48cf-9f4f-6299441c611d · outbound

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

Block-Biased Mamba for Long-Range Sequence Processing Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 26

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Observation d813169c-49c8-4bcc-956f-1b903609a623 · outbound

This paper cites On the parameterization and initialization of diagonal state space models.

Block-Biased Mamba for Long-Range Sequence Processing On the parameterization and initialization of diagonal state space models

Reference 27

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Observation f7078c03-fd72-485b-a437-e2c8a7d3723e · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Block-Biased Mamba for Long-Range Sequence Processing Efficiently modeling long sequences with structured state spaces

Reference 28

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Observation d42e3001-6d14-4238-8f0f-5238c85e2b52 · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces.

Block-Biased Mamba for Long-Range Sequence Processing Diagonal state spaces are as effective as structured state spaces

Reference 29

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Observation 7639d05e-b1bb-42c6-90d1-2ab9df7ca533 · outbound

This paper cites Liquid structural state-space models.

Block-Biased Mamba for Long-Range Sequence Processing Liquid structural state-space models

Reference 30

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Observation d06cc120-e07c-4b12-ad84-a2df7833b0c6 · outbound

This paper cites Deep residual learning for image recognition.

Block-Biased Mamba for Long-Range Sequence Processing Deep residual learning for image recognition

Reference 31

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Observation e8a9c5d3-9111-4c7d-9cc7-7d191f6f64c6 · outbound

This paper cites Generalization error analysis for selective state-space models through the lens of attention.

Block-Biased Mamba for Long-Range Sequence Processing Generalization error analysis for selective state-space models through the lens of attention

Reference 32

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Observation cc921ff7-a238-4547-81db-2d0e9d26fd2e · outbound

This paper cites State-space models are accurate and efficient neural operators for dynamical systems.

Block-Biased Mamba for Long-Range Sequence Processing State-space models are accurate and efficient neural operators for dynamical systems

Reference 33

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Observation c5dfd83a-963e-4e97-8e63-5ac193424d25 · outbound

This paper cites Hydra: Bidirectional state space models through generalized matrix mixers.

Block-Biased Mamba for Long-Range Sequence Processing Hydra: Bidirectional state space models through generalized matrix mixers

Reference 34

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Observation 8ce413f9-499e-495d-a5dc-6b7cd4c66eff · outbound

This paper cites Jacot, F.

Block-Biased Mamba for Long-Range Sequence Processing Jacot, F

Reference 35

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Observation 96fec00f-fae4-44e9-b48c-0829aa476fe5 · outbound

This paper cites A new approach to linear filtering and prediction problems.

Block-Biased Mamba for Long-Range Sequence Processing A new approach to linear filtering and prediction problems

Reference 36

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Observation da0c45ef-4184-41c0-a672-1426abe41d00 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Block-Biased Mamba for Long-Range Sequence Processing Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 37

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Observation 4e7064ee-fcf6-448a-bc77-0f5d37531f17 · outbound

This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Block-Biased Mamba for Long-Range Sequence Processing On large-batch training for deep learning: Generalization gap and sharp minima

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.511394Z

Source-reported events for the cited work

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Observation 059d51e2-fdf9-466e-8f4c-7983e15aa50e · outbound

This paper cites Universal Approximation with Deep Narrow Networks.

Block-Biased Mamba for Long-Range Sequence Processing Universal Approximation with Deep Narrow Networks

Reference 39

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source=pdf_text observed=2026-08-15T21:49:30.383291Z digest=sha256:2a2554893c02ca58d345e5a4afe36680adf5a394a843cf9d28d6a9db29c65e4c

Observation d7d46c35-aae2-4c7e-9eca-2e1fee4e3112 · outbound

This paper cites Exploring the loss landscape of regularized neural networks via convex duality.

Block-Biased Mamba for Long-Range Sequence Processing Exploring the loss landscape of regularized neural networks via convex duality

Reference 40

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raw_fallback, observed 2026-08-15T21:49:31.499329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.387241Z digest=sha256:1fd2a092ee9567907a826a420c0785fe9b0b52a16599ed57b77762e0fe7738af

Observation ed287507-808b-4750-a7ea-07cb704c9390 · outbound

This paper cites Reformer: The efficient transformer.

Block-Biased Mamba for Long-Range Sequence Processing Reformer: The efficient transformer

Reference 41

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raw_fallback, observed 2026-08-15T21:49:31.489576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.390996Z digest=sha256:9668c1df4e33e5a366dbc021336dd913d839cde9930992db0565eaa9d0fbf56e

Observation bc839248-8138-43a2-90fe-9faa78606599 · outbound

This paper cites Visualizing the loss landscape of neural nets.

Block-Biased Mamba for Long-Range Sequence Processing Visualizing the loss landscape of neural nets

Reference 42

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no resolver link, observed 2026-08-15T21:49:30.394447Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.394447Z digest=sha256:e9829dec9346b02b13a81e583ce734f704af8f7736985d47c47eb4f618e0b0db

Observation d9e0101f-8494-4dca-b333-c61317a3dbf3 · outbound

This paper cites SPMamba: State-space model is all you need in speech separation.

Block-Biased Mamba for Long-Range Sequence Processing SPMamba: State-space model is all you need in speech separation

Reference 43

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.398032Z digest=sha256:297d090dbc1100ff51e634592be16b97bfc01a50eea918eec76f8305149014b3

Observation b739569f-98fe-437b-9e00-a89d31eab676 · outbound

This paper cites Videomamba: State space model for efficient video understanding.

Block-Biased Mamba for Long-Range Sequence Processing Videomamba: State space model for efficient video understanding

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.472372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.401630Z digest=sha256:7b56315728bea08a5aa1afcb848e8ee172de8420c1275152cd526578bb63fc69

Observation fe558ccf-e2c1-40fd-89fe-0f33c0eabd67 · outbound

This paper cites Jamba: A hybrid transformer-mamba language model.

Block-Biased Mamba for Long-Range Sequence Processing Jamba: A hybrid transformer-mamba language model

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.461991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.405272Z digest=sha256:b6cafb73e39893f0f9e00caeac4d47d67e7f2f333ad2835ae8c4be7ff8563060

Observation 13da0156-e500-47e8-bbe4-71d941b3d841 · outbound

This paper cites From Generalization Analysis to Optimization Designs for State Space Models.

Block-Biased Mamba for Long-Range Sequence Processing From Generalization Analysis to Optimization Designs for State Space Models

Reference 46

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.408905Z digest=sha256:1f58342b6dee6bf411badec39a7e24e1e36efb5849e29448bb8b3139dea1b6ca

Observation c0f7d985-ac72-4026-ad0d-0924729f48fa · outbound

This paper cites Autocorrelation matters: Understanding the role of initialization schemes for state space models.

Block-Biased Mamba for Long-Range Sequence Processing Autocorrelation matters: Understanding the role of initialization schemes for state space models

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.451947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.412603Z digest=sha256:0257d5950ecc75758f489e8efdcd10fcb693dc2ed43c821c7234db2a999da24a

Observation 181a103c-c55a-43d2-900d-1bb159d8f0bb · outbound

This paper cites Sigma: Selective gated mamba for sequential recommenda- tion.

Block-Biased Mamba for Long-Range Sequence Processing Sigma: Selective gated mamba for sequential recommenda- tion

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.441099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.415932Z digest=sha256:6b8e453171b55355f7e42ffaafcd1f2735c19d598d0cc1e9cc7059c0e798c82b

Observation d8a7dae3-c9c0-484e-b0f0-c8d37210af77 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

Block-Biased Mamba for Long-Range Sequence Processing Sgdr: Stochastic gradient descent with warm restarts

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.430045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.419332Z digest=sha256:b56e9b2b9d4648522e0a43e5baa76ad76f36c681caf43727a321ff4832ef5fcb

Observation ca18c959-d4d0-4d17-92c1-97392d75d3a1 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Block-Biased Mamba for Long-Range Sequence Processing Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T21:49:30.422957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.422957Z digest=sha256:f83b7fdb4023c0a7581d24a5a24a28f451bfdbe555028b64c9e5034710dbb0d0

Observation efce10ba-c865-4913-9786-aa897e891197 · outbound

This paper cites A Mamba Foundation Model for Time Series Forecasting.

Block-Biased Mamba for Long-Range Sequence Processing A Mamba Foundation Model for Time Series Forecasting

Reference 51

Resolution
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no resolver link, observed 2026-08-15T21:49:30.426327Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.426327Z digest=sha256:105acc5f0609028b95af40ef0cc7c93536aa6cd45c6055f91baa1913977f87c1

Observation 8cce120a-67c6-464a-8098-4d33b1d3dd1c · outbound

This paper cites A mean field view of the landscape of two-layer neural networks.Proceedings of the National Academy of Sciences, 115(33):E7665– E7671, 2018.

Block-Biased Mamba for Long-Range Sequence Processing A mean field view of the landscape of two-layer neural networks.Proceedings of the National Academy of Sciences, 115(33):E7665– E7671, 2018

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.429687Z digest=sha256:b635f8c217a4a88abc669639da30425f0be384dd78cf152efc8db505ea836cdd

Observation 1d4a2556-3fd9-4463-9270-063aa906a67c · outbound

This paper cites Theoretical foundations of deep selective state-space models.

Block-Biased Mamba for Long-Range Sequence Processing Theoretical foundations of deep selective state-space models

Reference 53

Resolution
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no resolver link, observed 2026-08-15T21:49:30.432944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.432944Z digest=sha256:a02a93ea1872d865d7d00133f0dd277a9e8cc6bd01af052fa8b4a7d99ce0f86b

Observation de244fe1-5343-48e4-8fac-af164f75e2c7 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

Block-Biased Mamba for Long-Range Sequence Processing A time series is worth 64 words: Long-term forecasting with transformers

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-15T21:49:30.436209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.436209Z digest=sha256:42132f3e532876320bf4110835ac3271d30a906ccc09b63b0996547a9a707801

Observation 1f7d8887-8000-459d-a1f3-014ab2de6d97 · outbound

This paper cites Resurrecting Recurrent Neural Networks for Long Sequences.

Block-Biased Mamba for Long-Range Sequence Processing Resurrecting Recurrent Neural Networks for Long Sequences

Reference 55

Resolution
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no resolver link, observed 2026-08-15T21:49:30.439548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.439548Z digest=sha256:679cd3a93ca599cb9f2d0f9aa75534b2082216723f8d132aa5c41f49d92d96a0

Observation 6e02c931-0297-4ee2-a7ef-e0a9d15f517e · outbound

This paper cites State-free inference of state-space models: The transfer function approach.

Block-Biased Mamba for Long-Range Sequence Processing State-free inference of state-space models: The transfer function approach

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.395368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.443099Z digest=sha256:0cb8b90743ecbb696072fcdff3d9c52a21a25561cedf7d7eea29fb84bc838b0f

Observation 2ce66572-4fe9-484b-a905-75ee909a55a1 · outbound

This paper cites On the difficulty of training recurrent neural networks.

Block-Biased Mamba for Long-Range Sequence Processing On the difficulty of training recurrent neural networks

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-15T21:49:30.446414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.446414Z digest=sha256:1cc7b65260a24cad2fd4e63f898c6e2a53f6242adb92e8df9ce033e1438a88f5

Observation 9c34ba97-dd5b-4700-b233-a5db00347e48 · outbound

This paper cites Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges.

Block-Biased Mamba for Long-Range Sequence Processing Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges

Reference 58

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.450813Z digest=sha256:0653ee93db0b81137fec70f60faad95ea512cda51f6e6ca9ced88cc76bf70179

Observation 1d5d5414-b918-4fdb-89bc-2eb4024c6ca5 · outbound

This paper cites Let SSMs be ConvNets: State-space modeling with optimal tensor contractions.

Block-Biased Mamba for Long-Range Sequence Processing Let SSMs be ConvNets: State-space modeling with optimal tensor contractions

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.378385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.454372Z digest=sha256:80b4009fe7ae6a060283948d2715c54ac0f6d5562e2d9593a99be4145e455e6d

Observation 99352441-95c0-49bf-b972-1ee31b53f430 · outbound

This paper cites Approximation theory of the mlp model in neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Approximation theory of the mlp model in neural networks

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.366961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.457921Z digest=sha256:27dc0649b7657f558b0664641b1de6d56d53da085e8b35538ef4884c4a7aae36

Observation 4f0f236f-e902-4d59-a642-f8e40f363263 · outbound

This paper cites S4++: Elevating long sequence modeling with state memory reply.

Block-Biased Mamba for Long-Range Sequence Processing S4++: Elevating long sequence modeling with state memory reply

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.356019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.461656Z digest=sha256:be203098e589dd49319b1c29896b8224d12591ca6391668676f50508dab3dcfd

Observation d5a9868d-4169-4c77-be4e-24fbca4d218a · outbound

This paper cites VL-Mamba: Exploring State Space Models for Multimodal Learning.

Block-Biased Mamba for Long-Range Sequence Processing VL-Mamba: Exploring State Space Models for Multimodal Learning

Reference 62

Resolution
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no resolver link, observed 2026-08-15T21:49:30.464971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.464971Z digest=sha256:001b1ed707b0859a133b642556484bde4b98600cce3fbc24268793468fb46e55

Observation 78f9438b-5176-45eb-b180-f75c800dca1a · outbound

This paper cites Perturbed State Space Feature Encoders for Optical Flow with Event Cameras.

Block-Biased Mamba for Long-Range Sequence Processing Perturbed State Space Feature Encoders for Optical Flow with Event Cameras

Reference 63

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:49:30.790983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.468537Z digest=sha256:4c8dadc99303643ef864cd31b94a08cc4a0cf60dad428f364186988753b73d34

Observation 49626bcd-1a95-42f8-ab40-8afa729be7b6 · outbound

This paper cites Provable benefits of complex parameterizations for structured state space models.

Block-Biased Mamba for Long-Range Sequence Processing Provable benefits of complex parameterizations for structured state space models

Reference 64

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.471941Z digest=sha256:7d3f6902c08f32af02b8f047a5e30eefea56cdb4384acacd4f0ceab699edec13

Observation 326a9f59-393e-4025-b6de-5fe5c7dd9090 · outbound

This paper cites SeRpEnt: Selective Resampling for Expressive State Space Models.

Block-Biased Mamba for Long-Range Sequence Processing SeRpEnt: Selective Resampling for Expressive State Space Models

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:49:30.776274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.475278Z digest=sha256:7636f4c46d1a1250a70aa44d102ba79d5b616145440a5d2fa9c5671b1c563611

Observation 1a99fc75-c0d0-4f0c-a6f6-97c539d1d248 · outbound

This paper cites Ckconv: Continuous kernel convolution for sequential data.

Block-Biased Mamba for Long-Range Sequence Processing Ckconv: Continuous kernel convolution for sequential data

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.337955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.478885Z digest=sha256:b57902325b28142f990f5da4844a0c9dceed8a818e9273f2e8a9ff76a094304f

Observation a0f25cf8-5165-4f3c-a5a9-28ef3e928ac2 · outbound

This paper cites Trainability and accuracy of artificial neural networks: An interacting particle system approach.

Block-Biased Mamba for Long-Range Sequence Processing Trainability and accuracy of artificial neural networks: An interacting particle system approach

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.326879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.482105Z digest=sha256:aac324a0494af5a72d0373692bd39ef9a341f50bc9bbd07ba1789881dcf78fce

Observation c021b988-27fa-434b-a7d9-08e4cf101365 · outbound

This paper cites Unicornn: A recurrent model for learning very long time dependencies.

Block-Biased Mamba for Long-Range Sequence Processing Unicornn: A recurrent model for learning very long time dependencies

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.316415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.485470Z digest=sha256:abf2dcfb8c43d7ae4e718775482cd3ec3a995123cb96ed735e9402c342405016

Observation 4796b129-afa5-4694-bebf-b73da4910411 · outbound

This paper cites Oscillatory state-space models.

Block-Biased Mamba for Long-Range Sequence Processing Oscillatory state-space models

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.305413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.488861Z digest=sha256:cfa1080fe861df050d456f46a237e25556d61a3766c3867285ef298871289007

Observation b48629b0-3f76-48f4-ab0b-c8d9a4b3693d · outbound

This paper cites Empirical Analysis of the Hessian of Over-Parametrized Neural Networks.

Block-Biased Mamba for Long-Range Sequence Processing Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-15T21:49:30.492533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.492533Z digest=sha256:c89dd3a99894c21f26ae51048cee98d4e58d5d4d1cef89fc3018a2bfe833ac12

Observation 4b956a55-396e-46d8-8f79-9b62252ddc24 · outbound

This paper cites Recurrent neural networks are universal approximators.

Block-Biased Mamba for Long-Range Sequence Processing Recurrent neural networks are universal approximators

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.294602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.495969Z digest=sha256:964b9069d4f71d24c52f9f6dcdfef6c8440fec9386018c5365c33aa942112506

Observation 0f70efda-c7e8-41ea-8f1a-16313efeebe9 · outbound

This paper cites SpikingSSMs: Learning long sequences with sparse and 17 parallel spiking state space models.

Block-Biased Mamba for Long-Range Sequence Processing SpikingSSMs: Learning long sequences with sparse and 17 parallel spiking state space models

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.282508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.498990Z digest=sha256:eb72a6fbf8a69d19ffc3ab66d6648002b6ab6016f7c67d0f14daed603262207c

Observation 5e347c4b-e5f9-4ab0-a535-4084ff002245 · outbound

This paper cites Learning important features through propagating activation differences.

Block-Biased Mamba for Long-Range Sequence Processing Learning important features through propagating activation differences

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-15T21:49:30.502789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.502789Z digest=sha256:ce457ea7e20b8e19d1a362150d9da4b8980affb791bcd0d2d91513fd31ddfffc

Observation 6ee8699f-c502-45f7-9023-b107c94edaa4 · outbound

This paper cites Understanding the differences in foundation models: Attention, state space models, and recurrent neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Understanding the differences in foundation models: Attention, state space models, and recurrent neural networks

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.266072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.506519Z digest=sha256:aa3aa5b2f77bcf5cff37c50568b724617bdf62db85957bbe77bc51cba80ee188

Observation c1d41373-63e6-473d-9341-1a8115c606ca · outbound

This paper cites Deep inside convolutional net- works: Visualising image classification models and saliency maps.

Block-Biased Mamba for Long-Range Sequence Processing Deep inside convolutional net- works: Visualising image classification models and saliency maps

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.254435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.509970Z digest=sha256:716b1f70eadd64ac17478270796c714867aaa8659c622cee03be01940afa2e67

Observation 999523ec-8554-4f8d-8fb1-964684819c74 · outbound

This paper cites Towards a theory of learning dynamics in deep state space models.

Block-Biased Mamba for Long-Range Sequence Processing Towards a theory of learning dynamics in deep state space models

Reference 76

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no resolver link, observed 2026-08-15T21:49:30.513445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.513445Z digest=sha256:219fb22ebd807f46e2030fd079eed147f59f5b8c198635592ce8f874935f85f5

Observation d25f5f6a-762d-4af8-b86b-b003b991e8ba · outbound

This paper cites Smith, Andrew Warrington, and Scott Linderman.

Block-Biased Mamba for Long-Range Sequence Processing Smith, Andrew Warrington, and Scott Linderman

Reference 77

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.516954Z digest=sha256:f87dfe646445daab0b123a6a56f313dad4e0f33466ebcd88c6633778c76d936a

Observation 5f4118e2-878d-4af9-9ed7-b69344243786 · outbound

This paper cites SlimPajama: A 627B token cleaned and deduplicated version of RedPajama, 2023.

Block-Biased Mamba for Long-Range Sequence Processing SlimPajama: A 627B token cleaned and deduplicated version of RedPajama, 2023

Reference 78

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no resolver link, observed 2026-08-15T21:49:30.520236Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.520236Z digest=sha256:5978ac7f8722e499755bc724c8c61a029cec55dd6615f90a59cbcaa944eb86d0

Observation 95b9d120-f626-4e01-8960-ee1ed5a7790d · outbound

This paper cites S7: Selective and Simplified State Space Layers for Sequence Modeling.

Block-Biased Mamba for Long-Range Sequence Processing S7: Selective and Simplified State Space Layers for Sequence Modeling

Reference 79

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no resolver link, observed 2026-08-15T21:49:30.523844Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.523844Z digest=sha256:e879c684a5639a28ef54b29a6a8add0923c5ec4e0c3a29edb17491ff042128fe

Observation 3126ce1c-38a8-469c-9279-89814d909b5e · outbound

This paper cites A survey on statistical theory of deep learning: Approxi- mation, training dynamics, and generative models.

Block-Biased Mamba for Long-Range Sequence Processing A survey on statistical theory of deep learning: Approxi- mation, training dynamics, and generative models

Reference 80

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verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.231559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.527500Z digest=sha256:c9133733765ad7b157528c74c5acfd0156b56f1df8a37fa73bcbeb01d5f1a62a

Observation 94408771-01d0-4163-926b-03da4bc718bc · outbound

This paper cites Axiomatic attribution for deep networks.

Block-Biased Mamba for Long-Range Sequence Processing Axiomatic attribution for deep networks

Reference 81

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no resolver link, observed 2026-08-15T21:49:30.530733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.530733Z digest=sha256:84d19da3a99c048f35f32dcc7e3b7659041a78cc9d27329ca9a115ec100a6657

Observation b7f4fd80-4ede-4477-b93c-5f02d54d4561 · outbound

This paper cites Benefits of depth in neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Benefits of depth in neural networks

Reference 82

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verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.214185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.534005Z digest=sha256:28d298ed851a5e013adf8323c9efac441fe0f08e7e02297c40493aedab4ff911

Observation 840ce248-1f72-4527-be45-ceef5b6b325f · outbound

This paper cites Attention is all you need.

Block-Biased Mamba for Long-Range Sequence Processing Attention is all you need

Reference 83

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no resolver link, observed 2026-08-15T21:49:30.537213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.537213Z digest=sha256:10079a0f53f0095287d8c0fbf2732744f8578e6b5d51275f18fb1ce1c876e0b7

Observation 68fc5b94-16ff-4a7a-8054-e9f36408cbb4 · outbound

This paper cites An Empirical Study of Mamba-based Language Models.

Block-Biased Mamba for Long-Range Sequence Processing An Empirical Study of Mamba-based Language Models

Reference 84

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no resolver link, observed 2026-08-15T21:49:30.540419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.540419Z digest=sha256:622555592bd56b5f5b71d1c79b3be45f35497dbc9de723693f79a61c579f21ca

Observation 91d8c88e-5d03-4e30-9eee-acafb237e014 · outbound

This paper cites STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation.

Block-Biased Mamba for Long-Range Sequence Processing STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation

Reference 85

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:49:30.725254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.543930Z digest=sha256:702e18a5ab497a92609b439cfda158fbe486651b66f4e81fbd67d539ebbd7a34

Observation bdf23c0f-e172-4d29-a469-50f29b7b9279 · outbound

This paper cites StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization.

Block-Biased Mamba for Long-Range Sequence Processing StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization

Reference 86

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no resolver link, observed 2026-08-15T21:49:30.547490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.547490Z digest=sha256:7d79f639dbf1379f2a2485489c1836a0f8c92d23708e353d6b23b81beb71bf19

Observation 224e0b80-38df-4220-8f88-a6646f9b2150 · outbound

This paper cites State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory.

Block-Biased Mamba for Long-Range Sequence Processing State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.196557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.553981Z digest=sha256:7f27587c28c565b9391a11021e8931f7bc0ffdca84778d5fac73b3a7e04100e7

Observation f4e0f38e-e220-419b-b05b-d935a07ac5bf · outbound

This paper cites State Space Model for New-Generation Network Alternative to Transformers: A Survey.

Block-Biased Mamba for Long-Range Sequence Processing State Space Model for New-Generation Network Alternative to Transformers: A Survey

Reference 89

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no resolver link, observed 2026-08-15T21:49:30.557268Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.557268Z digest=sha256:ec266816715489768c2caab48bce9e025f1394502534d915dded443b1a69f7d8

Observation a95c8129-a7e9-49be-aa05-6ca47f2c7040 · outbound

This paper cites A Deep State Space Model for Rainfall-Runoff Simulations.

Block-Biased Mamba for Long-Range Sequence Processing A Deep State Space Model for Rainfall-Runoff Simulations

Reference 90

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:49:30.691078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.561262Z digest=sha256:09d834b75a0f27e3bf2d66d792e6e5eb14c1a102c67f30a7500e3e73199acce2

Observation f8bc417e-031d-405a-9257-30326bd54e07 · outbound

This paper cites Is mamba effective for time series forecasting? Neurocomputing, 619:129178, 2025.

Block-Biased Mamba for Long-Range Sequence Processing Is mamba effective for time series forecasting? Neurocomputing, 619:129178, 2025

Reference 91

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.564650Z digest=sha256:8f5245de6bb62a1cb286540091acf583f118046912c869b1589736bbc7198940

Observation 7dd15174-d0f1-4bf9-99ba-efab6ff154eb · outbound

This paper cites A superfast direct inversion method for the nonuniform discrete fourier transform.

Block-Biased Mamba for Long-Range Sequence Processing A superfast direct inversion method for the nonuniform discrete fourier transform

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.179008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.567946Z digest=sha256:01b6c20008cb93de0ae34871ec5eafa63c33efa7edad08004909a7a785ea1580

Observation 515c5a71-cb9a-4146-b765-9d1ab3156998 · outbound

This paper cites Disentangling trainability and generalization in deep neural networks.

Block-Biased Mamba for Long-Range Sequence Processing Disentangling trainability and generalization in deep neural networks

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.168176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.571006Z digest=sha256:ddf70b2faf53b284677f9b35e8293efee6b4f841d3797095ce22247b2311ce40

Observation c6e328ea-9a60-4f12-b122-1b681f843f87 · outbound

This paper cites Evaluating Loss Landscapes from a Topology Perspective.

Block-Biased Mamba for Long-Range Sequence Processing Evaluating Loss Landscapes from a Topology Perspective

Reference 94

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no resolver link, observed 2026-08-15T21:49:30.574330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:49:30.574330Z digest=sha256:5db6d3e71cb8ad42a62a3219f00694e6878927936f2bb89cd38ec8844bb2067c

Observation 7df5373f-e590-47ae-a9a4-96701ab351bc · outbound

This paper cites Feature Learning in Infinite-Width Neural Networks.

Block-Biased Mamba for Long-Range Sequence Processing Feature Learning in Infinite-Width Neural Networks

Reference 95

Resolution
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no resolver link, observed 2026-08-15T21:49:30.577694Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.577694Z digest=sha256:9a5b289b06bc18433dff18630512f14ea9ba560bf0045aed7423717428bdac55

Observation fbfc1fe1-8ad9-448e-9e16-0b4026b93d18 · outbound

This paper cites Longmamba: Enhancing mamba’s long-context capabilities via training-free receptive field enlargement.

Block-Biased Mamba for Long-Range Sequence Processing Longmamba: Enhancing mamba’s long-context capabilities via training-free receptive field enlargement

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.156045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.580941Z digest=sha256:7ca4b80cc962dae38431a1c0f58e98e14c4c21f187f3f811084d788d187124cd

Observation a051064b-d0e2-45e7-a2fd-6c86fca18b88 · outbound

This paper cites Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks.

Block-Biased Mamba for Long-Range Sequence Processing Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks

Reference 97

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:49:30.584095Z digest=sha256:d1a6bf61a8ececf332a2e869a64d011c7cb9d9f93393f362bfed48a45943f0be

Observation dc912768-fcee-4ce5-a710-403bd9f54344 · outbound

This paper cites Tuning frequency bias of state space models.

Block-Biased Mamba for Long-Range Sequence Processing Tuning frequency bias of state space models

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.143335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.587907Z digest=sha256:45a72570eae935c1340f98e88a7eb8c5ab59ffa069ede26c6d1d934addd881c3

Observation ce2302f9-f432-448f-b526-78a344f13e55 · outbound

This paper cites HOPE for a robust pa- rameterization of long-memory state space models.

Block-Biased Mamba for Long-Range Sequence Processing HOPE for a robust pa- rameterization of long-memory state space models

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.130991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.591148Z digest=sha256:736cc30da04f8267e32415ac7ab5b518486b7e30c2a28ff0b90a6360c46aeb95

Observation 0d7034ba-99e4-4e48-9f87-6240ca8eb930 · outbound

This paper cites Mahoney, and N.

Block-Biased Mamba for Long-Range Sequence Processing Mahoney, and N

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.119648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.594805Z digest=sha256:d246407dd992ca4f0400d9c0a085d1953b7c01fed39f249e534e73ad1d7e4eb6

Observation a69fe337-c6df-4ed9-9bc8-e567140a9702 · outbound

This paper cites On the stability of unevenly spaced samples for interpolation and quadrature.

Block-Biased Mamba for Long-Range Sequence Processing On the stability of unevenly spaced samples for interpolation and quadrature

Reference 101

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:49:31.109440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T21:49:30.598041Z digest=sha256:15aee37dcb2ae579427b0cd1574983ae7d1718ba5bf566061a51154599d62ff9

Pith citing papers

Observation 89402e50-ca0e-44ae-ae9a-9e864dd8a10f · inbound

Rethinking the long-range dependency in Mamba/SSM and transformer models cites this paper.

Rethinking the long-range dependency in Mamba/SSM and transformer models Block-Biased Mamba for Long-Range Sequence Processing

Reference 31

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no resolver link, observed 2026-08-05T10:22:53.184571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:22:53.184571Z digest=sha256:882cfbbc517f5101ae88eae656f577af095b821793978d4e739c1f82e9a4f1dc

Observation 86a4d40d-c8af-497f-ad2e-409f3385a5f7 · inbound

Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking cites this paper.

Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking Block-Biased Mamba for Long-Range Sequence Processing

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-03T01:07:31.507642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T01:07:31.507642Z digest=sha256:8450525dc547e4eff8c581ab046cb5f2c35f528b41d4167ca0c665be54bb0bef

Observation 4acf473a-4036-4b6f-82f8-ed7e72ec4b63 · inbound

Continuity Laws for Sequential Models cites this paper.

Continuity Laws for Sequential Models Block-Biased Mamba for Long-Range Sequence Processing

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:56:26.796144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-12T01:32:13.445719Z digest=sha256:f1feacf22e42752237db64532d5b2ba309c6b450809ec6a0586f6a5bc3a867c9