Paper Citation Record · LEDGER

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

As of 13 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 4 inbound Pith citation observations for arXiv:2501.00658.

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

pith.paper-citation-record.v1
2501.00658 v2

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:52:33.772825Z

measured 83 of 83 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:58:54.578841Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T04:25:56.146025Z

Reference resolution

79 of 79 outbound references displayed

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External citation measurements

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Outbound references

Observation db420ba5-9de6-4c93-839a-53348ab29c1a · outbound

This paper cites The Hidden Attention of Mamba Models.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing The Hidden Attention of Mamba Models

Reference 1

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Observation e0d11af2-eaeb-46be-b550-ef666c06c5e9 · outbound

This paper cites DeciMamba: Exploring the Length Extrapolation Potential of Mamba.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing DeciMamba: Exploring the Length Extrapolation Potential of Mamba

Reference 5

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Observation b3726c25-d3c1-4271-b87f-f66b23dcd435 · outbound

This paper cites Language models are few-shot learners.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Language models are few-shot learners

Reference 6

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Observation 0b53bfbc-b713-4d11-b675-4c8034e50fd4 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 7

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Observation e648a296-7d88-4cd8-9573-f06a8ec462e6 · outbound

This paper cites Rethinking Attention with Performers.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Rethinking Attention with Performers

Reference 9

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Observation ad2c096e-eb0b-4ada-97f1-f0301739c813 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 10

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Observation ff742bf8-2803-48b4-990b-dd784d4ac900 · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 12

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Observation 46ff30e7-9068-4de8-a393-2d3f1182e06a · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 13

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Observation 65edb53b-c766-4914-a89f-1d00d1585662 · outbound

This paper cites Were RNNs All We Needed?.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Were RNNs All We Needed?

Reference 15

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Observation 8701a239-5a12-4284-9d32-4c65459f1f28 · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 16

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Observation f44c4f39-53a2-4934-a11d-6ed4523c579c · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Efficiently Modeling Long Sequences with Structured State Spaces

Reference 19

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Observation 52e4a487-a12e-4691-b1b3-24fed19f0800 · outbound

This paper cites Long short-term memory.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Long short-term memory

Reference 20

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Observation 196423ba-964a-463b-87b4-7a607dd2f3bc · outbound

This paper cites Repeat After Me: Transformers are Better than State Space Models at Copying.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 22

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Observation 7a60ab9c-7c21-4ff1-a378-ee88380691d1 · outbound

This paper cites Mistral 7B.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Mistral 7B

Reference 23

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Observation 4d0cfb62-0c7d-4ea3-8edd-387a9418404a · outbound

This paper cites Scaling Laws for Neural Language Models.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Scaling Laws for Neural Language Models

Reference 24

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Observation cd04fae6-c460-4fb2-9b81-ae140044975a · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Semi-Supervised Classification with Graph Convolutional Networks

Reference 25

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Observation 981a46df-05ba-48ed-ba67-ae8ce609bb7c · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Jamba: A Hybrid Transformer-Mamba Language Model

Reference 28

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Observation 604fde47-106f-4e2e-8fe9-8f6ca2dc52de · outbound

This paper cites Longhorn: State Space Models are Amortized Online Learners.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Longhorn: State Space Models are Amortized Online Learners

Reference 29

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This paper cites Mega: Moving Average Equipped Gated Attention.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Mega: Moving Average Equipped Gated Attention

Reference 30

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This paper cites Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Reference 31

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Observation f7c675f7-ccbc-472d-b38e-bf0e31e13161 · outbound

This paper cites The Illusion of State in State-Space Models.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing The Illusion of State in State-Space Models

Reference 32

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing In-context Learning and Induction Heads

Reference 33

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Observation 94bd2d67-7ceb-4d68-b90d-af485a0d876c · outbound

This paper cites Graph neural networks exponentially lose expressive power for node classification.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Graph neural networks exponentially lose expressive power for node classification

Reference 34

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This paper cites Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Reference 35

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing RWKV: Reinventing RNNs for the Transformer Era

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This paper cites Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Reference 37

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This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Ignore Previous Prompt: Attack Techniques For Language Models

Reference 38

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Mechanistic Design and Scaling of Hybrid Architectures

Reference 39

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 40

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing HGRN2: Gated Linear RNNs with State Expansion

Reference 41

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This paper cites Revisiting Over-smoothing in BERT from the Perspective of Graph.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Revisiting Over-smoothing in BERT from the Perspective of Graph

Reference 42

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This paper cites Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 43

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing A Length-Extrapolatable Transformer

Reference 44

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Retentive Network: A Successor to Transformer for Large Language Models

Reference 45

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Long Range Arena: A Benchmark for Efficient Transformers

Reference 46

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Understanding over-squashing and bottlenecks on graphs via curvature

Reference 47

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing The unreasonable effectiveness of the forget gate

Reference 49

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing An Empirical Study of Mamba-based Language Models

Reference 50

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

Reference 51

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing On the Role of Attention Masks and LayerNorm in Transformers

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 54

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Parallelizing Linear Transformers with the Delta Rule over Sequence Length

Reference 55

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry

Reference 56

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 57

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Arora et al

Reference 58

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Linear Attention

Reference 59

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This paper cites Each layer of RetNet consists of a key, 16 Published as a conference paper at ICLR 2025 query, and value transformation, akin to linear attention.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Each layer of RetNet consists of a key, 16 Published as a conference paper at ICLR 2025 query, and value transformation, akin to linear attention

Reference 60

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This paper cites Similar to Mamba (Gu & Dao, 2023), RetNet shares bt and ct across channels while assigning distinct ∆t for each channel when handling multi-channel inputs.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Similar to Mamba (Gu & Dao, 2023), RetNet shares bt and ct across channels while assigning distinct ∆t for each channel when handling multi-channel inputs

Reference 61

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Observation 8be4f9ad-4423-4eff-b9f8-e7f7cebadf1f · outbound

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Its computational mechanism can be encompassed by our formulation in Eq

Reference 62

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Observation b2504547-851c-4e66-9a8b-e242a8c6d643 · outbound

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Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-10T22:52:35.137753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9c86dd9e-3253-4af5-a355-f7c35df7859b · outbound

This paper cites an unresolved cited work.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:52:35.120957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b58c48aa-d56c-4ae0-a6a7-eb3cd43f4f80 · outbound

This paper cites In particular, the dimension of ht in Griffin is equal to the dimension of xt.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing In particular, the dimension of ht in Griffin is equal to the dimension of xt

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:35.099255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2c4ae5a1-9287-43b2-be62-a1da2360be1e · outbound

This paper cites This design has quickly become a standard backbone for various SSMs (Gu & Dao, 2023; Beck et al., 2024).

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing This design has quickly become a standard backbone for various SSMs (Gu & Dao, 2023; Beck et al., 2024)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:35.079906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ad87dfd-bb5e-4198-97de-4fbe5e2d05d6 · outbound

This paper cites attention.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing attention

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:35.062080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f362ba20-f132-47cb-9635-ea2101b7fd0b · outbound

This paper cites We consider ϵ >0 small enough, thus, it is sufficient to consider the scenario when |ω| > Amax ≜ maxn∈[N ] |An,n|.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing We consider ϵ >0 small enough, thus, it is sufficient to consider the scenario when |ω| > Amax ≜ maxn∈[N ] |An,n|

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:35.044358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 60b5fc55-623d-445f-9c91-633d71208f2a · outbound

This paper cites Furthermore, let q = 1 − p.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Furthermore, let q = 1 − p

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:35.027899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ff8a946-7cf4-42f8-86cc-c3260a97e1d5 · outbound

This paper cites less than.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing less than

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:35.010124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e199a764-7053-462f-a8e5-97568a4d0196 · outbound

This paper cites Needle in a Haystack.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Needle in a Haystack

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.875178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 92ff088c-6436-4679-92dd-0e678b6714e9 · outbound

This paper cites In SSMs, the class token must be positioned last to aggregate features from the entire sequence.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing In SSMs, the class token must be positioned last to aggregate features from the entire sequence

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.858842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 33ea289f-1c1b-45b4-92cf-b1dc038a563e · outbound

This paper cites In addition, our image classification setup differs from Tay et al.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing In addition, our image classification setup differs from Tay et al

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.840131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cef29079-19c9-46f9-b0d9-b9f933e1ea1d · outbound

This paper cites The models and training pipelines are built on Arora et al.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing The models and training pipelines are built on Arora et al

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.822951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 688f22a6-b7d7-45c6-a3ba-1edfb924e787 · outbound

This paper cites For each curve in Fig.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing For each curve in Fig

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.803795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6bf046c4-9441-4621-a89b-01a5b3a1aa9e · outbound

This paper cites The evaluation set is created by holding out a subset of 10M tokens from the training data.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing The evaluation set is created by holding out a subset of 10M tokens from the training data

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.781393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dc07023b-8caf-43bc-ad5c-0828f5bcd519 · outbound

This paper cites We test two block sizes {2048, 8192}.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing We test two block sizes {2048, 8192}

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.764751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 682063cf-cf7c-4876-a815-9a389b0b523b · outbound

This paper cites 0 A −1000 # , At ≈.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing 0 A −1000 # , At ≈

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.748149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 55b2cff7-9f66-465f-9b3f-8f1d9719e749 · outbound

This paper cites We consider 1-polarization mitigates locality most significantly, while deepening architecture only relieves recency mildly but deteriorates over-smoothing.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing We consider 1-polarization mitigates locality most significantly, while deepening architecture only relieves recency mildly but deteriorates over-smoothing

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:52:34.730788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c812e534-2070-4e09-9dd2-d6c59daae234 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Training Compute-Optimal Large Language Models

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.455866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbbac38c-b5cb-42f7-9537-2af0e4d67ef8 · outbound

This paper cites DataComp-LM: In search of the next generation of training sets for language models.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing DataComp-LM: In search of the next generation of training sets for language models

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.483637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88595fe3-5e42-42e3-9ba2-de9ce0d109b8 · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.384730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e6c85fc-6a4a-4d53-8292-32b5122f7358 · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.440979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a86b3051-3238-40c5-b68c-7d4911d25845 · outbound

This paper cites What Makes Convolutional Models Great on Long Sequence Modeling?.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.489223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 694c31f0-3289-4e9a-9462-624096c29bfd · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.402204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02387a9e-f650-4752-9771-413844168932 · outbound

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

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.356788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30770a5f-7dcb-4956-a4ea-d4384b00656f · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.419780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baced2b1-8fcc-4e09-adb2-d23b25c95fdb · outbound

This paper cites A mathematical perspective on Transformers.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing A mathematical perspective on Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.435966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76ed0eb1-b777-4cee-a63a-b260004c07bf · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing xLSTM: Extended Long Short-Term Memory

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.362373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64837234-4575-4e0b-b303-0bafb2f189b9 · outbound

This paper cites On the Bottleneck of Graph Neural Networks and its Practical Implications.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing On the Bottleneck of Graph Neural Networks and its Practical Implications

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.351307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 78136a9d-755a-4e54-a2d9-3a851978ff3c · inbound

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention cites this paper.

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T04:58:54.578841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6a00698-189c-4a82-abb3-014f0b22a923 · inbound

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators cites this paper.

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:25:56.150123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b59c1c1e-61d6-4d90-a7d9-14f50aeb86fb · inbound

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale cites this paper.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-02T06:39:25.045565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b9a51b3-bdaf-4644-83ec-dc2a6ca9bd5e · inbound

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale cites this paper.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T02:03:25.442040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.