Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:52:33.772825Z
Paper Citation Record · LEDGER
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing
As of 12 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:58:54.578841Z
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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- verified fuzzy23
- unresolved56
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- malformed identifier0
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External citation measurements
No source-named external measurement is stored.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:52:33.368101Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:52:33.373579Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:52:33.379010Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:52:35.253392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5a25b695-3eb7-42c2-bead-ee61821c716f · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b830b8c-4773-4ce7-a0a6-2fdf0ffdabd9 · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d2451af-a931-47a5-b4f3-c10f01d81e91 · outbound
This paper cites In-context Learning and Induction Heads.
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing In-context Learning and Induction Heads
Reference 33
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
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Observation bb41ab24-0105-441a-8d79-be64415d5e43 · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa7d45c6-9e40-4381-abdd-c0db406053e1 · outbound
This paper cites RWKV: Reinventing RNNs for the Transformer Era.
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing RWKV: Reinventing RNNs for the Transformer Era
Reference 36
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f1fe9ae7-d4f5-4272-806a-a7f3dccc2fcd · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31794b19-e32f-462e-a7be-4115a891856a · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3313a84a-12f0-41a4-9f60-523c149c119c · outbound
This paper cites Mechanistic Design and Scaling of Hybrid Architectures.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b7c8b1c-75c6-4691-824a-824f3bc3daa1 · outbound
This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bb9dc605-5b32-4362-95c2-dfc742b0af19 · outbound
This paper cites HGRN2: Gated Linear RNNs with State Expansion.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a2869e78-4e65-4c9a-b094-5fb5bd046626 · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d590677e-9b9b-4c03-97ec-cee95a138271 · outbound
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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This paper cites A Length-Extrapolatable Transformer.
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing A Length-Extrapolatable Transformer
Reference 44
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 38c5dc08-a5b4-4e9f-8c95-2a9bc85b24e6 · outbound
This paper cites Retentive Network: A Successor to Transformer for Large Language Models.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ee84a7d-addb-4452-8dc3-4ffec2706397 · outbound
This paper cites Long Range Arena: A Benchmark for Efficient Transformers.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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This paper cites Understanding over-squashing and bottlenecks on graphs via curvature.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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This paper cites Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel.
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
Reference 48
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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This paper cites The unreasonable effectiveness of the forget gate.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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This paper cites An Empirical Study of Mamba-based Language Models.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9fae8463-f14f-42d4-8f5a-1a071cc6734e · outbound
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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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff99a6c3-f4c5-4123-957e-d1daa19e3488 · outbound
This paper cites A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks.
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
Reference 52
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab356f8d-85e6-4549-bff7-1b23830adf12 · outbound
This paper cites On the Role of Attention Masks and LayerNorm in Transformers.
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
Reference 53
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4aa22575-10a5-4c71-99b3-af692f1008c1 · outbound
This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ba87f36-9fb3-4c98-b0dc-8822888cfb0e · outbound
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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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ca922e9-e7f9-4072-98f4-df3502f21d96 · outbound
This paper cites The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7bea6beb-642d-4d91-8fa1-7f41c625fcc8 · outbound
This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation efe2d6c0-284e-4b5e-8d57-d9a70d40839b · outbound
This paper cites Arora et al.
Reference 58
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation cec37677-5009-4c35-9a1f-1d003b909a9d · outbound
This paper cites Linear Attention.
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Linear Attention
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:52:35.204856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 988a1212-ba25-431d-ae6f-15c8d3853831 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:52:35.188224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ca99d2e5-b3ea-427a-8027-19f3f5abb175 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:52:35.171024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8be4f9ad-4423-4eff-b9f8-e7f7cebadf1f · outbound
This paper cites Its computational mechanism can be encompassed by our formulation in Eq.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T22:52:35.153508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation b2504547-851c-4e66-9a8b-e242a8c6d643 · 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 63
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+00:00.
Observation 0ad87dfd-bb5e-4198-97de-4fbe5e2d05d6 · outbound
This paper cites 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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+00:00.
Observation 0ff8a946-7cf4-42f8-86cc-c3260a97e1d5 · outbound
This paper cites 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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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 2026-08-12T06:34:41.77262+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.