Disentangling the Expressivity of RoPE | Paper Citation Record

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This paper cites The Llama 3 Herd of Models.

Disentangling the Expressivity of RoPE The Llama 3 Herd of Models

Reference 1

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This paper cites Meyer and C.

Disentangling the Expressivity of RoPE Meyer and C

Reference 2

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This paper cites Training Neural Networks as Recognizers of Formal Languages.

Disentangling the Expressivity of RoPE Training Neural Networks as Recognizers of Formal Languages

Reference 3

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Disentangling the Expressivity of RoPE Pseudovarieties of semigroups , url =

Reference 4

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This paper cites The Impact of Positional Encoding on Length Generalization in Transformers.

Disentangling the Expressivity of RoPE The Impact of Positional Encoding on Length Generalization in Transformers

Reference 5

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This paper cites Qwen3 Technical Report.

Disentangling the Expressivity of RoPE Qwen3 Technical Report

Reference 6

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Disentangling the Expressivity of RoPE arXiv , author =:2510.00368 , primaryclass =

Reference 7

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This paper cites Counting Like Transformers: Compiling Temporal Counting Logic Into Softmax Transformers , url =.

Disentangling the Expressivity of RoPE Counting Like Transformers: Compiling Temporal Counting Logic Into Softmax Transformers , url =

Reference 8

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This paper cites Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages.

Disentangling the Expressivity of RoPE Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages

Reference 9

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Disentangling the Expressivity of RoPE arXiv , author =:2506.16055 , primaryclass =

Reference 10

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This paper cites Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't , url =.

Disentangling the Expressivity of RoPE Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't , url =

Reference 11

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This paper cites Attention Is All You Need.

Disentangling the Expressivity of RoPE Attention Is All You Need

Reference 12

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Disentangling the Expressivity of RoPE Code Llama: Open Foundation Models for Code

Reference 13

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This paper cites Exposing Attention Glitches with Flip-Flop Language Modeling.

Disentangling the Expressivity of RoPE Exposing Attention Glitches with Flip-Flop Language Modeling

Reference 14

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This paper cites Ash and Surbhi Goel and Akshay Krishnamurthy and Cyril Zhang , booktitle =.

Disentangling the Expressivity of RoPE Ash and Surbhi Goel and Akshay Krishnamurthy and Cyril Zhang , booktitle =

Reference 15

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Disentangling the Expressivity of RoPE Unresolved cited work

Reference 16

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This paper cites Circuit Complexity Bounds for.

Disentangling the Expressivity of RoPE Circuit Complexity Bounds for

Reference 17

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This paper cites doi:10.4230/LIPIcs.STACS.2013.329 , editor =.

Disentangling the Expressivity of RoPE doi:10.4230/LIPIcs.STACS.2013.329 , editor =

Reference 18

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This paper cites Regular languages in NC1 , url =.

Disentangling the Expressivity of RoPE Regular languages in NC1 , url =

Reference 19

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Disentangling the Expressivity of RoPE Transformers in Uniform

Reference 20

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This paper cites Overcoming a Theoretical Limitation of Self-Attention.

Disentangling the Expressivity of RoPE Overcoming a Theoretical Limitation of Self-Attention

Reference 21

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This paper cites Tighter Bounds on the Expressivity of Transformer Encoders.

Disentangling the Expressivity of RoPE Tighter Bounds on the Expressivity of Transformer Encoders

Reference 22

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This paper cites DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models.

Disentangling the Expressivity of RoPE DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

Reference 23

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Disentangling the Expressivity of RoPE arXiv , author =:2606.19348 , primaryclass =

Reference 24

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This paper cites Round and Round We Go! What makes Rotary Positional Encodings useful?.

Disentangling the Expressivity of RoPE Round and Round We Go! What makes Rotary Positional Encodings useful?

Reference 25

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This paper cites Why do LLMs attend to the first token?.

Disentangling the Expressivity of RoPE Why do LLMs attend to the first token?

Reference 26

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Disentangling the Expressivity of RoPE On the temporal analysis of fairness , url =

Reference 27

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This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Disentangling the Expressivity of RoPE Gemma: Open Models Based on Gemini Research and Technology

Reference 28

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Disentangling the Expressivity of RoPE Neural Networks and the Chomsky Hierarchy

Reference 29

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This paper cites Efficient Streaming Language Models with Attention Sinks.

Disentangling the Expressivity of RoPE Efficient Streaming Language Models with Attention Sinks

Reference 30

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This paper cites Venkateswaran , doi =.

Disentangling the Expressivity of RoPE Venkateswaran , doi =

Reference 31

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This paper cites Theoretical Limitations of Self-Attention in Neural Sequence Models , url =.

Disentangling the Expressivity of RoPE Theoretical Limitations of Self-Attention in Neural Sequence Models , url =

Reference 32

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Disentangling the Expressivity of RoPE Why are Sensitive Functions Hard for Transformers? , url =

Reference 33

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Disentangling the Expressivity of RoPE Tense Logic and the Theory of Linear Order , url =

Reference 34

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Disentangling the Expressivity of RoPE Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity , url =

Reference 35

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Disentangling the Expressivity of RoPE Languages of

Reference 36

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Disentangling the Expressivity of RoPE Unique Hard Attention: A Tale of Two Sides , url =

Reference 37

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Disentangling the Expressivity of RoPE RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 38

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Disentangling the Expressivity of RoPE Characterizing the Expressivity of Local Attention in Transformers

Reference 39

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Disentangling the Expressivity of RoPE arXiv , author =:2505.23623 , primaryclass =

Reference 40

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Disentangling the Expressivity of RoPE Qwen Technical Report

Reference 41

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Disentangling the Expressivity of RoPE Adding modular predicates to first-order fragments

Reference 42

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Disentangling the Expressivity of RoPE Counter-Free Automata , url =

Reference 43

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Disentangling the Expressivity of RoPE arXiv , author =:2602.07599 , primaryclass =

Reference 44

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Disentangling the Expressivity of RoPE The Parallelism Tradeoff: Limitations of Log-Precision Transformers , url =

Reference 45

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Disentangling the Expressivity of RoPE Unresolved cited work

Reference 46

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Disentangling the Expressivity of RoPE arXiv , author =:2603.11611 , primaryclass =

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Disentangling the Expressivity of RoPE On the Relation between Position Information and Sentence Length in Neural Machine Translation , url =

Reference 48

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Disentangling the Expressivity of RoPE Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation , url =

Reference 49

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Disentangling the Expressivity of RoPE The temporal logic of programs , url =

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Disentangling the Expressivity of RoPE arXiv , author =:2509.14233 , primaryclass =

Reference 51

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Disentangling the Expressivity of RoPE Analysis of Positional Encodings for Neural Machine Translation , url =

Reference 52

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Disentangling the Expressivity of RoPE MLRegTest: A Benchmark for the Machine Learning of Regular Languages

Reference 53

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This paper cites On the Ability and Limitations of Transformers to Recognize Formal Languages.

Disentangling the Expressivity of RoPE On the Ability and Limitations of Transformers to Recognize Formal Languages

Reference 54

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Disentangling the Expressivity of RoPE Periodic RoPE for Infinite Context LLMs

Reference 55

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This paper cites What Formal Languages Can Transformers Express? A Survey , url =.

Disentangling the Expressivity of RoPE What Formal Languages Can Transformers Express? A Survey , url =

Reference 56

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Disentangling the Expressivity of RoPE Can Transformers Learn n -gram Language Models? , url =

Reference 57

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Disentangling the Expressivity of RoPE Transformers Can Represent n -gram Language Models , url =

Reference 58

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Disentangling the Expressivity of RoPE Olmo 3

Reference 59

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Disentangling the Expressivity of RoPE On the representation theory of finite J-trivial monoids

Reference 60

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Observation ed7fd9c6-aa70-4481-a0f7-2d34ddd74b4b · outbound

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Disentangling the Expressivity of RoPE , isbn =

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:38:40.446046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae6d52d4-dc60-4de7-9005-c9317a45f352 · outbound

This paper cites Resonance.

Disentangling the Expressivity of RoPE Resonance

Reference 62

Resolution: verified exact; doi, observed 2026-08-16T00:38:38.472957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 907ca5fc-1c3b-4a92-a31c-2422c91d04df · outbound

This paper cites A Logic for Expressing Log-Precision Transformers.

Disentangling the Expressivity of RoPE A Logic for Expressing Log-Precision Transformers

Reference 63

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T00:38:38.933167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 17756888-d9b3-4b73-b3ad-89cf8eabaec3 · outbound

This paper cites Why Are Linear RNNs More Parallelizable?.

Disentangling the Expressivity of RoPE Why Are Linear RNNs More Parallelizable?

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.180824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f68859d-bc0c-48c7-896f-7f61c494e098 · outbound

This paper cites Olmo Hybrid: From Theory to Practice and Back.

Disentangling the Expressivity of RoPE Olmo Hybrid: From Theory to Practice and Back

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.273530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2911c0d-cb60-46b2-9a40-401b8aad4f5a · outbound

This paper cites Scaling Laws of RoPE-based Extrapolation.

Disentangling the Expressivity of RoPE Scaling Laws of RoPE-based Extrapolation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.364909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39bf5756-3521-4115-813c-9aa1802aabcb · outbound

This paper cites Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers.

Disentangling the Expressivity of RoPE Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.408162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd2e55f9-286b-43c8-9184-8acdd3a0bc9d · outbound

This paper cites Base of RoPE Bounds Context Length.

Disentangling the Expressivity of RoPE Base of RoPE Bounds Context Length

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.413890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3676eeb3-eeba-40ca-be56-f6b5cf4ded17 · outbound

This paper cites Rope to Nope and Back Again: A New Hybrid Attention Strategy , url =.

Disentangling the Expressivity of RoPE Rope to Nope and Back Again: A New Hybrid Attention Strategy , url =

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:38:40.428703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c45639e-bad3-49f6-8069-847ad99a6d24 · outbound

This paper cites Locally testable languages , url =.

Disentangling the Expressivity of RoPE Locally testable languages , url =

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:38:40.372133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc557fb7-eaa7-4170-9c6b-ed619af407f4 · outbound

This paper cites arXiv , author =:2512.12167 , primaryclass =.

Disentangling the Expressivity of RoPE arXiv , author =:2512.12167 , primaryclass =

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.428838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 562ef1a7-54ea-4922-874f-bdb79c433ebd · outbound

This paper cites RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably.

Disentangling the Expressivity of RoPE RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T00:38:38.433724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference resolution

Outbound references

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