Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Xiaoyu, Liang, Yingyu, Shi, Zhenmei, Song, Zhao, Wan, Mingda |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Circuit Complexity Bounds for RoPE-based Transformer Architecture
by: Chen, Bo, et al.
Published: (2024)
by: Chen, Bo, et al.
Published: (2024)
RoPE Attention Can Be Trained in Almost Linear Time
by: Cao, Yang, et al.
Published: (2024)
by: Cao, Yang, et al.
Published: (2024)
Modern Hopfield Networks Require Chain-of-Thought to Solve $\mathsf{NC}^1$-Hard Problems
by: Cao, Yang, et al.
Published: (2024)
by: Cao, Yang, et al.
Published: (2024)
Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding
by: Chen, Yifang, et al.
Published: (2025)
by: Chen, Yifang, et al.
Published: (2025)
On Fine-Grained I/O Complexity of Attention Backward Passes
by: Li, Xiaoyu, et al.
Published: (2024)
by: Li, Xiaoyu, et al.
Published: (2024)
The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity
by: Chen, Yifang, et al.
Published: (2024)
by: Chen, Yifang, et al.
Published: (2024)
Circuit Complexity Bounds for Visual Autoregressive Model
by: Ke, Yekun, et al.
Published: (2025)
by: Ke, Yekun, et al.
Published: (2025)
Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers
by: Huang, Zekai, et al.
Published: (2025)
by: Huang, Zekai, et al.
Published: (2025)
On Computational Limits of FlowAR Models: Expressivity and Efficiency
by: Cao, Yang, et al.
Published: (2025)
by: Cao, Yang, et al.
Published: (2025)
When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?
by: Li, Chenyang, et al.
Published: (2025)
by: Li, Chenyang, et al.
Published: (2025)
Training Tensor Attention Efficiently: From Cubic to Almost Linear Time
by: Cao, Yang, et al.
Published: (2024)
by: Cao, Yang, et al.
Published: (2024)
On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective
by: Li, Xiaoyu, et al.
Published: (2025)
by: Li, Xiaoyu, et al.
Published: (2025)
Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
by: Liang, Yingyu, et al.
Published: (2024)
by: Liang, Yingyu, et al.
Published: (2024)
On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis
by: Ke, Yekun, et al.
Published: (2025)
by: Ke, Yekun, et al.
Published: (2025)
Scaling Laws of RoPE-based Extrapolation
by: Liu, Xiaoran, et al.
Published: (2023)
by: Liu, Xiaoran, et al.
Published: (2023)
Demystifying the Slash Pattern in Attention: The Role of RoPE
by: Cheng, Yuan, et al.
Published: (2026)
by: Cheng, Yuan, et al.
Published: (2026)
LinearARD: Linear-Memory Attention Distillation for RoPE Restoration
by: Yang, Ning, et al.
Published: (2026)
by: Yang, Ning, et al.
Published: (2026)
Periodic RoPE for Infinite Context LLMs
by: Huo, Simin
Published: (2026)
by: Huo, Simin
Published: (2026)
Universal Approximation of Visual Autoregressive Transformers
by: Chen, Yifang, et al.
Published: (2025)
by: Chen, Yifang, et al.
Published: (2025)
Towards Infinite-Long Prefix in Transformer
by: Liang, Yingyu, et al.
Published: (2024)
by: Liang, Yingyu, et al.
Published: (2024)
Rotary Positional Embeddings as Phase Modulation: Theoretical Bounds on the RoPE Base for Long-Context Transformers
by: Liu, Feilong
Published: (2026)
by: Liu, Feilong
Published: (2026)
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
by: Liang, Yingyu, et al.
Published: (2025)
by: Liang, Yingyu, et al.
Published: (2025)
A Tighter Complexity Analysis of SparseGPT
by: Li, Xiaoyu, et al.
Published: (2024)
by: Li, Xiaoyu, et al.
Published: (2024)
HSR-Enhanced Sparse Attention Acceleration
by: Chen, Bo, et al.
Published: (2024)
by: Chen, Bo, et al.
Published: (2024)
The Expressive Power of Transformers with Chain of Thought
by: Merrill, William, et al.
Published: (2023)
by: Merrill, William, et al.
Published: (2023)
Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
by: Liang, Yingyu, et al.
Published: (2024)
by: Liang, Yingyu, et al.
Published: (2024)
On the token distance modeling ability of higher RoPE attention dimension
by: Hong, Xiangyu, et al.
Published: (2024)
by: Hong, Xiangyu, et al.
Published: (2024)
RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution
by: Picov, Isaac, et al.
Published: (2026)
by: Picov, Isaac, et al.
Published: (2026)
Capturing P: On the Expressive Power and Efficient Evaluation of Boolean Retrieval
by: Aavani, Amir
Published: (2026)
by: Aavani, Amir
Published: (2026)
The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought
by: Brösamle, Moritz, et al.
Published: (2026)
by: Brösamle, Moritz, et al.
Published: (2026)
Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
by: Liang, Yingyu, et al.
Published: (2024)
by: Liang, Yingyu, et al.
Published: (2024)
Resonance RoPE: Improving Context Length Generalization of Large Language Models
by: Wang, Suyuchen, et al.
Published: (2024)
by: Wang, Suyuchen, et al.
Published: (2024)
RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models
by: Pan, Dayan, et al.
Published: (2025)
by: Pan, Dayan, et al.
Published: (2025)
Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective
by: Zhong, Meizhi, et al.
Published: (2024)
by: Zhong, Meizhi, et al.
Published: (2024)
Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent
by: Chen, Bo, et al.
Published: (2025)
by: Chen, Bo, et al.
Published: (2025)
Base of RoPE Bounds Context Length
by: Men, Xin, et al.
Published: (2024)
by: Men, Xin, et al.
Published: (2024)
CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs
by: Li, Haoran, et al.
Published: (2026)
by: Li, Haoran, et al.
Published: (2026)
Exact Expressive Power of Transformers with Padding
by: Merrill, William, et al.
Published: (2025)
by: Merrill, William, et al.
Published: (2025)
Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
by: Svete, Anej, et al.
Published: (2026)
by: Svete, Anej, et al.
Published: (2026)
A Provable Expressiveness Hierarchy in Hybrid Linear-Full Attention
by: Ye, Xiaowei, et al.
Published: (2026)
by: Ye, Xiaowei, et al.
Published: (2026)
Similar Items
-
Circuit Complexity Bounds for RoPE-based Transformer Architecture
by: Chen, Bo, et al.
Published: (2024) -
RoPE Attention Can Be Trained in Almost Linear Time
by: Cao, Yang, et al.
Published: (2024) -
Modern Hopfield Networks Require Chain-of-Thought to Solve $\mathsf{NC}^1$-Hard Problems
by: Cao, Yang, et al.
Published: (2024) -
Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding
by: Chen, Yifang, et al.
Published: (2025) -
On Fine-Grained I/O Complexity of Attention Backward Passes
by: Li, Xiaoyu, et al.
Published: (2024)