Cover Image

Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Zekai, Liang, Yingyu, Shi, Zhenmei, Song, Zhao, Zhuang, Zhen
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computational Complexity Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers
by: Li, Xiaoyu, et al.
Published: (2024)

Circuit Complexity Bounds for RoPE-based Transformer Architecture
by: Chen, Bo, et al.
Published: (2024)

Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding
by: Chen, Yifang, et al.
Published: (2025)

Circuit Complexity Bounds for Visual Autoregressive Model
by: Ke, Yekun, et al.
Published: (2025)

RoPE Attention Can Be Trained in Almost Linear Time
by: Cao, Yang, 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)

On Fine-Grained I/O Complexity of Attention Backward Passes
by: Li, Xiaoyu, et al.
Published: (2024)

Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
by: Liang, Yingyu, 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)

When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?
by: Li, Chenyang, et al.
Published: (2025)

On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective
by: Li, Xiaoyu, et al.
Published: (2025)

Towards Infinite-Long Prefix in Transformer
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)

Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
by: Liang, Yingyu, et al.
Published: (2024)

Training Tensor Attention Efficiently: From Cubic to Almost Linear Time
by: Cao, Yang, et al.
Published: (2024)

Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent
by: Chen, Bo, et al.
Published: (2025)

Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
by: Xu, Zhuoyan, et al.
Published: (2024)

Universal Approximation of Visual Autoregressive Transformers
by: Chen, Yifang, et al.
Published: (2025)

On Computational Limits of FlowAR Models: Expressivity and Efficiency
by: Cao, Yang, et al.
Published: (2025)

HSR-Enhanced Sparse Attention Acceleration
by: Chen, Bo, et al.
Published: (2024)

Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
by: Liang, Yingyu, et al.
Published: (2024)

Unlocking the Theory Behind Scaling 1-Bit Neural Networks
by: Daliri, Majid, et al.
Published: (2024)

A Tighter Complexity Analysis of SparseGPT
by: Li, Xiaoyu, et al.
Published: (2024)

Why Larger Language Models Do In-context Learning Differently?
by: Shi, Zhenmei, et al.
Published: (2024)

A Measure-Theoretic Analysis of Reasoning: Structural Generalization and Approximation Limits
by: Zhang, Yuyang, et al.
Published: (2026)

NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes
by: Fan, Lizhou, et al.
Published: (2023)

Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction
by: Shi, Zhenmei, et al.
Published: (2024)

Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
by: Xu, Zhuoyan, et al.
Published: (2024)

RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation
by: Cao, Yuefan, et al.
Published: (2025)

HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
by: Liang, Yingyu, et al.
Published: (2025)

Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models
by: Guo, Xuyang, et al.
Published: (2025)

Demystifying the unreasonable effectiveness of online alignment methods
by: Kang, Enoch Hyunwook
Published: (2026)

Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent
by: Chen, Bo, et al.
Published: (2024)

Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
by: Liang, Yingyu, et al.
Published: (2024)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)

Transformers Can Represent $n$-gram Language Models
by: Svete, Anej, et al.
Published: (2024)

How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers
by: Wang, Xiao
Published: (2026)

Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
by: Svete, Anej, et al.
Published: (2026)

Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help
by: Guo, Xuyang, et al.
Published: (2025)

Visual Autoregressive Transformers Must Use $Ω(n^2 d)$ Memory
by: Cao, Yang, et al.
Published: (2025)