Saved in:
| Main Authors: | Zhang, Juyuan, Zhu, Wei, Gao, Jiechao |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13721 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Adapting Large Language Models for Time Series Modeling via a Novel Parameter-efficient Adaptation Method
by: Zhang, Juyuan, et al.
Published: (2025)
by: Zhang, Juyuan, et al.
Published: (2025)
Federated Neural Architecture Search with Model-Agnostic Meta Learning
by: Huang, Xinyuan, et al.
Published: (2025)
by: Huang, Xinyuan, et al.
Published: (2025)
Metadata Matters for Time Series: Informative Forecasting with Transformers
by: Dong, Jiaxiang, et al.
Published: (2024)
by: Dong, Jiaxiang, et al.
Published: (2024)
Sparse Transformer with Local and Seasonal Adaptation for Multivariate Time Series Forecasting
by: Zhang, Yifan, et al.
Published: (2023)
by: Zhang, Yifan, et al.
Published: (2023)
ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
by: Wang, Juyuan, et al.
Published: (2025)
by: Wang, Juyuan, et al.
Published: (2025)
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
by: Cao, Defu, et al.
Published: (2023)
by: Cao, Defu, et al.
Published: (2023)
Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer Architectures for Cross-Language Structural Priming
by: Zhang, Demi, et al.
Published: (2024)
by: Zhang, Demi, et al.
Published: (2024)
Differential Transformer
by: Ye, Tianzhu, et al.
Published: (2024)
by: Ye, Tianzhu, et al.
Published: (2024)
LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
by: Gao, Jiechao, et al.
Published: (2026)
by: Gao, Jiechao, et al.
Published: (2026)
Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
by: Huang, Zeyi, et al.
Published: (2026)
by: Huang, Zeyi, et al.
Published: (2026)
Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning
by: Gado, Elena Grazia, et al.
Published: (2024)
by: Gado, Elena Grazia, et al.
Published: (2024)
Residual Stream Duality in Modern Transformer Architectures
by: Zhang, Yifan
Published: (2026)
by: Zhang, Yifan
Published: (2026)
CART: Context-Anchored Recurrent Transformer -- A Parameter-Efficient Architecture with Learned Stability
by: Capps, Chad A.
Published: (2026)
by: Capps, Chad A.
Published: (2026)
Changes by Butterflies: Farsighted Forecasting with Group Reservoir Transformer
by: Kowsher, Md, et al.
Published: (2024)
by: Kowsher, Md, et al.
Published: (2024)
Born a Transformer -- Always a Transformer? On the Effect of Pretraining on Architectural Abilities
by: Jobanputra, Mayank, et al.
Published: (2025)
by: Jobanputra, Mayank, et al.
Published: (2025)
LLMs on a Budget? Say HOLA
by: Siddiqui, Zohaib Hasan, et al.
Published: (2025)
by: Siddiqui, Zohaib Hasan, et al.
Published: (2025)
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers
by: Choi, Sehyun
Published: (2024)
by: Choi, Sehyun
Published: (2024)
The Curved Spacetime of Transformer Architectures
by: Di Sipio, Riccardo, et al.
Published: (2025)
by: Di Sipio, Riccardo, et al.
Published: (2025)
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
by: Liu, Yong, et al.
Published: (2024)
by: Liu, Yong, et al.
Published: (2024)
Probing Information Distribution in Transformer Architectures through Entropy Analysis
by: Buonanno, Amedeo, et al.
Published: (2025)
by: Buonanno, Amedeo, et al.
Published: (2025)
TyphoFormer: Language-Augmented Transformer for Accurate Typhoon Track Forecasting
by: Li, Lincan, et al.
Published: (2025)
by: Li, Lincan, et al.
Published: (2025)
Learning to Focus: Focal Attention for Selective and Scalable Transformers
by: Ram, Dhananjay, et al.
Published: (2025)
by: Ram, Dhananjay, et al.
Published: (2025)
A Novel Spinor-Based Embedding Model for Transformers
by: White, Rick
Published: (2024)
by: White, Rick
Published: (2024)
Using Pre-trained LLMs for Multivariate Time Series Forecasting
by: Wolff, Malcolm L., et al.
Published: (2025)
by: Wolff, Malcolm L., et al.
Published: (2025)
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
by: Kowsher, Md, et al.
Published: (2024)
by: Kowsher, Md, et al.
Published: (2024)
LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics
by: Tang, Jialiang, et al.
Published: (2025)
by: Tang, Jialiang, et al.
Published: (2025)
Mitigating Hallucinations in Large Language Models via Causal Reasoning
by: Li, Yuangang, et al.
Published: (2025)
by: Li, Yuangang, et al.
Published: (2025)
Nexus : An Agentic Framework for Time Series Forecasting
by: Das, Sarkar Snigdha Sarathi, et al.
Published: (2026)
by: Das, Sarkar Snigdha Sarathi, et al.
Published: (2026)
DRUM: Learning Demonstration Retriever for Large MUlti-modal Models
by: Yi-Ge, Ellen, et al.
Published: (2024)
by: Yi-Ge, Ellen, et al.
Published: (2024)
VITRO: Vocabulary Inversion for Time-series Representation Optimization
by: Bellos, Filippos, et al.
Published: (2024)
by: Bellos, Filippos, et al.
Published: (2024)
A Comparative Analysis of Contextual Representation Flow in State-Space and Transformer Architectures
by: Hoang, Nhat M., et al.
Published: (2025)
by: Hoang, Nhat M., et al.
Published: (2025)
TTRL: Test-Time Reinforcement Learning
by: Zuo, Yuxin, et al.
Published: (2025)
by: Zuo, Yuxin, et al.
Published: (2025)
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning
by: Bogahawatte, Jayanie, et al.
Published: (2024)
by: Bogahawatte, Jayanie, et al.
Published: (2024)
In-Context Language Learning: Architectures and Algorithms
by: Akyürek, Ekin, et al.
Published: (2024)
by: Akyürek, Ekin, et al.
Published: (2024)
Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
by: Ilyankou, Ilya, et al.
Published: (2024)
by: Ilyankou, Ilya, et al.
Published: (2024)
Supernova: Achieving More with Less in Transformer Architectures
by: Tanase, Andrei-Valentin, et al.
Published: (2025)
by: Tanase, Andrei-Valentin, et al.
Published: (2025)
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
by: Zheng, Chenyu, et al.
Published: (2024)
by: Zheng, Chenyu, et al.
Published: (2024)
When Does Multimodality Lead to Better Time Series Forecasting?
by: Zhang, Xiyuan, et al.
Published: (2025)
by: Zhang, Xiyuan, et al.
Published: (2025)
STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions
by: Fan, Junjie, et al.
Published: (2025)
by: Fan, Junjie, et al.
Published: (2025)
Converting Transformers into DGNNs Form
by: Zhang, Jie, et al.
Published: (2025)
by: Zhang, Jie, et al.
Published: (2025)
Similar Items
-
Adapting Large Language Models for Time Series Modeling via a Novel Parameter-efficient Adaptation Method
by: Zhang, Juyuan, et al.
Published: (2025) -
Federated Neural Architecture Search with Model-Agnostic Meta Learning
by: Huang, Xinyuan, et al.
Published: (2025) -
Metadata Matters for Time Series: Informative Forecasting with Transformers
by: Dong, Jiaxiang, et al.
Published: (2024) -
Sparse Transformer with Local and Seasonal Adaptation for Multivariate Time Series Forecasting
by: Zhang, Yifan, et al.
Published: (2023) -
ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
by: Wang, Juyuan, et al.
Published: (2025)