Papers

• Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders (2026)
  Shengbang Tong et al.
  1.94
• One Model, Many Budgets: Elastic Latent Interfaces for Diffusion Transformers (2026)
  Moayed Haji-Ali et al.
  1.94
• Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders (2026)
  Shang-Jui Ray Kuo et al.
  1.94
• Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement (2026)
  Injin Kong et al.
  1.94
• Improving Recursive Transformers with Mixture of LoRAs (2025)
  Mohammadmahdi Nouriborji et al.
  1.28
• Supervised Pretraining Can Learn In-Context Reinforcement Learning (2023)
  Jonathan N. Lee et al.
  —
• Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT) (2023)
  Parsa Kavehzadeh et al.
  —
• Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization (2023)
  Weiyang Liu et al.
  —
• Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster (2023)
  Hongxuan Zhang et al.
  —
• Weight subcloning: direct initialization of transformers using larger pretrained ones (2023)
  Mohammad Samragh et al.
  —
• Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model (2024)
  Lianghui Zhu et al.
  —
• Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models (2024)
  Hengyi Wang et al.
  —
• OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents (2024)
  Zihao Wang et al.
  —
• Transformer Layers as Painters (2024)
  Qi Sun et al.
  —
• In-Context LoRA for Diffusion Transformers (2024)
  Lianghua Huang et al.
  —