Papers

• Artificial Entanglement in the Fine-Tuning of Large Language Models (2026)
  Min Chen et al.
  1.94
• CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation (2026)
  Fangtai Wu et al.
  1.94
• MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning (2026)
  Ionut-Vlad Modoranu et al.
  1.89
• M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment (2025)
  Chuan Cui et al.
  1.28
• Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment (2025)
  Chenghao Fan et al.
  1.28
• FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA (2025)
  Seanie Lee et al.
  1.28
• GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning (2025)
  Yeonjoon Jung et al.
  1.28
• SingLoRA: Low Rank Adaptation Using a Single Matrix (2025)
  David Bensaïd et al.
  1.28
• Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery (2025)
  Jiatong Li et al.
  1.28
• Understanding and Harnessing Sparsity in Unified Multimodal Models (2025)
  Shwai He et al.
  1.28
• LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM (2023)
  Wen-Yu Hua et al.
  —
• One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning (2023)
  Arnav Chavan et al.
  —
• Orthogonal Adaptation for Modular Customization of Diffusion Models (2023)
  Ryan Po et al.
  —
• LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report (2024)
  Justin Zhao et al.
  —
• LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning (2024)
  Yiming Shi et al.
  —
• LoRA-Contextualizing Adaptation of Large Multimodal Models for Long Document Understanding (2024)
  Jian Chen et al.
  —
• EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM (2024)
  Zhuofan Zong et al.
  —