低秩适配论文趋势 2023–2026

LoRA 与参数高效微调

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 2,273 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

低秩适配论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
202378766563282
202419395124106518
2025241152136103632
2026269184240148841

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

全部低秩适配论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 2,272 篇，第 14 / 46 页

  1. NeurIPS 2026Efficient Algorithms For Fully Dynamic Bipartite Matching In Metric Spaces
  2. NeurIPS 2026Efficient algorithms for linear regression with heteroskedastic errors
  3. NeurIPS 2026Efficient Collaborative LLM Fine-Tuning over Heterogeneous Mobile Devices via Many Backbones to One Side-Network Tuning
  4. NeurIPS 2026Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
  5. NeurIPS 2026Efficient Fine-Tuning for Structured Sparsity Under Group Repartitioning
  6. NeurIPS 2026Efficient Learning of Truncated Boolean Product Distributions: Influence to the Rescue
  7. NeurIPS 2026Efficient LLM Adaptation with Forward-Only Passes
  8. NeurIPS 2026Efficient Pre-Training with Token Superposition
  9. NeurIPS 2026Efficient SAM 3 Adaptation for Multi-Class Semantic Segmentation via Dense Competitive Representations
  10. NeurIPS 2026Efficient Test-Time Adaptation For Robot Policies
  11. NeurIPS 2026Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
  12. NeurIPS 2026Escaping Parameter Space: Tight Generalization Bounds via Representation Quality
  13. NeurIPS 2026ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
  14. NeurIPS 2026EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
  15. NeurIPS 2026Exploring Lifelong Adaptation: In-Context Reinforcement Learning in Non-Stationary Environments
  16. NeurIPS 2026Extending 3D Reconstruction Models to Any Camera
  17. NeurIPS 2026FacePhys: State of the Heart Learning
  18. NeurIPS 2026Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics
  19. NeurIPS 2026FAUST: Federated Asynchronous Update with Staggered Timescales for Low-Communication Foundation Model Training
  20. NeurIPS 2026FedIBS: Federated Vision-Language Adaptation via Intrinsic Bias Selection
  21. NeurIPS 2026FedLoVA: Value-Only Aggregation for Federated LoRA Fine-Tuning of Large Language Models
  22. NeurIPS 2026FedReCall: Recalling Client-Specific Directions in Federated LoRA Fine-tuning
  23. NeurIPS 2026FELPS: Fair and Efficient Scheduling for Multi-LoRA Serving System
  24. NeurIPS 2026FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance
  25. NeurIPS 2026Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting
  26. NeurIPS 2026Flash-KMeans: Fast and Memory-Efficient Exact K-Means
  27. NeurIPS 2026FLoRA-Chef: Making A Good LoRA Recipe in Federated Generalization
  28. NeurIPS 2026Follow the Mean: Reference-Guided Flow Matching
  29. NeurIPS 2026Frank-LoRA: Federated Rank-Aware LoRA for Fine-Tuning Large Models
  30. NeurIPS 2026From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs
  31. NeurIPS 2026From Next-Token to Next-Block: A Principled Adaptation Path for Diffusion LLMs
  32. NeurIPS 2026From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations
  33. NeurIPS 2026From Weeks to Hours: Fast and Principled SFT Curation for LLM
  34. NeurIPS 2026Full Fine-Tuning Is Not the Problem: Why Adam Fails and SGD Succeeds in Few-Shot CLIP Adaptation
  35. NeurIPS 2026FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning
  36. NeurIPS 2026Generalizing the Geometry of Model Merging Through Fréchet Averages
  37. NeurIPS 2026Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes
  38. NeurIPS 2026GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
  39. NeurIPS 2026Hadamard Representation: Scaffolding Performance Across Model-free RL
  40. NeurIPS 2026HAPS: Hierarchical LLM Routing with Joint Architecture and Parameter Search
  41. NeurIPS 2026Heavy-Tailed Flow Matching via Random Clocks
  42. NeurIPS 2026HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs
  43. NeurIPS 2026HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
  44. NeurIPS 2026Hint Tuning: Less Data Makes Better Reasoners
  45. NeurIPS 2026How Finite-Rank Bottleneck Shape the Low-Rank Adaptation Landscape
  46. NeurIPS 2026Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
  47. NeurIPS 2026Hyperbolic Displacement-Constrained Adaptation for CLIP-Based Class-Incremental Learning
  48. NeurIPS 2026Improving Quantized Zeroth-Order Optimization through Reconstructed Low-Rank Structures
  49. NeurIPS 2026InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories
  50. NeurIPS 2026Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查低秩适配在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "low-rank-adaptation"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

常见问题

2023–2026 年四大会议一共有多少篇低秩适配论文？
共 2,273 篇，按年份分别是 2023 年 282 篇、2024 年 518 篇、2025 年 632 篇、2026 年 841 篇。
NeurIPS 2026 有多少篇低秩适配论文？
NeurIPS 2026 有 269 篇低秩适配论文，上一届是 241 篇。
ICLR 2026 有多少篇低秩适配论文？
ICLR 2026 有 184 篇低秩适配论文，上一届是 152 篇。
ICML 2026 有多少篇低秩适配论文？
ICML 2026 有 240 篇低秩适配论文，上一届是 136 篇。
CVPR 2026 有多少篇低秩适配论文？
CVPR 2026 有 148 篇低秩适配论文，上一届是 103 篇。
哪个会议的低秩适配论文最多？
NeurIPS，2023–2026 年共 781 篇。

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