基础模型适配论文趋势 2023–2026
微调与适配
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 1,492 篇被归入该主题的论文。
主要结论
- 基础模型适配论文从 2023 年的 159 篇增长到 2026 年的 602 篇,三年间扩大到约 3.8 倍。
- NeurIPS 是发基础模型适配论文最多的会议,2023–2026 年共 531 篇,占四个会议总量的 36%。
- 交叉方向里涨得最快的是深度学习 · foundation models:同时属于两个主题的论文从 2023 年的 7 篇增至 2026 年的 66 篇。
- 在「元学习与迁移」的 5 个主题里,它的 2026 年论文数排第 1;同组增长最快的持续学习是 1.7 倍,基础模型适配是 3.8 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 81 | 18 | 36 | 24 | 159 |
| 2024 | 94 | 50 | 70 | 56 | 270 |
| 2025 | 174 | 149 | 76 | 62 | 461 |
| 2026 | 182 | 187 | 142 | 91 | 602 |
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
- NeurIPS 2026(Mis)generalization of Helpful-Only Fine-Tuning
- ICML 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
- ICLR 2026Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
- CVPR 2026CD-Buffer Complementary Dual-Buffer Framework for Test-Time Adaptation in Adverse Weather
- NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
- ICML 2025A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints
- ICLR 2025Better autoregressive regression with LLMs via regression-aware fine-tuning
- CVPR 20255%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks
- NeurIPS 2024Instruction Tuning Large Language Models to Understand Electronic Health Records
- ICML 2024By Tying Embeddings You Are Assuming the Distributional Hypothesis
- ICLR 2024A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
- CVPR 2024DePT: Decoupled Prompt Tuning
- NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
- ICML 2023Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
- ICLR 2023A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
- CVPR 2023A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
- NeurIPS 2026A Graph Foundation Model for Unified Clustering
- ICML 2026AdaMeZO: Adam-style Zeroth-Order Optimizer for LLM Fine-tuning Without Maintaining the Moments
- ICLR 2026Bridging Radiology and Pathology Foundation Models via Concept-Based Multimodal Co-Adaptation
- CVPR 2026Fed-ADE Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift
全部基础模型适配论文
共 174 篇,第 1 / 4 页
- NeurIPS 2025A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study
- NeurIPS 2025Activated LoRA: Fine-tuned LLMs for Intrinsics
- NeurIPS 2025AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining
- NeurIPS 2025AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning
- NeurIPS 2025Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
- NeurIPS 2025Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
- NeurIPS 2025AI Progress Should Be Measured by Capability-Per-Resource, Not Scale Alone: A Framework for Gradient-Guided Resource Allocation in LLMs
- NeurIPS 2025AION-1: Omnimodal Foundation Model for Astronomical Sciences
- NeurIPS 2025All You Need is One: Capsule Prompt Tuning with a Single Vector
- NeurIPS 2025AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
- NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
- NeurIPS 2025Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
- NeurIPS 2025AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
- NeurIPS 2025Axial Neural Networks for Dimension-Free Foundation Models
- NeurIPS 2025Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
- NeurIPS 2025Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models
- NeurIPS 2025BlockScan: Detecting Anomalies in Blockchain Transactions
- NeurIPS 2025Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
- NeurIPS 2025Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens
- NeurIPS 2025BrainMoE: Cognition Joint Embedding via Mixture-of-Expert Towards Robust Brain Foundation Model
- NeurIPS 2025BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
- NeurIPS 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
- NeurIPS 2025Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
- NeurIPS 2025Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning
- NeurIPS 2025CF-VLM:CounterFactual Vision-Language Fine-tuning
- NeurIPS 2025ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibiltiy Data
- NeurIPS 2025Class-aware Domain Knowledge Fusion and Fission for Continual Test-Time Adaptation
- NeurIPS 2025Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
- NeurIPS 2025Correlated Low-Rank Adaptation for ConvNets
- NeurIPS 2025CrossSpectra: Exploiting Cross-Layer Smoothness for Parameter-Efficient Fine-Tuning
- NeurIPS 2025CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding
- NeurIPS 2025Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
- NeurIPS 2025Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
- NeurIPS 2025Deep Gaussian from Motion: Exploring 3D Geometric Foundation Models for Gaussian Splatting
- NeurIPS 2025Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
- NeurIPS 2025DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models
- NeurIPS 2025Demystifying Network Foundation Models
- NeurIPS 2025Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
- NeurIPS 2025Distribution-Aligned Decoding for Efficient LLM Task Adaptation
- NeurIPS 2025Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
- NeurIPS 2025DLoFT: Gradient-Decoupled Fine-Tuning for Generalizable Long Chain-of-Thought Reasoning
- NeurIPS 2025DOTA: Distributional Test-time Adaptation of Vision-Language Models
- NeurIPS 2025DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment
- NeurIPS 2025Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation
- NeurIPS 2025E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models
- NeurIPS 2025Efficient Data Selection at Scale via Influence Distillation
- NeurIPS 2025Efficient Randomized Experiments Using Foundation Models
- NeurIPS 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
- NeurIPS 2025EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
- NeurIPS 2025Enhanced Expert Merging for Mixture-of-Experts in Graph 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 查基础模型适配在 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": "foundation-model-adaptation"}
常见问题
- 2023–2026 年四大会议一共有多少篇基础模型适配论文?
- 共 1,492 篇,按年份分别是 2023 年 159 篇、2024 年 270 篇、2025 年 461 篇、2026 年 602 篇。
- NeurIPS 2026 有多少篇基础模型适配论文?
- NeurIPS 2026 有 182 篇基础模型适配论文,上一届是 174 篇。
- ICLR 2026 有多少篇基础模型适配论文?
- ICLR 2026 有 187 篇基础模型适配论文,上一届是 149 篇。
- ICML 2026 有多少篇基础模型适配论文?
- ICML 2026 有 142 篇基础模型适配论文,上一届是 76 篇。
- CVPR 2026 有多少篇基础模型适配论文?
- CVPR 2026 有 91 篇基础模型适配论文,上一届是 62 篇。
- 哪个会议的基础模型适配论文最多?
- NeurIPS,2023–2026 年共 531 篇。