深度学习 · foundation models论文趋势 2023–2026

主要结论

• 深度学习 · foundation models论文从 2023 年的 72 篇增长到 2026 年的 177 篇，三年间扩大到约 2.5 倍。
• NeurIPS 是发深度学习 · foundation models论文最多的会议，2023–2026 年共 299 篇，占四个会议总量的 56%。
• 交叉方向里涨得最快的是基础模型适配：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「深度学习」的 11 个主题里，它的 2026 年论文数排第 8；同组增长最快的扩散模型是 4.1 倍，深度学习 · foundation models是 2.5 倍。

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 2026Inferotemporal Cortex Collaborates Before It Codes: Non-Serial Inter-Area Synergy in the Macaque Ventral Stream
• ICML 2026ACO-MoE-LoRA: Evolving-while-Training for Adapting Segment Anything Model 2 to Specialized Domains
• ICLR 2026Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
• CVPR 2026Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression
• NeurIPS 2025An Investigation of Memorization Risk in Healthcare Foundation Models
• ICML 2025Contrastive Localized Language-Image Pre-Training
• ICLR 2025Revisiting Convolution Architecture in the Realm of DNA Foundation Models
• CVPR 2025DEFOM-Stereo: Depth Foundation Model Based Stereo Matching
• NeurIPS 2024$\textit{Read-ME}$: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
• ICML 2024Position: On the Societal Impact of Open Foundation Models
• CVPR 20243D-LFM: Lifting Foundation Model
• NeurIPS 2023Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
• ICML 2023Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
• NeurIPS 2026Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations
• ICML 2026Alethia: a Foundational Encoder for Voice Deepfakes
• ICLR 2026DND: Boosting Large Language Models with Dynamic Nested Depth
• CVPR 2026UniSER A Foundation Model for Unified Soft Effects Removal
• NeurIPS 2025ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibiltiy Data
• ICML 2025Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
• ICLR 2025Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

全部深度学习 · foundation models论文

共 536 篇，第 3 / 11 页

101. ICML 2026PretrainZero: Reinforcement Active Learning on Pretraining Data
102. ICML 2026PRIM：Cooperative Dynamic Token Compression for Efficient Large Multimodal Models
103. ICML 2026PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
104. ICML 2026Pull Requests as a Training Signal for Repo-Level Code Editing
105. ICML 2026Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles
106. ICML 2026R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
107. ICML 2026RAGEN-2: Reasoning Collapse in Agentic RL
108. ICML 2026Ramba: Selective State-Space Models for Relational Deep Learning
109. ICML 2026ReasonEdit: Editing Vision--Language Models using Human Reasoning
110. ICML 2026Regression Language Models for Code
111. ICML 2026Relational In-Context Learning via Synthetic Pre-training with Structural Prior
112. ICML 2026Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
113. ICML 2026Scaling Laws and Architectural Frontiers in Metagenomic Foundation Models
114. ICML 2026ScDiVa: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression
115. ICML 2026ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision
116. ICML 2026Seeing Without Understanding: Disentangling Perception, Reasoning, and Simulation in VLM Gameplay
117. ICML 2026Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models
118. ICML 2026SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
119. ICML 2026SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
120. ICML 2026SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
121. ICML 2026Steer Like the LLM: Activation Steering that Mimics Prompting
122. ICML 2026Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization
123. ICML 2026TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model
124. ICML 2026TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
125. ICML 2026TGV-KV: Text-Grounded KV Eviction for Vision-Language Models
126. ICML 2026The Deterministic Horizon: When Extended Reasoning Fails and Tool Delegation Becomes Necessary
127. ICML 2026The Illusion of Generalization in Tabular Language Models
128. ICML 2026Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders
129. ICML 2026Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
130. ICML 2026Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm
131. ICML 2026Training-Free Vector Quantization via Gaussian VAEs
132. ICML 2026TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation
133. ICML 2026TVI-CoT: Text-Visual Interleaved Chain-of-Thought Reasoning for Multimodal Understanding
134. ICML 2026UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing
135. ICML 2026UI2Code^N: UI-to-Code Generation as Interactive Visual Optimization
136. ICML 2026Uncovering Grounding IDs: How External Cues Shape Multi-Modal Binding
137. ICML 2026Uncovering the Latent Potential of Deep Intermediate Representations
138. ICML 2026Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective
139. ICML 2026Understanding Transfer Learning of RNA Foundation Models on Downstream Tasks
140. ICML 2026Unified Multimodal Autoregressive Modeling with Shared Context—Visual Tokenizer is Key to Unification
141. ICML 2026Unveiling the Visual Counting Bottleneck in Vision-Language Models
142. ICML 2026Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
143. ICML 2026V-LynX: Token Interface Alignment for Video+X LLMs
144. ICML 2026Video-o3: Native Interleaved Clue Seeking for Long Video Multi-Hop Reasoning
145. ICML 2026VisualScore: Learning Holistic Visual Quality Scores via Multi-Task Reasoning
146. ICML 2026When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
147. ICML 2026When Is Rank-1 Enough? Geometry-Guided Initialization for Parameter-Efficient Fine-Tuning
148. ICML 2026When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
149. ICML 2026WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting
150. ICML 2026Zero-Shot Rankability: Revealing Latent Ordinal Structure in Multimodal Large Language Models via Language

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · foundation models论文？

共 536 篇，按年份分别是 2023 年 72 篇、2024 年 209 篇、2025 年 78 篇、2026 年 177 篇。

NeurIPS 2026 有多少篇深度学习 · foundation models论文？

NeurIPS 2026 有 27 篇深度学习 · foundation models论文，上一届是 15 篇。

ICLR 2026 有多少篇深度学习 · foundation models论文？

ICLR 2026 有 8 篇深度学习 · foundation models论文，上一届是 6 篇。

ICML 2026 有多少篇深度学习 · foundation models论文？

ICML 2026 有 135 篇深度学习 · foundation models论文，上一届是 53 篇。

CVPR 2026 有多少篇深度学习 · foundation models论文？

CVPR 2026 有 7 篇深度学习 · foundation models论文，上一届是 4 篇。

哪个会议的深度学习 · foundation models论文最多？

NeurIPS，2023–2026 年共 299 篇。