深度学习 · large language models论文趋势 2023–2026

主要结论

• 深度学习 · large language models论文从 2023 年的 488 篇增长到 2026 年的 1,131 篇，三年间扩大到约 2.3 倍。
• ICML 是发深度学习 · large language models论文最多的会议，2023–2026 年共 1,608 篇，占四个会议总量的 69%。
• 交叉方向里涨得最快的是AI 智能体：同时属于两个主题的论文从 2023 年的 5 篇增至 2026 年的 184 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 3；同组增长最快的扩散模型是 4.1 倍，深度学习 · large language models是 2.3 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

• ICML 2026“very likely” Means “uncertain”? How LLMs Diverge from Humans in Linguistic Uncertainty Quantification
• ICML 2025(How) Do Language Models Track State?
• ICLR 2025AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models
• NeurIPS 2024$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$
• NeurIPS 20233D-LLM: Injecting the 3D World into Large Language Models
• ICML 2023Are Large Kernels Better Teachers than Transformers for ConvNets?
• ICML 2026$f$-Divergence Self-Play for Tabular Anomaly Detection via Large Language Models
• ICML 2025A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
• ICLR 2025Emergence of a High-Dimensional Abstraction Phase in Language Transformers
• NeurIPS 2024$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning
• NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
• ICML 2023Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
• ICML 2026$L^3$: Large Lookup Layers
• ICML 2025Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies
• ICLR 2025Holistically Evaluating the Environmental Impact of Creating Language Models
• NeurIPS 2024A distributional simplicity bias in the learning dynamics of transformers
• NeurIPS 2023A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints
• ICML 2023Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
• ICML 2026$V_0$: A Generalist Value Model for Any Policy at State Zero
• ICML 2025Accelerating Unbiased LLM Evaluation via Synthetic Feedback

全部深度学习 · large language models论文

共 1,608 篇，第 22 / 33 页

1051. ICML 2026Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity
1052. ICML 2026Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers
1053. ICML 2026TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization
1054. ICML 2026UltraHorizon: Benchmarking LLM-Agent Capabilities in Ultra Long-Horizon Scenarios
1055. ICML 2026UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
1056. ICML 2026Unbiased Alignment for Large Language Models with Noisy Preferences
1057. ICML 2026Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization
1058. ICML 2026Unbiased Reward Modeling from Implicit Feedback for LLM Alignment
1059. ICML 2026Uncovering Hidden Triggers: Backdoor Attribution in Language Models
1060. ICML 2026Understanding Data Temporality Impact on Large Language Models Pre-training
1061. ICML 2026Understanding Dynamic Compute Allocation in Recurrent Transformers
1062. ICML 2026Understanding LoRA as Knowledge Memory: An Empirical Analysis
1063. ICML 2026Understanding the Ability of LLMs to Handle Character-Level Perturbation
1064. ICML 2026UniFLoW: Universal Multi-Modal Federated LoRA Fine-Tuning Framework with Analytical Aggregation
1065. ICML 2026UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling
1066. ICML 2026UniSVQ: 2-bit Unified Scalar-Vector Quantization
1067. ICML 2026Universal One-third Time Scaling in Learning Peaked Distributions
1068. ICML 2026Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
1069. ICML 2026UnMaskFork: Test-Time Scaling for Masked Diffusion via Deterministic Action Branching
1070. ICML 2026Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
1071. ICML 2026Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities
1072. ICML 2026Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance
1073. ICML 2026VecDesigner: Exploring Visual Guidance and Structural Consistency for Semantic Typography
1074. ICML 2026Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention
1075. ICML 2026VENOMREC: Cross-Modal Interactive Poisoning for Targeted Promotion in Multimodal LLM Recommender Systems
1076. ICML 2026VEQ: Modality-Adaptive Quantization for MoE Vision-Language Models
1077. ICML 2026Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
1078. ICML 2026Verifying Meta-Awareness via Predictive Rewards in Reasoning Models
1079. ICML 2026VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
1080. ICML 2026Video-OPD: Efficient Post-Training of Multimodal Large Language Models for Temporal Video Grounding via On-Policy Distillation
1081. ICML 2026video-SALMONN S: Memory-Enhanced Streaming Audio-Visual LLM
1082. ICML 2026Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
1083. ICML 2026VideoBrain: Learning Adaptive Frame Sampling for Long Video Understanding
1084. ICML 2026ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models
1085. ICML 2026Vision-aligned Latent Reasoning for Multi-modal Large Language Model
1086. ICML 2026Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models
1087. ICML 2026Visual Para-Thinker: Divide-and-Conquer Reasoning for Visual Comprehension
1088. ICML 2026Wait, Wait, Wait... Why Do Reasoning Models Loop?
1089. ICML 2026WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving
1090. ICML 2026WaterSIC: Information-Theoretically (Near) Optimal \\Linear Layer Quantization
1091. ICML 2026WAVE: Window-Aware Vocabulary-Efficient Early-Exit for Training-Free LLM Acceleration
1092. ICML 2026Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection
1093. ICML 2026Weaving Graph over Tokens: Contextualizing Structured Sequences for LLMs
1094. ICML 2026WebWorld: A Large-Scale World Model for Web Agent Training
1095. ICML 2026WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
1096. ICML 2026Weight Decay Improves Language Model Plasticity
1097. ICML 2026WET: Mitigating World-Conditioned Knowledge Conflicts via World Entropy Tethering
1098. ICML 2026What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
1099. ICML 2026What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
1100. ICML 2026What Does Vision Tool-Use Reinforcement Learning Really Learn? Disentangling Tool-Induced and Intrinsic Effects for Crop-and-Zoom

常见问题

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

共 2,334 篇，按年份分别是 2023 年 488 篇、2024 年 298 篇、2025 年 417 篇、2026 年 1,131 篇。

NeurIPS 2024 有多少篇深度学习 · large language models论文？

NeurIPS 2024 有 298 篇深度学习 · large language models论文，上一届是 415 篇。

ICLR 2025 有多少篇深度学习 · large language models论文？

ICLR 2025 有 13 篇深度学习 · large language models论文。

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

ICML 2026 有 1,131 篇深度学习 · large language models论文，上一届是 404 篇。

哪个会议的深度学习 · large language models论文最多？

ICML，2023–2026 年共 1,608 篇。