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 2,334 篇，第 19 / 47 页

901. ICML 2026SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
902. ICML 2026SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection
903. ICML 2026SpanNorm: Reconciling Training Stability and Performance in Deep Transformers
904. ICML 2026SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance–Diversity Data Selection
905. ICML 2026Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agents
906. ICML 2026SparseInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
907. ICML 2026Sparser, Faster, Lighter Transformer Language Models
908. ICML 2026Spatio-Temporal LLM: Reasoning about Environments and Actions
909. ICML 2026SpecExit: Accelerating Large Reasoning Model via Speculative Exit
910. ICML 2026SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
911. ICML 2026SpecMD: A Comprehensive Study On Speculative Expert Prefetching
912. ICML 2026SpecPL: Disentangling Spectral Granularity for Prompt Learning
913. ICML 2026Spectra: Rethinking Optimizers for LLMs Under Spectral Anisotropy
914. ICML 2026SPEED: Sharpened-Teacher Distillation for Parallel Decoding of Diffusion Language Models
915. ICML 2026Spherical Steering: Geometry-Aware Activation Rotation for Language Models
916. ICML 2026Spurious Rewards: Rethinking Training Signals in RLVR
917. ICML 2026Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text
918. ICML 2026Stabilizing Native Low-Rank LLM Pretraining
919. ICML 2026Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models
920. ICML 2026Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
921. ICML 2026Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control
922. ICML 2026STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control
923. ICML 2026STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
924. ICML 2026Statistical Early Stopping for Reasoning Models
925. ICML 2026Steal the Patch Size: Adversarially Manipulate Vision Language Models
926. ICML 2026Steer Like the LLM: Activation Steering that Mimics Prompting
927. ICML 2026Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding
928. ICML 2026Stop When Further Reasoning Won’t Help: Attention-State Adaptive Generation in Reasoning Models
929. ICML 2026Strategy Executability in Mathematical Reasoning: Leveraging Human–Model Differences for Effective Guidance
930. ICML 2026Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
931. ICML 2026Structurally Aligned Subtask-Level Memory for Software Engineering Agents
932. ICML 2026Structure Enables Effective Self-Localization of Errors in LLMs
933. ICML 2026Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs
934. ICML 2026Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
935. ICML 2026SubspacePath Pruner: Inference-time Pruning via Probe-based Representation–Parameter Coupling
936. ICML 2026SuCo: Sufficiency-guided Continuous Adaptive Reasoning
937. ICML 2026Surgery: Mitigating Harmful Fine-Tuning for Large Language Models via Attention Sink
938. ICML 2026SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark
939. ICML 2026SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
940. ICML 2026SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
941. ICML 2026SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale
942. ICML 2026Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression
943. ICML 2026Swordsman: Entropy-Driven Adaptive Block Partition for Efficient Diffusion Language Models
944. ICML 2026Sycophancy Towards Researchers Drives Performative Misalignment
945. ICML 2026Symbol-Equivariant Recurrent Reasoning Models
946. ICML 2026Syntax vs. Semantics: How Transformers Learn Deep Dependencies
947. ICML 2026T-POP: Test-Time Personalization with Online Preference Feedback
948. ICML 2026Tackling Length Inflation Without Trade-offs: Group Relative Reward Rescaling for Reinforcement Learning
949. ICML 2026Tailoring the Training: Difficulty-Aware Learning Strategy Allocation for Large Language Models
950. ICML 2026TAMPO: Task- and Model-Aware Automatic Prompt Optimization for Auto-Routing in LLM-based Systems

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。