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 15 / 47 页

701. ICML 2026PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
702. ICML 2026POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation
703. ICML 2026POLCA: Stochastic Generative Optimization with LLM
704. ICML 2026PonderLM-2: Pretraining LLM with Latent Thoughts in Continuous Space
705. ICML 2026Position Is All You Need: A Free Lunch Token Compression Strategy for MLLM-based Referring Expression Segmentation
706. ICML 2026Positive–Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
707. ICML 2026Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing
708. ICML 2026Post-Training Language Models for Crosslingual Consistency
709. ICML 2026Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
710. ICML 2026PostTrainBench: Can LLM Agents Automate LLM Post-Training?
711. ICML 2026PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
712. ICML 2026Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction
713. ICML 2026Predicting Large Model Test Losses with a Noisy Quadratic System
714. ICML 2026Predicting the Emergence of Induction Heads in Language Model Pretraining
715. ICML 2026Predicting the Order of Upcoming Tokens Improves Language Modeling
716. ICML 2026Predictive Prefetching for Retrieval-Augmented Generation
717. ICML 2026Preserve-Then-Quantize: Balancing Rank Budgets for Quantization Error Reconstruction in LLMs
718. ICML 2026Principled SVD-based Delta Compression via Quantization Error Minimization
719. ICML 2026Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
720. ICML 2026Prioritize the Process, Not Just the Outcome: Rewarding Latent Thought Trajectories Improves Reasoning in Looped Language Models
721. ICML 2026PRISM: Demystifying Retention and Interaction in Mid-Training
722. ICML 2026PRISM: Perception Reasoning Interleaved for Sequential Decision Making.
723. ICML 2026PRM-PBE: Process Reward Model for Reinforcement Learning in Programming-by-Example
724. ICML 2026Proact-VL: A Proactive VideoLLM for Real-Time AI Companions
725. ICML 2026ProactiveLLM: Learning Active Interaction for Streaming Large Language Models
726. ICML 2026Probability-Entropy Calibration: An Elastic Indicator for Adaptive Fine-tuning
727. ICML 2026ProbeLLM: Automating Principled Diagnosis of LLM Failures
728. ICML 2026Probing RLVR Training Instability through the Lens of Objective-Level Hacking
729. ICML 2026ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
730. ICML 2026Progressive Cramming: Reliable Token Compression and What It Reveals
731. ICML 2026ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
732. ICML 2026Prompt Optimization with Minimal Unlabeled Input via Meta-Reasoning
733. ICML 2026ProphetKV: User-Query-Driven Selective Recomputation for Efficient KV Cache Reuse in Retrieval-Augmented Generation
734. ICML 2026Propose, Solve, Verify: Self-Play Through Formal Verification
735. ICML 2026Proteus: Lookup-Free Trellis-Coded Quantization by Lattice-Breaking Compute Codes for 2-Bit LLMs
736. ICML 2026ProtocolBench: Which LLM MultiAgent Protocol to Choose?
737. ICML 2026ProtoKV: Streaming Video Understanding under Delayed Query with Summary-State Memory
738. ICML 2026Provable Training Data Identification for Large Language Models
739. ICML 2026Proxy Compression for Language Modeling
740. ICML 2026PS-PPO : Prefix-Sampling PPO for Critic-Free RLHF
741. ICML 2026Pull Requests as a Training Signal for Repo-Level Code Editing
742. ICML 2026Push, Pop, Parallelize: Stack-Augmented Linear Attention via the Delta Rule
743. ICML 2026Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
744. ICML 2026Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
745. ICML 2026Pushing the Limits of Block Rotations in Post-Training Quantization
746. ICML 2026PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference
747. ICML 2026QiMeng-ChipV-RTL: Exploiting Information Locality for IP-level Verilog Generation
748. ICML 2026QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
749. ICML 2026Quantifying the Generalization Gap in Seizure Detection: A Large-Scale Empirical Benchmark via the SzCORE Challenge
750. ICML 2026R-Diverse: Mitigating Diversity Illusion in Self-Play LLM Training

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。