Deep learning · large language models papers, 2023–2026

Key findings

• Deep learning · large language models went from 488 papers in 2023 to 1,131 in 2026, roughly 2.3x in three years.
• ICML publishes the most Deep learning · large language models work: 1,608 papers from 2023 to 2026, 69% of the four-venue total.
• The fastest-growing overlap is with AI Agents: papers tagged with both rose from 5 in 2023 to 184 in 2026.
• Among the 11 topics under Deep Learning, it ranks #3 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 2.3x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Deep learning · large language models papers

1,608 papers, page 6 of 33

251. ICML 2026Discrete Tilt Matching
252. ICML 2026Disentangling Geometry, Performance, and Training in Language Models
253. ICML 2026Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment
254. ICML 2026DisPPO: Quantile-Based Distributional Reinforcement Learning for Large Language Models
255. ICML 2026Distilling Task-Level Coordination Policies for Generalizable Multi-Agent Cooperation
256. ICML 2026Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
257. ICML 2026Distributed Direct Preference Optimization
258. ICML 2026Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge
259. ICML 2026Distributional Alignment Games for Answer-Level Fine-Tuning
260. ICML 2026Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
261. ICML 2026DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use
262. ICML 2026Diversity Over Frequency: Rethinking Tool Use in Visual Chain-of-Thought Agents
263. ICML 2026Diving into Kronecker Adapters: Component Design Matters
264. ICML 2026dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching
265. ICML 2026DLLMQuant: A Post-Training Quantization Framework Tailored for Diffusion-Based Large Language Models
266. ICML 2026DNACHUNKER: Learnable Tokenization for DNA Language Models
267. ICML 2026Do LLMs “Feel”? Emotion Circuits Discovery and Control
268. ICML 2026Do Sparse Autoencoders Identify Reasoning Features in Language Models?
269. ICML 2026Do Vision and Text Cues Exhibit Evidential Coupling? UFO: A Benchmark for Compositional Multimodal Reasoning in Unified Models
270. ICML 2026Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
271. ICML 2026DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
272. ICML 2026Does Your Reasoning Model Implicitly Know When to Stop Thinking?
273. ICML 2026Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
274. ICML 2026Don't Force the Fit: Bounded Log-Likelihood Loss for Enhanced Reasoning in Large Language Models
275. ICML 2026Don't Ignore the Tail: Decoupled Distillation Produces Top Maths Students on an Academic Budget
276. ICML 2026DOT-MoE: Differentiable Optimal Transport for MoEfication
277. ICML 2026DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
278. ICML 2026DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents
279. ICML 2026Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
280. ICML 2026Draft-and-Audit Reinforcement Learning for Optimization Modeling
281. ICML 2026DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
282. ICML 2026DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs
283. ICML 2026DSGym: A Standardized and Holistic Framework for Evaluating and Training Data Science Agents
284. ICML 2026DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA
285. ICML 2026DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
286. ICML 2026Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding
287. ICML 2026DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
288. ICML 2026DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
289. ICML 2026Dynamic Multimodal Evaluation via Knowledge-Enhanced Benchmark Evolution
290. ICML 2026Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
291. ICML 2026DynaVieW: Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics
292. ICML 2026E-mem: Multi-Agent Based Episodic Context Reconstruction for LLM Agent Memory
293. ICML 2026E-VAds: An E-commerce Short Videos Understanding Benchmark for MLLMs
294. ICML 2026EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
295. ICML 2026EAKV: An Entropy-Driven Adaptive KV Compression Framework for Long Video Understanding
296. ICML 2026EAPO: Enhancing Policy Optimization with On-Demand Expert Assistance
297. ICML 2026Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models
298. ICML 2026Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
299. ICML 2026EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference
300. ICML 2026ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning

FAQ

How many Deep learning · large language models papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

2,334 in total: 488 in 2023, 298 in 2024, 417 in 2025, 1,131 in 2026.

How many Deep learning · large language models papers are at NeurIPS 2024?

NeurIPS 2024 has 298 Deep learning · large language models papers, versus 415 the year before.

How many Deep learning · large language models papers are at ICLR 2025?

ICLR 2025 has 13 Deep learning · large language models papers.

How many Deep learning · large language models papers are at ICML 2026?

ICML 2026 has 1,131 Deep learning · large language models papers, versus 404 the year before.

Which conference publishes the most Deep learning · large language models papers?

ICML, with 1,608 papers from 2023 to 2026.