Research map / Large language models
Reasoning and chain of thought: research map
1,557 accepted papers on Reasoning and chain of thought in Large language models, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 3 clusters and 12 approaches. The busiest year so far is 2026.
Within Large language models, its share grew from 6.5% in 2023–24 to 20.6% in 2025–26 (130 → 1,417 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
test time · scaling · rl · 880 papers
Approaches in this cluster:
- Efficient and adaptive thinking (231 papers)
Control reasoning length and self-verification so models think wisely rather than longer. - Test-time compute scaling (205 papers)
Allocate inference-time compute through confidence, branching, and self-consistency strategies. - RL with verifiable rewards for reasoning (270 papers)
Train reasoning LLMs with reinforcement learning and analyze mathematical reasoning gains. - Routing, pruning and distillation for reasoning (135 papers)
Reduce reasoning cost via model routing, small-large collaboration, pruning, and distillation. - Process reward models (39 papers)
Train process-level reward models with synthetic annotation and calibration to verify reasoning steps.
Most cited and most cited since 2024:
- Measuring Mathematical Problem Solving With the MATH Dataset (NeurIPS 2021 · 276 citations)
- Inference-Time Intervention: Eliciting Truthful Answers from a Language Model (NeurIPS 2023 · 56 citations)
- The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity (NeurIPS 2025 · 29 citations)
- ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search (NeurIPS 2024 · 21 citations)
chain thought · thought cot · cot reasoning · 414 papers
Approaches in this cluster:
- Chain-of-thought training and analysis (127 papers)
Train and analyze chain-of-thought reasoning, including its faithfulness and information content. - Long CoT dynamics and efficiency (136 papers)
Study the geometry, legibility, and efficiency of long reasoning chains. - Search and self-correction in reasoning (123 papers)
Improve reasoning with trial-and-error, calibration, rollback, and selection-inference loops. - Theory of autoregressive reasoning (28 papers)
Analyze compositional generalization and learnability of reasoning by next-token predictors.
Most cited and most cited since 2024:
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (NeurIPS 2022 · 2,251 citations)
- Self-Consistency Improves Chain of Thought Reasoning in Language Models (ICLR 2023 · 706 citations)
- Graph of Thoughts: Solving Elaborate Problems with Large Language Models (AAAI 2024 · 512 citations)
- MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (NeurIPS 2024 · 82 citations)
decoding · speculative · cache · 263 papers
Approaches in this cluster:
- Efficient LLM serving (126 papers)
Schedule queries, reuse prefixes, and optimize architectures for efficient LLM inference. - Speculative decoding (92 papers)
Use draft models and acceptance-rate optimization to decode faster without changing outputs. - KV cache compression (45 papers)
Evict or merge key-value cache entries to cut memory in LLM inference.
Most cited and most cited since 2024:
- ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe (CVPR 2024 · 108 citations)
- H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models (NeurIPS 2023 · 66 citations)
- SnapKV: LLM Knows What You are Looking for Before Generation (NeurIPS 2024 · 51 citations)
- Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models (AAAI 2024 · 34 citations)
Related topics in Large language models
- Instruction and fine-tuning (871)
- Retrieval and knowledge (1,064)
- Agents, code and math (1,722)
- Alignment and preferences (665)
- AI and society (827)
- Language, safety and interpretability (2,928)