Research map / Large language models
Agents, code and math: research map
1,722 accepted papers on Agents, code and math in Large language models, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 4 clusters and 16 approaches. The busiest year so far is 2026.
Within Large language models, its share held steady from 18.3% in 2023–24 to 18.3% in 2025–26 (363 → 1,258 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
programs · program synthesis · programming · 805 papers
Approaches in this cluster:
- Mathematical and structural reasoning benchmarks (304 papers)
Benchmark LLM math and structural reasoning, with decomposition-based analysis. - Inductive and abstract reasoning evaluation (158 papers)
Test inductive, abstract, and symbolic reasoning, including synthesized benchmarks. - LLMs for optimization and heuristics (147 papers)
Use LLMs with evolutionary search to design heuristics and model optimization problems. - Code generation benchmarks (96 papers)
Evaluate LLM code generation with evolving, domain-specific, and verification-focused benchmarks. - LLMs for scientific discovery (100 papers)
Apply LLMs to drug discovery, equation discovery, and molecular reasoning with new benchmarks.
Most cited and most cited since 2024:
- Self-Refine: Iterative Refinement with Self-Feedback (NeurIPS 2023 · 350 citations)
- Measuring Massive Multitask Language Understanding (ICLR 2021 · 325 citations)
- Can LLM Replace Stack Overflow? A Study on Robustness and Reliability of Large Language Model Code Generation (AAAI 2024 · 63 citations)
- ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution (NeurIPS 2024 · 53 citations)
evaluating · capabilities · bench · 572 papers
Approaches in this cluster:
- Agent instruction-following benchmarks (176 papers)
Evaluate and improve LLM agents' instruction following and decision-making. - Long-horizon LLM agents (155 papers)
Train and structure LLM agents for long-horizon planning, exploration, and in-context learning. - Software engineering agents (140 papers)
Build and evaluate coding agents using execution feedback and code-graph models. - Reasoning benchmarks and platforms (47 papers)
Probe LLM reasoning with games, multilingual math, and competitive programming evaluations. - LLM-driven embodied and driving agents (54 papers)
Use language models for autonomous driving, navigation, and manipulation instructions.
Most cited and most cited since 2024:
- LMDrive: Closed-Loop End-to-End Driving with Large Language Models (CVPR 2024 · 167 citations)
- ExpeL: LLM Agents Are Experiential Learners (AAAI 2024 · 123 citations)
- ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles (CVPR 2024 · 79 citations)
- MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making (NeurIPS 2024 · 58 citations)
search · optimization · decision · 258 papers
Approaches in this cluster:
- LLM-guided program synthesis (93 papers)
Synthesize programs with LLMs through decomposition, learned abstractions, and per-instance search. - Execution-based code verification (90 papers)
Verify and debug LLM-generated code using execution feedback, critics, and rigorous tests. - LLM planning with symbolic tools (44 papers)
Combine language models with action languages and commonsense knowledge for task planning. - Neural program synthesis and transpilation (31 papers)
Guide program synthesis, translation, and robot programs with learned models and probabilistic programs.
Most cited and most cited since 2024:
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation (NeurIPS 2023 · 172 citations)
- Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents (ICML 2022 · 155 citations)
- SGLang: Efficient Execution of Structured Language Model Programs (NeurIPS 2024 · 109 citations)
- Large Language Models as Optimizers (ICLR 2024 · 93 citations)
theorem · proof · proving · 87 papers
Approaches in this cluster:
- Retrieval-augmented formal theorem proving (62 papers)
Prove theorems with language models using retrieval, hammers, and subgoal demonstrations. - Formal verification and proof data (25 papers)
Synthesize proof data and use autoformalization and Lean to verify LLM mathematical reasoning.
Most cited and most cited since 2024:
- HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving (ICML 2019 · 65 citations)
- GamePad: A Learning Environment for Theorem Proving (ICLR 2019 · 56 citations)
- Llemma: An Open Language Model for Mathematics (ICLR 2024 · 15 citations)
- E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator (CVPR 2024 · 10 citations)
Related topics in Large language models
- Reasoning and chain of thought (1,557)
- Instruction and fine-tuning (871)
- Retrieval and knowledge (1,064)
- Alignment and preferences (665)
- AI and society (827)
- Language, safety and interpretability (2,928)