Below is a curated list of recent, detailed survey papers in LLMs, deep learning, and machine learning. It is not literally exhaustive—hundreds of surveys appear monthly—but these are substantial, recent, and useful starting points, especially for a technically oriented reader.
LLM foundations and systems
| Survey | Focus | Date / venue |
|---|---|---|
| A Survey of Large Language Models | Broad LLM landscape: pretraining, architectures, alignment, evaluation, applications, and open challenges. link.springer | May 2026, Frontiers of Computer Science |
| The Versatility of Large Language Models: A Comprehensive Review and Structured Survey | LLM architectures, applications, limitations, and future directions; open access. link.springer | Aug 2026, Springer |
| Efficient training of large language models on distributed systems | Distributed training, parallelism, communication, and systems-level efficiency for LLMs. link.springer | Jun 2026 |
| Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques | Compression, quantization, pruning, distillation, and deployment under constrained resources. arxiv | 2025, IEEE COMPSAC |
| A Survey on Large Language Models Unlearning: Taxonomy, Methods, and Challenges | Machine unlearning for LLMs, privacy, forgetting mechanisms, and evaluation. link.springer | Oct 2025, Artificial Intelligence Review |
LLM reasoning, agents, and training
| Survey | Focus | Date / venue |
|---|---|---|
| A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions | RL for LLM post-training when supervised or preference data is limited. arxiv | Apr 2026, accepted to ACL 2026 |
| Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle | Data selection, weighting, curriculum, pretraining, and post-training trade-offs. arxiv.deeppaper | Oct 2026, arXiv |
| From Long Context to Selective Memory: A Survey of Memory-Centric Compression for Large Language Models | Long-context compression as memory management; inference-cost optimization. arxiv.deeppaper | Oct 2026, arXiv |
| Characterising and Analysing Retrieval-Augmented Generation Techniques | RAG method taxonomy and framework for comparing retrieval-augmented approaches. arxiv.deeppaper | Oct 2026, arXiv |
| Pluralistic Alignment in Language Models | Alignment objectives, human preference representation, and pluralistic value alignment. arxiv.deeppaper | Oct 2026, arXiv |
| Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR | Verifier errors and robustness in reinforcement learning with verifiable rewards. arxiv.deeppaper | Oct 2026, arXiv |
| LLM Agents for Time-Series: A Survey | LLM-based agents for forecasting, anomaly detection, and time-series reasoning. arxiv | 2026, arXiv |
LLM security and trust
| Survey | Focus | Date / venue |
|---|---|---|
| SoK: PIAtlas: Decomposing the Unbounded Prompt Injection Attack Space | Mechanism-based taxonomy of prompt-injection attacks, evaluation datasets, and research gaps. arxiv.deeppaper | Oct 2026, arXiv |
| Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives | Privacy-preserving ML across cryptographic protocols, model design, and systems. arxiv | Jul 2025, arXiv |
| Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models | Reducing LLM benchmark cost while preserving evaluation reliability. arxiv.deeppaper | Oct 2026, arXiv |
Deep learning and core ML
| Survey | Focus | Date / venue |
|---|---|---|
| A Survey on Deep Learning Approaches for Tabular Data Generation | Generative models for tabular data: utility, fidelity, privacy, alignment, and diversity. arxiv | Updated Mar 2026, arXiv |
| Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey | Deep-learning anomaly detection in railway infrastructure and operations. arxiv | 2026, Engineering Applications of Artificial Intelligence |
| FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review | Hardware-aware ML for satellite and Earth-observation workloads. arxiv | Accepted at ACM Computing Surveys, 2026 |
| A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications | Deep learning, reinforcement learning, generative learning, and federated learning for vehicular networks. arxiv | Jun 2025, arXiv |
| A Systematic Survey on Large Language Models for Optimization | LLMs for optimization modeling, surrogate-assisted search, and evolutionary algorithms. arxiv | 2025–2026, arXiv |
Broader survey collections
For continuous tracking rather than a fixed list, these are especially useful:
- arXiv cs.LG recent listings include explicitly tagged survey papers across machine learning; for example, recent entries include railway anomaly detection and FPGA-enabled Earth-observation ML surveys. arxiv
- LLM Survey Papers on arXiv aggregates recent LLM surveys, including prompt injection, alignment, RAG, data strategy, memory compression, RLVR, benchmark compression, and tutoring. arxiv.deeppaper
- NiuTrans/ABigSurveyOfLLMs is a large curated GitHub collection of more than 150 LLM surveys, organized by topics such as pretraining, prompting, data, evaluation, efficiency, and agents. github
How to filter for “detailed” surveys
When selecting papers, prioritize ones that:
- Provide an explicit taxonomy rather than only a chronological literature review.
- Include comparison tables of methods, datasets, benchmarks, and metrics.
- Cover both training-time and deployment/inference-time concerns.
- Discuss limitations, threat models, failure modes, and open problems.
- Are published in a peer-reviewed venue or have a mature, repeatedly revised arXiv version.
For your interests in cloud systems, identity, and security, the strongest immediate reads are likely the prompt-injection SoK, LLM unlearning survey, privacy-preserving ML review, distributed LLM training survey, and model-compression survey. link.springer