This survey provides a comprehensive guide to Multimodal Large Language Models (MLLMs) with a focus on vision–language tasks, including image captioning, visual question answering, cross-modal retrieval, visual grounding, multi-image reasoning, long-video understanding, and embodied AI. We examine architectures, training pipelines, and practical applications, covering visual encoders, language model backbones, connector modules, contrastive pre-training, instruction tuning, and preference alignment. We also foreground first-principles constraints—information bottlenecks, data-processing limits, and statistical co-occurrence bias—that shape architecture, robustness, and evaluation. This survey centers on vision–language systems and does not cover audio-only models or code-generation tools without visual inputs. Through task-level analysis and system-level case studies, we examine prominent MLLM implementations while addressing key challenges in scalability, memory, energy use, inference cost, robustness, and cross-modal learning. We present a unified taxonomy of the MLLM design space, a comparative overview of representative models and evaluation benchmarks, and a discussion of open problems. Concluding with ethical considerations and responsible AI development, this survey offers theoretical frameworks and practical insights for researchers, practitioners, and students working at the intersection of natural language processing and computer vision.

Large Language Model (LLM) agents represent a paradigm shift in artificial intelligence, combining the remarkable reasoning capabilities of foundation models with the ability to perceive environments, make decisions, and take actions autonomously. This comprehensive survey provides an in-depth examination of LLM-based agents across multiple dimensions. We first establish a formal definition of LLM agents and trace their evolution from early language models to today's sophisticated autonomous systems. We then present a novel taxonomy that organizes the field into four fundamental categories: reasoning-enhanced agents that leverage chain-of-thought and tree-structured deliberation; tool-augmented agents that extend LLM capabilities through external APIs and knowledge bases; multi-agent systems that enable collaborative problem-solving through inter-agent communication; and memory-augmented agents that maintain persistent context across interactions. For each category, we analyze representative architectures, discuss key innovations, and evaluate their relative strengths and limitations. We further examine diverse applications spanning software engineering, scientific research, embodied robotics, and web automation, supported by systematic comparisons on established benchmarks including SWE-bench, WebArena, and AgentBench. Our analysis reveals that while current agents achieve impressive performance on structured tasks, significant challenges remain in areas such as long-horizon planning, hallucination mitigation, and safe deployment. We conclude by identifying promising research directions, including neuro-symbolic integration, multi-modal perception, and human-agent collaboration frameworks, providing a roadmap for advancing this rapidly evolving field.

The rapid growth of large language models (LLMs) and deep neural networks has necessitated the development of sophisticated distributed training techniques. Models like GPT-4 with trillions of parameters cannot be trained on a single GPU, making distributed training across multiple GPUs and nodes essential. This survey provides a comprehensive overview of distributed deep learning training technologies, covering four key dimensions: (1) parallelism strategies including data parallelism, tensor parallelism, pipeline parallelism, and their combinations; (2) training frameworks such as DeepSpeed, Megatron-LM, GPipe, and PyTorch FSDP; (3) communication optimization techniques including collective operations, gradient compression, and computation-communication overlap; and (4) network interconnect technologies including NVLink, NVSwitch, InfiniBand, and RDMA over Converged Ethernet (RoCE). We analyze the trade-offs between memory efficiency, computational efficiency, and communication overhead for each approach. Furthermore, we discuss practical deployment considerations for single-node multi-GPU and multi-node multi-GPU configurations. Finally, we identify open challenges and future research directions in this rapidly evolving field.

Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. Practical implementations, including adversarial examples, label flipping, and backdoor attacks, are explored alongside defenses such as adversarial training, differential privacy, and federated learning, highlighting their strengths and limitations. Advanced methods like contrastive and self-supervised learning are presented for enhancing robustness. The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models. A balanced approach to performance and security is essential for developing reliable deep learning systems.

Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, yet their ability to execute long-horizon processes with sustained accuracy remains a fundamental challenge. This survey provides a comprehensive examination of reasoning in LLMs, spanning from foundational prompting techniques to emerging massively decomposed agentic processes. We first establish a taxonomy that categorizes reasoning approaches into three primary paradigms: prompting-based methods including Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts; training-based methods encompassing reinforcement learning from human feedback, process reward models, and self-taught reasoning; and multi-agent systems that leverage decomposition and collaborative error correction. We analyze the persistent error rate problem that prevents LLMs from scaling to extended sequential tasks, where recent experiments demonstrate performance collapse beyond a few hundred dependent steps. We then examine MAKER, a breakthrough framework that achieves over one million LLM steps with zero errors through extreme task decomposition combined with multi-agent voting schemes. Our analysis reveals that massively decomposed agentic processes represent a promising paradigm shift from relying solely on improving individual model capabilities toward orchestrating ensembles of focused microagents. We synthesize empirical findings across major benchmarks including GSM8K, MATH, MMLU, and PlanBench, and identify critical open challenges including compositional generalization, error propagation mitigation, and the computational costs of inference-time scaling. This survey aims to provide researchers and practitioners with a unified perspective on the landscape of LLM reasoning and illuminate pathways toward solving problems at organizational and societal scales.

Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores the theoretical foundations, methodological advancements, and practical implementations of these technologies, emphasizing their role in uncovering actionable insights from massive, high-dimensional datasets. The study presents a systematic overview of data preprocessing techniques, including data cleaning, normalization, integration, and dimensionality reduction, to prepare raw data for analysis. Core analytics methodologies such as classification, clustering, regression, and anomaly detection are examined, with a focus on algorithmic innovation and scalability. Furthermore, the text delves into state-of-the-art frameworks for data mining and predictive modeling, highlighting the role of neural networks, support vector machines, and ensemble methods in tackling complex analytical challenges. Special emphasis is placed on the convergence of big data with distributed computing paradigms, including cloud and edge computing, to address challenges in storage, computation, and real-time analytics. The integration of ethical considerations, including data privacy and compliance with global standards, ensures a holistic perspective on data management. Practical applications across healthcare, finance, marketing, and policy-making illustrate the real-world impact of these technologies. Through comprehensive case studies and Python-based implementations, this work equips researchers, practitioners, and data enthusiasts with the tools to navigate the complexities of modern data analytics. It bridges the gap between theory and practice, fostering the development of innovative solutions for managing and leveraging data in the era of artificial intelligence.