Efficient LLMs Training and Inference: An Introduction by Rui Li, Deji Fu, Chunyu Shi, Zhilan Huang, Gang Lu

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ELO-Mask: Effective and Layerwise Optimization of Mask for Sparse LLMs by Bingjie Xiang, Jiarui Wu, Xiaoying Han, Qian Gu, Fei Chao, Xiao Yang, Fan Wu, Xin Fu

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Long-context inference optimization for large language models: a survey by TAO Wei, WANG Jianzong, ZHANG Xulong, QU Xiaoyang

“…To improve the efficiency of LLMs in long-text inference, a comprehensive review and analysis of existing optimization techniques were conducted. …”
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Long-context inference optimization for large language models: a survey by TAO Wei, WANG Jianzong, ZHANG Xulong, QU Xiaoyang

“…To improve the efficiency of LLMs in long-text inference, a comprehensive review and analysis of existing optimization techniques were conducted. …”
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A study on classification based concurrent API calls and optimal model combination for tool augmented LLMs for AI agent by HeounMo Go, SangHyun Park

“…With the rapid advancement of LLMs, enhanced models continue to emerge. Considering the trade-offs between performance and cost in models, it is crucial to find an optimal combination of models in each stage of tool augmented LLM. …”
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Entropy-Guided KV Caching for Efficient LLM Inference by Heekyum Kim, Yuchul Jung

“…However, their practical deployment—especially in long-context scenarios—is often hindered by the computational and memory costs associated with managing the key–value (KV) cache during inference. Optimizing this process is therefore crucial for improving LLM efficiency and scalability. …”
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AsymGroup: Asymmetric Grouping and Communication Optimization for 2D Tensor Parallelism in LLM Inference by Ki Tae Kim, Seok-Ju Im, Eui-Young Chung

“…Recent advances in Large Language Models (LLMs), such as GPT and LLaMA, have demonstrated remarkable capabilities across a wide array of natural language processing tasks. …”
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ORANSight-2.0: Foundational LLMs for O-RAN by Pranshav Gajjar, Vijay K. Shah

“…We thoroughly evaluate the energy characteristics of ORANSight-2.0, demonstrating its efficiency in training, inference, and inference with RAG augmentation, ensuring optimal performance while maintaining low computational and energy costs. …”
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Survey and Evaluation of Converging Architecture in LLMs Based on Footsteps of Operations by Seongho Kim, Jihyun Moon, Juntaek Oh, Insu Choi, Joon-Sung Yang

“…The evolution of LLMs has been driven by advances in high-bandwidth memory, specialized accelerators, and optimized architectures, enabling models to scale to billions of parameters. …”
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LLMs on a Budget: System-Level Approaches to Power-Efficient and Scalable Fine-Tuning by Kailash Gogineni, Ali Suvizi, Guru Venkataramani

“…Large Language Models (LLMs) have shown remarkable capabilities in various applications, including robotics, telecommunications, and scientific discovery. …”
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Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection by Elsen Ronando, Sozo Inoue

“…In this paper, we propose a novel few-shot optimization with Hybrid Euclidean Distance with Large Language Models (HED-LM) to improve example selection for sensor-based classification tasks. …”
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Unveiling the Power of Large Language Models: A Comparative Study of Retrieval-Augmented Generation, Fine-Tuning, and Their Synergistic Fusion for Enhanced Performance by Gulsum Budakoglu, Hakan Emekci

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BALI—A Benchmark for Accelerated Language Model Inference by Lena Jurkschat, Preetam Gattogi, Sahar Vahdati, Jens Lehmann

“…These applications rely on real-time or near-real-time responses to process sequential LLM requests, creating a critical demand for efficient and accelerated inference. These developments have led to numerous frameworks optimizing inference speed and resource utilization. …”
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Impact of Developer Queries on the Effectiveness of Conversational Large Language Models in Programming by Viktor Taneski, Sašo Karakatič, Patrik Rek, Gregor Jošt

“…These findings suggest that the nature of the queries made to LLMs influences the success of programming tasks and provides insights into how AI tools can assist learning in software development.…”
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Agentic AI Systems: Architecture and Evaluation Using a Frictionless Parking Scenario by Alaa Khamis

“…Key metrics (agent’s response time or latency and lexical consistency) show that a lightweight gpt-4o-mini backbone and concise verbosity minimize latency, while medium prompt specificity and moderate query complexity optimize consistency. Decoding entropy influences stylistic diversity without significant latency costs but reduces consistency at high settings. …”
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Human-Centered AI for Migrant Integration Through LLM and RAG Optimization by Dagoberto Castellanos-Nieves, Luis García-Forte

“…Our proposal involves the optimal tuning of key hyperparameters for LLMs and RAG through multi-criteria decision-making (MCDM) methods to ensure the solutions are fair, equitable, and non-discriminatory. …”
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AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…Furthermore, the research underscores the role of LLMs in personalized medicine, identifying patient-specific risk factors and optimizing treatment pathways for cardiac patients. …”
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Exploring the Joint Influence of Built Environment Factors on Urban Rail Transit Peak-Hour Ridership Using DeepSeek by Zhuorui Wang, Xiaoyu Zheng, Fanyun Meng, Kang Wang, Xincheng Wu, Dexin Yu

“…Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. …”
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Probing the Pitfalls: Understanding SVD’s Shortcomings in Language Model Compression by Сергей Александрович Плетенев

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Evaluating and Improving Syndrome Differentiation Thinking Ability in Large Language Models: Method Development Study by Chunliang Chen, Xinyu Wang, Ming Guan, Wenjing Yue, Yuanbin Wu, Ya Zhou, Xiaoling Wang

“…The optimized LLMs show a high degree of similarity in reasoning results, consistent with the opinions of domain experts, indicating that they can simulate syndrome differentiation thinking to a certain extent. …”
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