小型语言模型指的是基于 Transformer 的自然语言处理系统，其参数量低于约 70 亿——这一阈值与其说是由形式上的边界定义的，不如说是由在消费硬件、移动设备和没有云推理基础设施的嵌入式系统上部署的实际限制定义的。
该领域的出现是对前沿规模模型的计算和经济成本的直接回应：虽然十亿参数以上的架构展现了广泛的通用能力，但它们的内存占用、推理延迟和能耗使其在结构上与设备端部署、隐私敏感型应用程序以及低带宽或离线操作环境不兼容。
中心研究计划通过知识蒸馏（用较大的教师模型的输出分布训练较小的学生模型）、结构化和非结构化剪枝、将权重量化到 INT4 和 INT8 表示的激进方法，以及 LoRA 和 QLoRA 等参数高效的微调方法，来缩小紧凑模型和前沿模型之间的能力差距。这些方法能够以最小的额外计算成本，使压缩的基础模型适应特定领域的任务。
以下列出的出版物和专利涉及模型压缩技术、量化算法、蒸馏协议、高效Transformer架构、设备端推理优化和特定领域的微调管道：
这是我们最新精选的关于小语言模型（SLM）的全球英文出版物和专利，这些出版物和专利来自众多科学在线期刊，分类并侧重于小语言模型、SLM、设备上语言模型、边缘语言模型、紧凑型转换器、7B 以下参数模型、语言模型压缩、知识提炼 NLP、结构化剪枝语言模型、非结构化修剪语言模型、权重量化语言模型、INT4 量化 NLP、INT8 量化 NLP、参数效率微调、LoRA 微调、QLoRA 微调、适配器调整语言模型、设备上推理、边缘推理 NLP、推测解码、模型蒸馏转换器、GGUF 量化格式和专家混合紧凑模型。.
Parameter-free method for efficient and accurate llm inference acceleration via speculative decoding
Patent published on the 2026-05-07 in WO under Ref WO2026092843 by MARZOLLO MICHELE [DE] (Marzollo Michele [de], Mueller Lorenz [de], Zhuang Jiawei [de], Roemer Niklas [de], Cavigelli Lukas [de])
Abstract: In some examples, apparatus and methods are provided for selecting a draft token sequence for verification by using a large language model, LLM. Different sources of statistics on text data (prompt, generated output, large dataset of text data) can be utilized in order to choose candidates to use for speculative decoding via look-ups.[...]
Our summary: This method accelerates LLM inference without parameters by using speculative decoding. It selects draft token sequences for verification through statistical analysis of text data. The approach utilizes various sources of statistics to optimize candidate selection for decoding.
speculative decoding, LLM inference, token sequence selection, text data statistics
Patent
Automated synthesis of planar linkage mechanisms with diverse joint types via spring-connected link models and contrastive graph learning
Published on 2026-03-28 by @OXFORD
Abstract: AbstractThe automated synthesis of planar linkage mechanisms has long been a challenge in mechanism design, requiring both geometric feasibility and motion accuracy. Recent advances in data-driven and neural network–based methods have shown promise in automating linkage synthesis, improving efficiency and scalability compared to traditional analytical or optimization-based techniques. Nevertheless, existing data-driven approaches remain limited in handling diverse joint configurations and ofte[...]
Our summary: This study presents a framework for automating the synthesis of planar linkage mechanisms using deep learning and physics-based modeling. It employs a spring-connected link model for diverse joint configurations and utilizes contrastive graph learning for efficient linkage retrieval. The method demonstrates improved accuracy and optimization stability compared to traditional approaches.
mechanism synthesis, deep learning, contrastive graph learning, optimization stability
Publication
Enhancing Whisper Fine-Tuning with Discrete Wavelet Transform-Based LoRA Initialization
Published on 2026-01-29 by Liang Lan, Molin Fang, Yuxuan Chen, Daliang Wang, Wenyong Wang @MDPI
Abstract: In low-resource automatic speech recognition (ASR) scenarios, parameter-efficient fine-tuning (PEFT) has become a crucial approach for adapting large pre-trained speech models. Although low-rank adaptation (LoRA) offers clear advantages in efficiency, stability, and deployment friendliness, its performance remains constrained because random initialization fails to capture the time–frequency structural characteristics of speech signals. To address this limitation, this work proposes[...]
Our summary: This work introduces a structured initialization mechanism combining LoRA with discrete wavelet transform for fine-tuning in low-resource ASR. The proposed DWTLoRA method enhances convergence speed, stability, and accuracy by aligning with speech signal characteristics. Experimental results show DWTLoRA outperforms standard LoRA and other PEFT methods in character error rate and training efficiency.
Fine-Tuning, Discrete Wavelet Transform, Low-Rank Adaptation, Automatic Speech Recognition
Publication
A Systematic Evaluation and Adaptation of Large Language Models for Very Short-Term Power Load Forecasting
Published on 2026-01-26 by Yansheng Chen, Miao Chen, Chenchao Hu, Jinxi Wu, Ruilin Qin @MDPI
Abstract: Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series data, yet their effectiveness in very short-term power load forecasting lacks systematic evaluation. This paper proposes a targeted prompt engineering framework an[...]
Our summary: This study evaluates the effectiveness of Large Language Models (LLMs) for very short-term power load forecasting. It proposes a targeted prompt engineering framework and introduces Ele-LLM, a model utilizing Low-Rank Adaptation for improved performance. Experimental results indicate Ele-LLM significantly outperforms traditional forecasting methods, demonstrating the potential of LLMs in this domain.
Large Language Models, Power Load Forecasting, Prompt Engineering, Low-Rank Adaptation
Publication
A Comparative Study and Introduction of a New Heat Source Model for the Macro-Scale Numerical Simulation of Selective Laser Melting Technology
Published on 2026-01-25 by Hao Zhang, Shuai Wang, Junjie Wang, Zhiqiang Yan @MDPI
Abstract: Selective Laser Melting (SLM), as a common metal additive manufacturing (AM) technology, achieves high-precision complex part formation by layer-by-layer melting of metal powder using a laser. However, the dynamic behavior of the melt pool during the SLM process is influenced by the heat source model, which is crucial for suppressing porosity defects and optimizing process parameters, directly determining the reliability of numerical simulations. To address the issue of traditional surface heat [...]
Our summary: This study introduces a new dynamic heat source model for Selective Laser Melting (SLM) to improve numerical simulations. It compares various heat source models and demonstrates that the dynamic model achieves greater accuracy in predicting melt pool dimensions. The results indicate significant improvements in both melt pool width and depth compared to traditional models.
Heat Source Model, Selective Laser Melting, Finite Element Analysis, Temperature Field
Publication
Influence and Optimization of Process Parameters on Surface Roughness of Selective Laser Melting of 316L Stainless Steel
Published on 2026-01-20 by Pin Dong, Kamonpong Jamkamon, Suppawat Chuvaree @MDPI
Abstract: To achieve better surface quality in selective laser melting (SLM), this study used 316L stainless steel powder and conducted a systematic design experiment to investigate the influence mechanism of process parameters on the surface roughness of the top and vertical surfaces. Response surface methodology (RSM) was then used for parameter optimization. The results showed that scanning speed has the greatest impact on surface roughness, followed by laser power, while scanning spacing has the least[...]
Our summary: This study investigates the impact of process parameters on the surface roughness of 316L stainless steel in selective laser melting. Scanning speed significantly affects surface quality, with optimal conditions identified for minimal roughness. The findings validate the effectiveness of the response surface methodology used for parameter optimization.
Selective Laser Melting, Surface Roughness, Process Parameters, Response Surface Methodology
Publication
Effect of Selectively Etched Al-Rich and Si-Rich Microstructures on the Adhesion of Polyimide Coatings to SLM AlSi10Mg
Published on 2026-01-18 by Jianzhu Li, Shuo Yang, Yujie Li @MDPI
Abstract: Interfacial adhesion between selective laser-melted (SLM) AlSi10Mg and polyimide (PI) insulating coatings is often limited by mismatched physicochemical properties. To improve adhesion, Al-rich and Si-rich microstructured surfaces were fabricated on the XY plane (perpendicular to the build direction) and the Z plane (parallel to the build direction) by acidic and alkaline etching, exploiting the characteristic microstructure of SLM AlSi10Mg. Surface topography, chemical composition, and wettabil[...]
Our summary: Selectively etched microstructures on AlSi10Mg improve adhesion to polyimide coatings. Al-rich surfaces showed cohesive failure, while Si-rich surfaces exhibited mixed failure modes. Shear and pull-off tests demonstrated significant increases in mechanical performance compared to polished surfaces.
adhesion, microstructures, polyimide, selective laser melting
Publication
Monolithic microelectromechanical systems based spatial light modulators including multiple arrays, each array configured to modulate different wavele
Patent published on the 2026-01-02 in WO under Ref WO2026006343 by SILICON LIGHT MACHINES CORP [US] (Hamann Stephen [us], Payne Alexander [us], Hunter James [us], Liu Tianbo [us], Eng Lars [us])
Abstract: A monolithic spatial light modulator (SLM) is provided. Generally, the SLM includes a substrate with a number of substrate electrodes in a surface thereof, multiple MEMS-based linear arrays formed on the surface of the substrate, and a drive circuit monolithically integrated in the substrate below the surface of the substrate. Each linear array includes multiple ribbons suspended above the surface of the substrate, each ribbon having a light reflective surface facing away from the surface of the[...]
Our summary: The content describes a monolithic spatial light modulator (SLM) that includes multiple MEMS-based linear arrays. Each array is designed to modulate different, non-overlapping wavelength ranges. The SLM integrates a drive circuit with substrate electrodes to control the electrostatically displaceable ribbons for light modulation.
MEMS, spatial light modulators, wave modulation, monolithic integration
Patent
