Domain-specialized small language models enable deterministic atomic-resolution scanning probe microscopy control with 99.3% command accuracy, lower computational cost, and better domain performance than larger general models.
A review on edge large language models: Design, execution, and applications
5 Pith papers cite this work, alongside 92 external citations. Polarity classification is still indexing.
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verdicts
UNVERDICTED 5representative citing papers
A decoupled pipeline with YOLO detection, deterministic prompt encoding, and QLoRA-adapted 1.5B LLM achieves superior structured report generation compared to monolithic VLMs on synthetic maintenance data.
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
SCENIC framework reports up to 99% exact match on structured IoT command generation using sub-0.2B models, with pruned INT8 versions retaining 91% EM@1 after 25% size reduction.
CoLLM unifies FL PEFT and inference on shared edge replicas via intra-replica model sharing and two-timescale inter-replica coordination, achieving up to 3x higher goodput than prior LLM systems.
citing papers explorer
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Integrating Domain-Specialized Language Models with AI Measurement Tools for Deterministic Atomic-Resolution Experimentation
Domain-specialized small language models enable deterministic atomic-resolution scanning probe microscopy control with 99.3% command accuracy, lower computational cost, and better domain performance than larger general models.
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A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection
A decoupled pipeline with YOLO detection, deterministic prompt encoding, and QLoRA-adapted 1.5B LLM achieves superior structured report generation compared to monolithic VLMs on synthetic maintenance data.
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AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
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SCENIC: Semantic-Conditioned Edge-Aware Neural Framework for Structured IoT Command Generation
SCENIC framework reports up to 99% exact match on structured IoT command generation using sub-0.2B models, with pruned INT8 versions retaining 91% EM@1 after 25% size reduction.
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CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters
CoLLM unifies FL PEFT and inference on shared edge replicas via intra-replica model sharing and two-timescale inter-replica coordination, achieving up to 3x higher goodput than prior LLM systems.