✨EdgeTAM: On-Device Track Anything Model

📝 Summary:
EdgeTAM optimizes SAM 2 for mobile devices by addressing memory attention bottlenecks with a novel 2D Spatial Perceiver. This lightweight Transformer encodes frame-level memories to reduce computational cost. A distillation pipeline improves performance, enabling high-quality video segmentation a...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.07256
• PDF: https://arxiv.org/pdf/2501.07256
• Github: https://github.com/facebookresearch/edgetam

🔹 Models citing this paper:
• https://huggingface.co/yonigozlan/EdgeTAM-hf
• https://huggingface.co/facebook/EdgeTAM

✨ Spaces citing this paper:
• https://huggingface.co/spaces/merve/EdgeTAM
• https://huggingface.co/spaces/yonigozlan/edgetam
• https://huggingface.co/spaces/facebook/EdgeTAM

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#EdgeAI #VideoSegmentation #ComputerVision #MobileAI #DeepLearning

✨UFO^3: Weaving the Digital Agent Galaxy

📝 Summary:
UFO^3 unifies diverse digital devices into a single orchestration fabric, enabling AI agents to collaborate seamlessly across platforms. It models tasks dynamically for asynchronous execution, achieving efficient, resilient, and accurate cross-device task orchestration with improved parallelism a...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11332
• PDF: https://arxiv.org/pdf/2511.11332
• Project Page: https://microsoft.github.io/UFO/
• Github: https://github.com/microsoft/UFO/

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#AIAgents #TaskOrchestration #DistributedSystems #EdgeAI #MultiAgentSystems

✨UFO^3: Weaving the Digital Agent Galaxy

📝 Summary:
UFO^3 unifies diverse digital devices into a single orchestration fabric, enabling AI agents to collaborate seamlessly across platforms. It models tasks dynamically for asynchronous execution, achieving efficient, resilient, and accurate cross-device task orchestration with improved parallelism a...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11332
• PDF: https://arxiv.org/pdf/2511.11332
• Project Page: https://microsoft.github.io/UFO/
• Github: https://github.com/microsoft/UFO/

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#AIAgents #TaskOrchestration #DistributedSystems #EdgeAI #MultiAgentSystems

✨Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM

📝 Summary:
Xmodel-2.5 is a 1.3B language model designed for efficient edge deployments. It uses maximal-update parameterization and a novel training curriculum that switches from AdamW to Muon, improving reasoning skills by 4.58% while maintaining efficiency.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19496
• PDF: https://arxiv.org/pdf/2511.19496
• Github: https://github.com/XiaoduoAILab/Xmodel-2.5

🔹 Models citing this paper:
• https://huggingface.co/XiaoduoAILab/Xmodel-2.5

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#SLM #EdgeAI #LanguageModels #DeepLearning #ReasoningAI

✨LFM2 Technical Report

📝 Summary:
LFM2 is a family of compact foundation models designed for efficient on-device deployment. It uses hardware-in-the-loop architecture search and advanced training to achieve high performance across diverse tasks, including multimodal applications.

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.23404
• PDF: https://arxiv.org/pdf/2511.23404

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#FoundationModels #EdgeAI #MultimodalAI #AIResearch #MachineLearning

✨UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs

📝 Summary:
UniQL unifies quantization and low-rank compression to deploy LLMs on mobile devices. It reduces memory by 4x-5.7x and improves token throughput by 2.7x-3.4x, maintaining accuracy across various model types.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03383
• PDF: https://arxiv.org/pdf/2512.03383
• Project Page: https://hychiang.info/projects/uniql/
• Github: https://github.com/enyac-group/UniQL

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#LLMs #EdgeAI #Quantization #ModelCompression #DeepLearning

✨AutoNeural: Co-Designing Vision-Language Models for NPU Inference

📝 Summary:
AutoNeural is an NPU-native VLM co-designed for efficient edge inference. It uses a MobileNetV5-style vision backbone for stable integer quantization and a hybrid SSM-Transformer language backbone. This design reduces quantization errors and latency, improving real-time performance on edge devices.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02924
• PDF: https://arxiv.org/pdf/2512.02924

🔹 Models citing this paper:
• https://huggingface.co/NexaAI/AutoNeural

==================================

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#AutoNeural #VisionLanguageModels #EdgeAI #AIHardware #EfficientAI

✨Real-Time Object Detection Meets DINOv3

📝 Summary:
DEIMv2 extends DEIM with DINOv3 features, achieving superior real-time object detection across GPU, edge, and mobile. It uses a Spatial Tuning Adapter and pruned HGNetv2 for diverse models, setting new state of the art with impressive performance-cost trade-offs.

🔹 Publication Date: Published on Sep 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.20787
• PDF: https://arxiv.org/pdf/2509.20787
• Project Page: https://intellindust-ai-lab.github.io/projects/DEIMv2/
• Github: https://github.com/Intellindust-AI-Lab/DEIMv2

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#ObjectDetection #RealTimeAI #ComputerVision #MachineLearning #EdgeAI

✨HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices

📝 Summary:
HyperVL is an efficient multimodal large language model for edge devices. It uses image tiling, a Visual Resolution Compressor, and Dual Consistency Learning to reduce memory, latency, and power. HyperVL maintains performance, making it practical for on-device inference.

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14052
• PDF: https://arxiv.org/pdf/2512.14052

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#HyperVL #MLLM #EdgeAI #EfficientAI #OnDeviceAI

✨Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

📝 Summary:
Bitnet.cpp enhances edge inference for ternary LLMs using a novel mixed-precision matrix multiplication library. This system incorporates Ternary Lookup Tables and Int2 with a Scale for efficient, lossless inference, achieving up to a 6.25x speed increase over baselines.

🔹 Publication Date: Published on Feb 17, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.11880
• PDF: https://arxiv.org/pdf/2502.11880
• Github: https://github.com/microsoft/BitNet/tree/paper

==================================

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#LLM #EdgeAI #MachineLearning #DeepLearning #AI