✨Fara-7B: An Efficient Agentic Model for Computer Use

📝 Summary:
FaraGen creates synthetic datasets for computer use agents, solving a data scarcity problem. This data trains Fara-7B, a small on-device model that perceives computers via screenshots and outperforms larger models on diverse web tasks.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19663
• PDF: https://arxiv.org/pdf/2511.19663

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For more data science resources:
✓ https://t.me/DataScienceT

#AIAgents #OnDeviceAI #SyntheticData #MachineLearning #ComputerVision

✨MemLoRA: Distilling Expert Adapters for On-Device Memory Systems

📝 Summary:
MemLoRA and MemLoRA-V enable efficient on-device memory-augmented AI by equipping small language and vision-language models with specialized, distilled memory adapters. This allows accurate local memory operations and native visual understanding, outperforming larger baselines in text and visual ...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04763
• PDF: https://arxiv.org/pdf/2512.04763

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#OnDeviceAI #LLMs #VLMs #AIAdapters #MemoryAugmentedAI

✨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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For more data science resources:
✓ https://t.me/DataScienceT

#HyperVL #MLLM #EdgeAI #EfficientAI #OnDeviceAI