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Thinking Machines Lab and OpenBMB released MiniCPM-o 4.5, a 9B full-duplex omnimodal model with the Omni-Flow framework that enables continuous, time-aligned real-time video and voice interaction, surpassing previous models and available as open source.
OpenBMB releases MiniCPM-V 4.6, a 1.3B-parameter multimodal LLM with 262k context and significantly reduced visual encoding FLOPs, achieving strong benchmark performance and broad inference framework support.
A highly efficient AI model architecture using ternary weights (-1, 0, 1) that achieves competitive performance while requiring only 1.58 bits per parameter, enabling deployment on extremely constrained devices.
NXP and Hugging Face demonstrate techniques for deploying Vision-Language-Action (VLA) models on embedded robotic platforms, covering dataset recording best practices, VLA fine-tuning, and on-device optimizations including quantization and asynchronous inference scheduling for the i.MX 95 processor.
This paper introduces Tequila, a trapping-free quantization method for Large Language Models that improves ternary quantization accuracy and inference speed by repurposing deadzone-trapped weights as dynamic biases.