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Loft Orbital's YAM-9 satellite runs Google's Gemma 3 vision-language model onboard for real-time image analysis, reducing downlink bandwidth and latency by deciding what data to send to Earth.
This paper systematically tests linear probes for deception detection in large language models, finding they fail under distributional shifts but style-augmented probes recover performance, and revealing that deception is encoded through distributed sub-threshold features.
Anthropic and Neuronpedia released research and tools on Natural Language Autoencoders (NLA), enabling users to view the internal 'thoughts' of Gemma 3 during token generation. The release includes model weights for the Auto Verbalizer and Activation Reconstructor, hosted on Hugging Face and Neuronpedia.
Google introduces Gemma 3 270M, a compact 270-million parameter model designed for efficient on-device AI with strong instruction-following capabilities and extreme energy efficiency (0.75% battery for 25 conversations on Pixel 9 Pro).
Google introduces Gemma 3, a collection of lightweight open models (1B, 4B, 12B, 27B) designed to run on single GPUs or TPUs, featuring support for 140+ languages, 128k context window, and multimodal capabilities. The models outperform larger competitors like Llama 3 and DeepSeek-V3 while maintaining efficiency for on-device deployment.