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Introducing Gemma 4 12B: a unified, encoder-free multimodal model
Today, we are introducing Gemma 4 12B, our latest model designed to bring agentic multimodal intelligence directly to laptops. Bridging the gap between our edge-friendly E4B and our more advanced 26B Mixture of Experts (MoE), Gemma 4 12B packages powerful capabilities inside a reduced memory footprint. It is also our…
Google DeepMind
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An environment gives an agent a task, responds to its actions with observations, and scores the outcome. The resulting rewards can measure an agent's performance during evaluation or provide a learning signal during training. For an introduction to this interaction loop, see our blogpost on environments . Within the e…
Hugging FacePublished Sep 28, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents (read it on Hugging Face , or on arXiv in the meantime), targets that gap. The failure mode we care about is one we call cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Evaluation, however, hasn't kept pace: it remains fragmented and unstandardized. The gold standard is human preference scores such as MOS or MUSHRA (more on metrics ). To this end, several arena-based leaderboards have established themselves as useful reference points for the community:
Hugging FacePublished Sep 30, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026