Noam Shazeer : le cerveau de Gemini lâche Google pour OpenAI
Noam Shazeer, key researcher in Gemini's development at Google, is leaving the company to join OpenAI. This departure marks a significant shift in competition between the two AI giants.
Gemini is Google DeepMind's family of multimodal AI models, designed to process text, images, audio, and video together. For example, Gemini 1.5 Pro can analyze long documents and videos within a single prompt.
Noam Shazeer, key researcher in Gemini's development at Google, is leaving the company to join OpenAI. This departure marks a significant shift in competition between the two AI giants.
GLM 5.2 generates videos via Remotion, comparable to Fable but below Gemini 3.1 Pro. Server overload observed on OpenRouter with timeouts on long outputs.
Gemma 4 E2B runs in-browser at 255 tokens/sec using WebGPU kernels optimized by Fable 5. Demo and kernels released on Hugging Face.
7B model fine-tuned to predict next step in concurrent Go programs by learning event distributions rather than single labels. On 798 predictions from real bugs (CockroachDB, Kubernetes, gRPC, etcd), achieves 36.2% accuracy with <1000 traces, outperforming Gemini 3.5 Flash zero-shot (34.8%). Dataset, adapters, and tooling released.
DiffusionGemma generates 256 tokens in parallel with bidirectional attention, enabling self-correction before finalization. Unlike autoregressive models locked after each token, this architecture could improve structured tool calls despite lower base quality than Gemma 4. Testing needed to confirm if bidirectional correction compensates for lower quality.
A user shares a system instruction to improve reasoning in Gemma 12b QAT. The technique aims to reduce cognitive bias and adapt reasoning depth to context. It works well on trick questions but partially fails on certain problems depending on framing.
The FBI and Google dismantled a Chinese cybercriminal network using Gemini for attacks. Google responded to these platform abuses.
Amazon accidentally revealed Google's Pixel Drop ahead of its official announcement. Three new AI features for Pixel smartphones were exposed prematurely.
ExecuTorch integrates Gemma 4 into React Native with GPU acceleration: Vulkan on Android, MLX on Apple Silicon. Fully offline execution.
LoSoNA is a benchmark measuring LLM ability to recognize and adapt to local social norms in group chats. Eight frontier and open-weight models tested under four prompting conditions: Gemini 3.1 Pro reaches 84.2%, Claude Fable 5 81.6%. Explicit norm-aware prompting helps unevenly.
Fine-tuning vision-language models on dense coordinate lists improves visual grounding but induces spurious repetitions. On Gemma 4 12B, high-capacity LoRA raises F1@0.3 from 0.007 to 0.448 but creates duplicate rate 0.080. Object-level control removes repetitions (rate 0.000) while preserving performance (F1 0.490).
Gemma 4 models exhibit repetition loops on long enumerations (up to 95% failure rate). Per-neuron ablation identifies a few MLP neurons responsible: suppressing them via weight edits removes simple loops but not 'doom loops' (infinite self-correction), limited by knowledge gaps rather than removable circuits.
Evaluation of commercial vs open-source LLMs on Islamic inheritance legal reasoning (QIAS 2026 shared task). Gemini 2.5 Flash achieves best performance (MRE 0.989), while open-source models show greater instability in dependent legal decisions and fractional share adjustments.
Gemma 12B running on Google Pixel 10 Pro via Termux and llama.cpp (v9639) consumes under 10W. Performance: 6.5 tokens/s prompt, 1.3 tokens/s generation with 32k context and Q3_K_XL quantization.
Gemma 4 benchmarked on triple GPU setup (3× GTX-1070, 24 GiB VRAM total). Gemma-4-26B-A4B-qat achieves 123.5 t/s prompt processing and 53.08 t/s generation. Gemma-4-E4B-BF16 reaches 302.16 t/s but limited to 11.54 t/s generation. Tests on llama.cpp build 9204 with GGUF quantizations.
User runs Gemma 12B QAT on GTX 1080 Ti (9 years old) at 50 tok/sec. Setup includes speculative decoding with MTP draft model and Q4_K_XL quantization. Seeking further optimizations.
Google Research's Gemini-SQL2, built on Gemini 3.1 Pro, achieves 80.04% accuracy on the BIRD benchmark for natural language-to-SQL conversion, significantly outperforming OpenAI and Anthropic. Google plans to integrate this technology into its data services.
H100 (FP8) benchmark: DiffusionGemma 26B generates 763 tok/s (3.7s) vs Gemma4 218 tok/s (15.1s), but produces 28 factual errors across 61 tested facts vs 5 for Gemma4. DiffusionGemma invents names, dates, numbers (Clara Clley as Jobs' mother, BeBox at $9,999 instead of $1,600). The diffusion model generates 256 tokens simultaneously and polishes text without verifying factuality.
Google integrates Gemini into Google TV to adjust image settings. The feature enables AI to control visual parameters, but limitations remain according to the article.
Apple integrates Google's Gemini into Siri at WWDC on June 8, but not as the primary engine. The article challenges the dominant interpretation of this partnership and explores Gemini's actual role in Siri AI's architecture.
Open Dungeon is a local roleplay game using Gemma 4 QAT (12B) via Ollama for narration and FLUX for image generation. Runs on 7.7GB RAM with full 256K context, no APIs or cloud. Features Do/Say/Story modes, line editing, model selection. MIT licensed, source available.
Shopping Reasoning Bench: expert-authored benchmark of 525 missions (232 single-turn, 293 multi-turn) with 10,863 importance-weighted binary rubrics for evaluating conversational shopping assistants. Evaluation of 9 models (GPT, Claude, Gemini): pass rates 57–77%, performance degrades 4–18 points across conversation turns, 13–29 point gap between required and optional criteria.
Empirical comparison of Gemma and Qwen quantizations across three tasks (arithmetic, presidential dates, attention). Gemma-4-31B-Q4_K_S reaches 83.8% arithmetic and 87% attention accuracy. Qwen3.6-27B-Q4_K_S achieves 95.5% arithmetic and 100% presidents. Results demonstrate major impact of model size and quantization scheme on accuracy.
Four quantized and uncensored Gemma 4 variants released: 12B, 12B QAT, 26B-A4B QAT, and 31B QAT. Multiple formats available (Safetensors, GGUF, NVFP4, GPTQ-Int4) on Hugging Face. Uncensored 'heretic' versions.
DiffusionGemma 26B A4B quantization benchmarks (Q6_K 22GB, Q4_K_M 16GB) on RTX 5090. Stable context: 6,144 tokens (Q6_K) and 10,240 tokens (Q4_K_M) limited by disabled Flash Attention on SM120. Optimal parameters and llama.cpp invocations documented.
Google introduces DiffusionGemma, an experimental text generation model 4x faster than standard approaches. The model rethinks the architecture of text generation.
Google launches Gemini 3.5 Translate to translate conversations in real-time across 70+ languages without altering original content.
APEX automatically optimizes prompts by dynamically selecting training data. The framework stratifies the dataset into Easy, Hard, and Mixed tiers, prioritizing the Mixed frontier to generate informative mutations. Within 5,000 evaluation calls, APEX improves performance by 11.2% on Gemini 2.5 Flash and 6.8% on Gemma 3 27B.
Google releases DiffusionGemma-26B, an open-weight Gemma model (Apache 2 license) based on its May 2024 Gemini Diffusion research. The model generates text at 500+ tokens/second. NVIDIA hosts it free on NIM cloud API.
Google releases DiffusionGemma, a 26B-parameter model generating text via diffusion (noise → text) rather than token-by-token. Achieves ~1,000 tokens/sec on H100 (4× faster than comparable autoregressive models), but lower output quality. Positioned as experimental tool for developers.
Google releases DiffusionGemma, a diffusion-based variant of Gemma with 26B parameters and 4B active. Claims 700+ tokens/sec throughput on RTX 5090.
DiffusionGemma achieves 4x faster text generation by using diffusion instead of autoregressive decoding. Built on Gemma, the model applies diffusion techniques to parallelize generation and reduce latency.
Google releases a developer guide for DiffusionGemma, its diffusion-based image generation model. The guide covers integration, optimization, and practical use cases for developers.
Gemini experiences outage affecting multiple users with error messages and mobile bugs. Google maintains official silence on the incident.
Google upgrades NotebookLM with Gemini 3.5 Flash, cloud-based code execution, and autonomous source discovery via Google Search. Internal tests show the new version outperforms the previous one 78.2% of the time.
User reports Gemma 4 12B (unified audio/vision/text model) ignores audio input when system prompt exceeds ~21k tokens. Model works well with minimal prompt but generates generic/hallucinated responses with dense context. Behavior reproduced across vLLM, llama.cpp, and LiteRT-LM. Appears to be an inherent attention saturation limit.
Researchers demonstrate that AI systems used for scientific peer review are vulnerable to simple manipulation: superficially rephrasing a manuscript abstract improves acceptance scores by 38% without changing scientific content. The attack costs ~$1 and takes 5 minutes, affecting Gemini 3 Flash and GPT 5.4 Mini reviewers.
A German court ruled Google liable for false answers generated by its AI Overviews system. The decision establishes that Google must verify the reliability of AI-generated content before displaying it to users.
Community challenge to optimize Gemma 4 E4B inference on A10G GPU. Participants test acceleration techniques in real-time to reduce latency and increase throughput on a single card.
Google releases Gemini 3.5 Live Translate, an audio model for real-time voice translation across 70+ languages. The system translates continuously without waiting for sentence completion and preserves speaker tone, pace, and pitch. Google Meet support expands from 5 to 70+ languages.