Gemini 3.7 Flash

2026-10-14

RESEARCH · 1 SOURCE · The Decoder

DeepMind says AI-driven self-improvement remains far off despite automation gains

DeepMind engineer Haydn Belfield reports no measurable acceleration of AI research driven by AI itself, based on a General Capability Index covering 123 models and 92 benchmarks; the team finds Gemini 4 Argon solves about half of 74 RE-Bench ML engineering tasks (vs. 27% for Gemini 3.7 Flash) and rates the risk from AI-driven research automation as currently low. Belfield also notes AI writes over 75% of production code but 88% of code changes still require human oversight and every change is reviewed by a human before submission.

7.0

2026-10-09

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

Google releases Android Bench 2.0 with long‑horizon tasks, agentic evaluation, and continuous scoring

Google published Android Bench 2.0, a major update to its benchmark for evaluating AI models on Android development tasks that adds long‑horizon tasks (LHTs), agentic evaluation (using providers' agents), and a continuous, multi‑factor scoring system instead of binary pass/fail. The new dashboard lists recent models (e.g., Gemini 3.8/3.7, OpenAI GPT‑6, Anthropic Fable 5.1, Kimi K3, Qwen 3.8 Max); at the time of reporting Claude Opus 5.5 led LHT pass rates at 32%, with GPT 6 Astra at 28%.

7.0

2026-09-28

MODELS · 1 SOURCE · Google

Google showcases community builds using Gemini 3.8 Flash

Google highlights developers using Gemini 3.8 Flash, which it says improves multistep reasoning and latency versus Gemini 3.7 Flash. Example builds include satellite-tracking with Google Antigravity, a multimodal Seigaiha animation, a stepwise T. rex skeleton prompt, and an interactive automatic-transmission model; the model is available across Google tools including Google Antigravity and Google AI Studio.

6.0

2026-09-19

RESEARCH · 1 SOURCE · The Decoder

Google DeepMind's Dream-RSI replays past searches to learn better exploration strategies

Researchers at Google and DeepMind introduced Dream‑RSI, a method that reuses recorded search histories to cheaply test alternative search strategies without rerunning expensive evaluations. In tests with Gemini 3.1 Pro and Gemini 3.7 Flash across code, math, and GPU-kernel tasks, Dream‑RSI reduced attempts and runtime, outperformed a competing system (SimpleTES) on some benchmarks, and matched or improved GPU-task performance while cutting computation; code and details are on GitHub.

7.0

2026-08-13

MODELS · 1 SOURCE · Google DeepMind

Introducing Gemini 3.7 Flash (Google DeepMind)

Google DeepMind published an announcement titled "Introducing Gemini 3.7 Flash." No article text or further details were provided in the source, so the model's capabilities, release timing, and availability cannot be confirmed from this item.

7.0