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DeepMind's SIMA 2 plays any game, MiniMax trains 2.7T model

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Saturday, August 22, 2026

DeepMind's SIMA 2 plays any game, MiniMax trains 2.7T model

DeepMind released SIMA 2, a Gemini-powered agent that plays any game like humans, while NVIDIA's AVO agent scored 100 on ARC-AGI-3 using 12% fewer actions. China's MiniMax is training a 2.7 trillion parameter model, the largest open-weight model yet.

Top Stories

1
Exploring new frontiers of AI and games research — Google DeepMind

Google DeepMind

Google DeepMind is shifting from AI that masters games to SIMA 2, a generalist agent powered by Gemini that understands and plays any game through natural language and visual input, partnering with game studios to create adaptive NPCs and transform game development.

deepmindagentsgaminggemini
2
NVIDIA's AVO Hits 100 on ARC-AGI-3, Uses 12% Fewer Actions

Nvidia

NVIDIA's AVO agent architecture scored perfectly on the ARC-AGI-3 public benchmark using 12% fewer actions than comparable systems, demonstrating that long-horizon agent capability depends on system design (persistent memory, supervision, feedback loops) beyond just model quality. The same architecture previously achieved state-of-the-art results optimizing GPU kernels, proving its transferability across vastly different reasoning tasks.

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3
China's MiniMax Plans Launch Giant 2.7 Trillion Parameter Model

Reuters

MiniMax is developing a massive 2.7 trillion parameter open-weight model targeting Q3 release, marking a major escalation in China's AI capability race and the broader competition for largest-scale models.

llmopen-sourcefoundation-modelschina-ai
4
ChatGPT Ads Expand to Europe

OpenAI

OpenAI is launching ChatGPT Ads across 31 European markets next week, its largest expansion yet, offering advertisers access to users during active decision-making moments while keeping paid tiers ad-free and maintaining privacy safeguards.

openaichatgptadvertisingmonetization
5
A generated image may have no identifiable source image

Nature Communications

Research shows diffusion model outputs typically cannot be attributed to specific training images, undermining copyright traceability arguments and complicating provenance claims for AI-generated content.

diffusion-modelsgenerative-aicopyrightai-provenance

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