Back to Blog

Titans: Learning to Memorize at Test Time

Titans: Learning to Memorize at Test Time

Google released a new paper on "Neural Memory" integrating persistent memory directly into transformer architectures at test time, showing promising long-context utilization. MiniMax-01 by @omarsar0 features a 4 million token context window with 456B parameters and 32 experts, outperforming GPT-4o and Claude-3.5-Sonnet. InternLM3-8B-Instruct is an open-source model trained on 4 trillion tokens with state-of-the-art results. Transformer² introduces self-adaptive LLMs that dynamically adjust weights for continuous adaptation. Advances in AI security highlight the need for agent authentication, prompt injection defenses, and zero-trust architectures. Tools like Micro Diffusion enable budget-friendly diffusion model training, while LeagueGraph and Agent Recipes support open-source social media agents.

Read original post

Turn insight into implementation

Want help turning this idea into a production system?

xAGI Labs helps teams scope, build, and deploy AI products, agent workflows, voice systems, and enterprise rollouts.

If this topic is relevant to your roadmap, we can translate "Titans: Learning to Memorize at Test Time" into a concrete build plan and launch path.