Guys, Google has made a big splash in the AI field again! Recently, it proposed the revolutionary framework "Reasoning Memory" (learnable reasoning memory), aiming to enable AI Agents to achieve true "self - evolution", which is simply stunning 👏.

First, let's talk about the pain points of current AI agents. Currently, AI Agents based on large language models perform well in reasoning and task execution, but they generally lack a sustainable learning mechanism. AIbase analysis shows that existing intelligent agents do not "grow" after completing tasks. Each execution is like starting anew, which brings a bunch of problems. For example, they make repeated mistakes, can't accumulate abstract experience, waste historical data, and have limited decision - making optimization. Even if a memory module is added, most of them are just simple information caches, lacking the ability to generalize, abstract, and reuse experience. It's very difficult to form "learnable reasoning memory", and thus they can't truly improve themselves 😔.

Next, look at Google's new framework. The Reasoning Memory framework is a memory system specifically designed for AI agents, which can accumulate, generalize, and reuse reasoning experiences. Its core is to enable agents to extract abstract knowledge from their own interactions, mistakes, and successes to form "reasoning memories". Specifically:

• Experience Accumulation: Agents no longer discard task history, but systematically record the reasoning process and results.
• Generalization and Abstraction: Use algorithms to turn specific experiences into general rules, not just simple episodic storage.
• Reuse and Optimization: Call on these memories in future tasks, adjust decisions according to past experiences, and reduce repeated mistakes.

This mechanism allows AI agents to "learn from mistakes" like humans and achieve closed - loop self - evolution. Experiments show that agents equipped with this framework have a significantly improved performance in complex tasks. This is a huge leap from static execution to dynamic growth 😎.

Finally, let's talk about the potential impact. AIbase believes that this research can reshape the AI application ecosystem. In fields such as automated customer service, medical diagnosis, and game AI, Agents can continuously optimize their own strategies and reduce human intervention. In the long run, it fills the "evolution gap" of LLM agents and lays the foundation for building more reliable autonomous systems. However, there are also challenges. For example, the memory generalization ability and computational cost still need to be further verified. But anyway, Google's move has strengthened its leading position in the forefront of AI, which is worthy of attention from the industry 🤩.

Guys, what do you think of Google's new framework? Come and chat in the comments section 🧐.

Paper address: https://arxiv.org/pdf/2509.25140https://arxiv.org/pdf/2509.25140

Hashtags and keywords

#Google #AI Agent #Self - evolution #Reasoning Memory #AI Framework #AI Application Ecosystem