Recently, Google's latest research has proposed a revolutionary framework called "Reasoning Memory" (learnable reasoning memory), aiming to allow AI agents to accumulate knowledge from their own experiences and mistakes, achieving true "self-evolution." This innovation has the potential to solve the critical flaw of current large language model (LLM)-driven agents, pushing AI toward a more intelligent and autonomous direction.
The core pain point of current AI agents: unable to "grow" from experience
Although AI agents based on large language models perform well in reasoning and task execution, they generally lack a sustainable learning mechanism. According to AIbase, existing agents do not "evolve" after completing tasks: each execution is like starting from scratch, equivalent to "starting over." This leads to a series of problems, including repeated errors, inability to accumulate abstract experiences, wasting historical data, and limited decision optimization. The deeper reason is that even when memory modules are added, they are mostly limited to simple information caching (such as episodic memory), lacking the ability to generalize, abstract, and reuse experiences. As a result, AI agents struggle to form "learnable reasoning memory," making it difficult for them to truly achieve self-improvement.
Explanation of Google's new framework: How Reasoning Memory empowers self-evolution
The Google research team introduced the Reasoning Memory framework, a memory system specifically designed for AI agents, capable of accumulating, generalizing, and reusing reasoning experiences. According to AIbase, the core of this framework is to enable agents to extract abstract knowledge from their interactions, mistakes, and successes, forming learnable "reasoning memory." Specifically:
- Accumulate experience: Agents no longer discard task history but systematically record reasoning processes and results.
- Generalize and abstract: Algorithms convert specific experiences into general rules, avoiding mere episodic storage.
- Reuse and optimize: These memories are called upon in future tasks, adjusting decisions based on past experiences to reduce repeated errors.
This mechanism allows AI agents to "learn from mistakes" like humans, achieving closed-loop self-evolution. Experiments show that agents equipped with this framework significantly improve performance in complex tasks, marking a leap from static execution to dynamic growth.
Potential impact: AI agents moving toward a truly autonomous era
AIbase believes this research will reshape the AI application ecosystem. For example, in automated customer service, medical diagnosis, or game AI, agents can continuously optimize their strategies, reducing human intervention. In the long run, it fills the "evolutionary gap" of LLM agents, paving the way for building more reliable autonomous systems. However, challenges remain, such as the need to further verify memory generalization capabilities and computational costs. Google's move undoubtedly strengthens its leadership in AI frontiers, and it is worth close attention from the industry.
Paper URL: https://arxiv.org/pdf/2509.25140
