Advances in Computer and Communication Article Recommendation｜When Multi-agent Systems Begin to “Share Memories”: How Can AI Collaborative Development Evolve Toward Self-Evolution?

“When multiple AIs no longer work independently but begin to share their past experiences, can they really become smarter as a result?” “In the face of constantly changing development tasks, how long can a fixed AI collaboration framework continue to adapt?” “If AI can remember successes and failures, can it also reshape the way it works through repeated collaboration?” These questions are becoming unavoidable issues as multi-agent collaborative development moves toward increasingly complex applications.

Lianzhen Xia, a scholar from Monash University, Australia, focuses in the paper “Elastic Evolution Path of Task Memory Sharing Framework in Multi-agent Collaborative Development,” published in Advances in Computer and Communication, on how task memory-sharing frameworks can achieve elastic evolution in dynamic multi-agent environments. The paper points out that existing frameworks face problems such as rigid module architectures, static memory storage mechanisms, and convergent collaboration strategies, and further proposes optimization paths including dynamically reconfigurable architectures, iterative task memory management, and differentiated deep collaboration.

Website Screenshot

AI Collaborative Development: Is the Real Bottleneck Not “Whether AI Can Do It,” but “Whether AI Can Remember”?

Imagine a scenario in which multiple AI agents jointly participate in a complex software development project, with one responsible for planning, another for coding, another for testing, and others for analysis and troubleshooting. When encountering a problem for the first time, they may need to explore a solution from scratch; when facing a similar problem for the second time, if past experience can be effectively stored, retrieved, and shared, collaborative efficiency may change significantly. This is precisely the importance of task memory sharing. The paper argues that multi-agent collaborative development is not simply about putting multiple AIs together; it also requires solving the question of “how experience flows”: Which task experiences should be stored? When should they be updated? Which memories should be retrieved? And how should different agents share this information? From this perspective, AI “memory” is no longer merely a means of information storage, but may become an important infrastructure affecting collaborative capabilities.

From “Fixed Processes” to “Dynamic Evolution”: AI Frameworks Also Need to Learn How to Change

Traditional frameworks often operate according to predefined modules and processes. However, real-world development environments do not remain stable forever: task objectives change, agents differ in their capabilities, and collaborative relationships may also be continuously adjusted. If a framework remains dependent on fixed modules, static memory, and a single collaboration strategy, it may struggle to cope with complex tasks. The paper therefore proposes three major evolutionary paths: First, dynamically reconfigurable architectures. Functional modules can be reorganized according to changing task requirements, allowing the system to break free from the constraints of a fixed structure. Second, iterative task memory management. Shared memory can be continuously updated, while selective utilization can reduce the interference of irrelevant information with subsequent tasks. Third, differentiated deep collaboration. By taking into account the heterogeneity of different agents, the framework can strengthen deeper and more targeted collaborative mechanisms. The paper argues that these mechanisms may enhance the adaptability of the framework, the efficiency of memory resource utilization, and cross-agent collaborative capabilities.

When AI Begins to “Remember Experience”: What Should We Really Be Concerned About?

Questions inevitably follow. If AI can continuously accumulate task experience, could incorrect experiences also be preserved? If shared memory continues to grow, how can AI determine which information is worth retaining? If different agents have different capabilities and task roles, is a single shared memory really suitable for all agents? These questions suggest that the competition in AI collaborative development may be extending beyond model capabilities alone toward memory management, architectural adaptation, and collaboration mechanism design. The value of the paper lies in the fact that it does not simply interpret multi-agent collaboration as “adding more AIs,” but instead focuses further on how systems can adjust and evolve as tasks change.

From “Being Able to Collaborate” to “Being Able to Evolve”: What Changes Are Taking Place in AI Development?

Today, AI is moving from being a single question-and-answer tool toward increasingly sophisticated task-execution systems. Software development, intelligent decision-making, automated research, and other scenarios may all require multiple agents to work together to accomplish complex tasks. In such environments, a system with genuine continuous collaborative capabilities needs not only to complete the task at hand, but also to transform past experience into resources that can be utilized in future tasks. This gives “memory” a new meaning:

It does not merely preserve the past; it may also determine how the future will be approached.

The concept of “elastic evolution” essentially explores an even deeper question—when the external environment continues to change, can AI systems change the way they organize themselves, manage memory, and collaborate? Perhaps what will truly matter in the future is not simply “how intelligent a single AI is,” but whether a group of AIs can continuously accumulate experience, adjust their structures, and develop more flexible working mechanisms through sustained collaboration. AI is moving from a “tool for executing tasks” toward a “collaborative system that continuously accumulates experience.” So, when AI can not only complete tasks, but also remember tasks, share experiences, and change the way it collaborates—could the core competitiveness of next-generation artificial intelligence shift from “more powerful models” to “better learning how to collaborate”?

The study was published in Advances in Computer and Communication

How to cite this paper

Lianzhen Xia. (2026) Elastic Evolution Path of Task Memory Sharing Framework in Multi-agent Collaborative Development. Advances in Computer and Communication, 7(3), 181-186.

DOI: http://dx.doi.org/10.26855/acc.2026.09.014

https://www.hillpublisher.com/ArticleDetails/7072