Distributed artificial intelligence (AI) often operates under sequential task exposure, uneven compute, and decentralized coordination. Here, we present a cancer-inspired, or oncomorphic, multi-agent framework in which simulated neural agents can replicate, mutate their neural network architecture, migrate across task environments, undergo ecological turnover, and recruit learning/ecological resources from a finite shared reserve. We evaluate the framework in controlled synthetic nonlinear classification environments in which each agent trains only on its local task, allowing population ecology rather than centralized optimization to determine which neural network architectures persist. For various initial conditions, we find that stronger selection increased the endpoint local accuracy of surviving agent populations. Architecture mutation played a state-dependent role: diverse initial populations performed best at low mutation, whereas clonal large-architecture populations benefited from mutation-generated variation. Selection also increased end-of-run multi-task competence, measured by evaluating surviving agents on all environments without additional training. Recruitment and elevated baseline replication reshaped demographic support while prediction quality remained within a narrow band, consistent with redistribution of finite learning resources. Time-resolved entropy and dominance analyses revealed concentration toward successful architectures, while finite training cycles kept agents in a non-asymptotic learning regime. These results provide proof-of-concept mechanistic evidence that oncomorphic population dynamics may offer a route to decentralized adaptation in engineering applications under bounded local resources.

翻译：分布式人工智能（AI）常面临顺序任务暴露、计算资源不均及去中心化协调等挑战。本文提出一种受癌症启发的多智能体框架（即癌态框架），其中模拟神经智能体可复制、变异其神经网络架构、跨任务环境迁移、经历生态更替，并从有限共享储备中征用学习/生态资源。我们在受控的合成非线性分类环境中评估该框架：每个智能体仅在其局部任务上训练，由种群生态学而非集中优化决定何种神经网络架构得以存续。针对不同初始条件，我们发现更强选择压力能提升存续智能体种群的最终局部准确率。架构突变发挥状态依赖作用：多样化初始种群在低突变率下表现最优，而克隆性大规模架构种群则从突变产生的变异中获益。选择还能提升运行终期的多任务能力（通过评估存续智能体在所有环境中的表现，无需额外训练）。征用机制与基线复制率提升重塑了人口统计数据支持，而预测质量保持在窄带范围内，这与有限学习资源的重新分配相一致。时间分辨的熵与优势度分析揭示了向成功架构的集中趋势，同时有限训练周期使智能体处于非渐近学习状态。这些结果提供了概念验证性机制证据，表明在资源受限的工程应用中，癌态种群动力学可能为去中心化自适应提供新路径。