Tasks on complex systems require high-precision numerical computation to support decisions, but current large language models (LLMs) cannot integrate such computations as an intrinsic and interpretable capability with existing architectures. Multi-agent approaches can leverage external experts, but inevitably introduce communication overhead and suffer from inefficiency caused by limited scalability. To this end, we propose Physically-isolated Experts Routing Network (PiERN), an architecture for integrating computation and reasoning. Instead of the tool-use workflows or function-calling, PiERN endogenously integrates computational capabilities into neural networks after separately training experts, a text-to-computation module, and a router. At inference, the router directs computation and reasoning at the token level, thereby enabling iterative alternation within a single chain of thought. We evaluate PiERN on representative linear and nonlinear computation-reasoning tasks against LLM finetuning and the multi-agent system approaches. Results show that the PiERN architecture achieves not only higher accuracy than directly finetuning LLMs but also significant improvements in response latency, token usage, and GPU energy consumption compared with mainstream multi-agent approaches. PiERN offers an efficient, interpretable, and scalable paradigm for interfacing language models with scientific systems.

翻译：摘要：复杂系统任务需要高精度数值计算以支持决策，但现有大语言模型（LLMs）无法将此类计算作为其固有且可解释的能力融入现有架构。多智能体方法可借助外部专家，但必然引入通信开销，且因可扩展性受限导致效率低下。为此，我们提出物理隔离专家路由网络（PiERN），这是一种融合计算与推理的架构。不同于工具调用工作流或函数调用，PiERN在分别训练专家模块、文本到计算模块及路由器后，将计算能力内嵌于神经网络中。推理阶段，路由器在令牌级指引计算与推理路径，从而在单条思维链内实现迭代交替。我们在典型线性和非线性计算-推理任务上，将PiERN与LLM微调方法及多智能体系统方法进行了对比评估。结果表明，PiERN架构不仅比直接微调LLM具有更高准确率，同时相比主流多智能体方法在响应延迟、令牌使用量和GPU能耗方面均有显著改进。PiERN为语言模型与科学系统的接口提供了一种高效、可解释且可扩展的范式。