Textual Gradient-style optimizers (TextGrad) enable gradient-like feedback propagation through compound AI systems. However, they do not work well for deep chains. The root cause of this limitation stems from the Semantic Entanglement problem in these extended workflows. In standard textual backpropagation, feedback signals mix local critiques with upstream contexts, leading to Attribution Ambiguity. To address this challenge, we propose TextResNet, a framework that reformulates the optimization process to achieve precise signal routing via four key innovations. Firstly, in the forward pass, it enforces Additive Semantic Deltas to preserve an Identity Highway for gradient flow. Secondly, in the backward pass, it introduces Semantic Gradient Decomposition via a Semantic Projector to disentangle feedback into causally independent subspaces. Thirdly, it implements Causal Routing, which routes projected signals to their specific components. Finally, it performs Density-Aware Optimization Scheduling to leverage the disentangled signals to dynamically allocate resources to key system bottlenecks. Our results show that TextResNet not only achieves superior performance compared to TextGrad, but also exhibits remarkable stability for agentic tasks in compound AI systems where baselines collapse. Code is available at https://github.com/JeanDiable/TextResNet.

翻译：文本梯度风格优化器（TextGrad）能够通过复合AI系统传播类梯度反馈信号，但在深度链条中表现不佳。该局限性的根本原因在于其扩展工作流程中的语义纠缠问题。标准文本反向传播过程中，反馈信号会将局部批评与上游上下文混合，导致归因模糊性。为应对这一挑战，我们提出TextResNet框架，通过四项关键创新重构优化过程以实现精确信号路由：首先，前向传播中强制执行加性语义增量以保留用于梯度流的恒等高速通道；其次，反向传播中引入语义投影器实现语义梯度分解，将反馈解耦为因果独立子空间；第三，实施因果路由机制将投影信号定向至对应组件；最后，通过密度感知优化调度利用解耦信号动态分配计算资源至系统关键瓶颈。实验表明，TextResNet不仅较TextGrad取得更优性能，更在基线方法失效的复合AI系统智能体任务中展现出卓越稳定性。代码开源地址：https://github.com/JeanDiable/TextResNet