GBC Uses Token-Level Gradient Weights to Optimize Multi-Agent LLM Pipelines

Gradient-Based Connections models a multi-agent LLM system as a computational graph and assigns token-level influence weights to each agent's output, enabling fine-grained credit assignment across agents. This replaces coarse reward signals with differentiable attribution, allowing targeted optimization of which agents and interaction steps drive errors.