Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows

Key points

• Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing accumulated context again.1
• Graph-based workflows therefore couple routing decisions across tasks.1
• Its allocation rule and payments, computed by integrating workload over possible cost reports, make truthful reporting utility-maximizing regardless of other providers' reports and cover each truthful provider's execution cost.1
• Controlled synthetic experiments separate the execution-cost benefit of topology awareness from the payment benefit of virtual-cost optimization: the former grows with fan-out and transferred payload, while the latter increases gradually along the tested prior-shape heterogeneity path.1

Sources (1)

• [1]Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 11:59 PM

  We formulate this routing problem as a single-parameter procurement auction and propose STAR (Strategy-proof Topology-aware Agent Routing), which minimizes virtual-cost-weighted graph workloads.

  Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing accumulated context again.

Extractive summary: sentences quoted from the sources.