CITA (arXiv:2610.02330v1) proposes CIM to estimate next-step value in long-horizon tool use
The paper (arXiv:2610.02330v1) introduces Comparative Inference for Tool-use Agents (CITA), which trains a Comparative Inference Model (CIM) to estimate the long-horizon value of candidate next tool invocations before execution. CIM is trained from paired signals combining observed tool behavior, scalable supervision from a Bayesian tool-graph simulator, and LLM-based semantic comparisons, and the authors report consistent improvements in Tool F1 and task success across three tool-use benchmarks and multiple backbone LLMs.