Researchers propose Comparative Inference for Tool-Use Agents
The paper argues that long‑horizon tool‑use agents should estimate the value of a potential next tool invocation before executing it. It introduces Comparative Inference for Tool‑use Agents (CITA), which trains a Comparative Inference Model (CIM) from paired signals that combine observed tool behavior, a Bayesian tool‑graph simulator, and LLM‑based semantic judgments.
Key points
- CITA trains a Comparative Inference Model using paired signals from tool behavior, simulator, and LLM judgments.
- CITA improves Tool F1 and task success across three benchmarks and multiple LLM backbones.
- CIM learns accurate step‑level value estimates for comparative tool choices.
CITA is evaluated on three tool‑use benchmarks with multiple backbone LLMs. The results show consistent improvements in Tool F1 and overall task success. Analysis indicates that CIM learns accurate step‑level value estimates for comparative tool choices, offering a more targeted feedback signal than final‑outcome rewards.
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