Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents

Apple Machine Learning Journal / 5/1/2026

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Key Points

The paper argues that current evaluations of tool-calling agents largely rely on post-hoc judgments that happen after execution, which limits their ability to correct mistakes in real time.
It proposes moving evaluation into the inference-time execution loop by using a specialized “reviewer” agent to assess the agent’s trajectory during tool calling.
The approach targets improvements in tool selection, parameter accuracy, and scope recognition by enabling feedback that can influence subsequent decisions while the interaction is still ongoing.
The work was accepted at the ACL 2026 Fifth Workshop on Natural Language Generation, Evaluation, and Metrics.

This paper was accepted at the Fifth Workshop on Natural Language Generation, Evaluation, and Metrics at ACL 2026. Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-hoc. Disconnected from the active execution loop, such assessments identify errors that are usually addressed through prompt-tuning or retraining, and fundamentally cannot course-correct the agent in real time. To close this gap, we move evaluation into the execution loop at inference time: a specialized reviewer agent evaluates…

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