Agents' Last Doubt: Knowing When to Act and When to Step Back in Tool-Using Agents
Tool-using LLM agents repeatedly face decision points under uncertainty, choosing between autonomous action, additional information gathering, and human escalation.
ProofPaper ↗
Key points
- These decisions require balancing task success against interaction cost and the risk of irreversible errors.
- Using this dataset, we train SURE-Act, a controller that selects actions for a frozen backbone agent at inference time.
- Our experiments show that learning to rank alternative actions improves offline action-selection utility by 0.0309 over a regression-only baseline.
- This work establishes the first counterfactual dataset for ToolSandbox and provides actionable insights into decision-making under uncertainty for tool-using agents.
Sources (1)
- [1]Agents' Last Doubt: Knowing When to Act and When to Step Back in Tool-Using AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:11 PM
Tool-using LLM agents repeatedly face decision points under uncertainty, choosing between autonomous action, additional information gathering, and human escalation.
These decisions require balancing task success against interaction cost and the risk of irreversible errors.
Extractive summary: sentences quoted from the sources.
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