Asking Earns Nothing: Scoring the Decision to Act in BFCL Multi-Turn

cs.AI

Submitted: 2026-10-03

Updated: 2026-10-03

Comments: 19 pages, 2 figures. Code and data: https://github.com/YangzeLiu/asking-earns-nothing

Code: https://github.com/YangzeLiu/asking-earns-nothing

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Benchmarking the Benchmarks: A Validity Audit of Tool-Calling Evaluation
• Coding Agents Are Guessing: Measuring Action-Boundary Violations in Underspecified DevOps Instructions
• AgentAbstain: Do LLM Agents Know When Not to Act?
• Agentic Abstention: Do Agents Know When to Stop Instead of Act?
• Categorizing Variants of Goodhart's Law
• Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
• Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents
• HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?
• Interface-Induced Trajectory Censoring
• Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling

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