The Verifier Tax: Horizon-Dependent Safety–Success Tradeoffs in Tool-Using LLM Agents [R]

Reddit r/MachineLearning 06/14/26, 02:09 AM Papers

safety tool-use llm-agents verification evaluation tradeoff

Summary

This paper presents a safety evaluation framework for tool-using LLM agents, introducing the concept of the 'Verifier Tax'—a horizon-dependent tradeoff between safety and task completion. It proposes a two-tier verification architecture and uses Tau-bench scenarios to demonstrate how verification can reduce unsafe successes but also decrease task completion as task horizon increases.

We recently presented a paper at ACM CAIS 2026 on safety evaluation for tool-using LLM agents. The core issue is that task completion alone can be misleading: an agent may complete a task while violating a safety or policy constraint. We separate outcomes into **safe success**, **unsafe success**, and **failure**, and study how verification changes this tradeoff. We evaluate this using **τ-bench / Tau-bench** tool-use scenarios and propose a **two-tier verification architecture**: deterministic policy/tool checks first, followed by an LLM-based verifier for more contextual safety cases. The main finding is that verification can reduce unsafe success, but it can also reduce task completion as the task horizon increases. This creates what we call the **Verifier Tax**: a horizon-dependent safety–success tradeoff in tool-using agents. Paper: [https://dl.acm.org/doi/full/10.1145/3786335.3813160](https://dl.acm.org/doi/full/10.1145/3786335.3813160) Curious how others think agent evaluations should report unsafe success. Should unsafe completion be counted as success, failure, or a separate category?

Original Article

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