POLAR: Ontology-Guided Risk Prevention for Tool-Calling LLM Agents

Abstract:LLM tool-use agents operate in dynamic environments where many actions carry operational risk. However, most safety mechanisms react only after errors manifest. Existing pre-emptive approaches either fine-tune the agent on chain-of-thought deliberation or compile natural-language guardrails into runtime checks, but they do so without exposing a structural, auditable verdict. We propose POLAR, a guardrail framework for small tool-calling agents that assesses reversibility through a structured two-layer ontology. POLAR assigns each action a graded reversibility score by deriving a candidate inverse sequence; calls failing a threshold are pruned before execution. Evaluated on $\tau^2$-bench across six agent models, POLAR improves mean task reward by 0.11 to 0.18 points on airline for four of six agents, but only eight of eighteen model--domain cells improve overall; retail and stronger agents often regress. POLAR provides an auditable structural check and characterizes its task-utility trade-offs. Reward is not a direct measure of prevented harm.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.08082 [cs.AI]

(or arXiv:2610.08082v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.08082 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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