MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Them

TL;DR

MIRAGE-Bench is a comprehensive benchmark designed to systematically evaluate hallucinations in LLM-based agents, addressing a critical gap by providing a unified, reproducible, and risk-aware assessment framework for their failure modes.

Contribution

The paper introduces MIRAGE-Bench, the first unified, systematic benchmark for measuring hallucinations in interactive LLM agents, including a taxonomy, test case synthesis, and a fine-grained evaluation paradigm.

Findings

Identifies three types of agentic hallucinations: unfaithfulness to instructions, history, or environment.

Provides a reproducible test case synthesis method for isolating decision points.

Develops a risk-aware LLM-as-a-Judge paradigm for scalable evaluation.

Abstract

Hallucinations pose critical risks for large language model (LLM)-based agents, often manifesting as hallucinative actions resulting from fabricated or misinterpreted information within the cognitive context. While recent studies have exposed such failures, existing evaluations remain fragmented and lack a principled testbed. In this paper, we present MIRAGE-Bench--Measuring Illusions in Risky AGEnt settings--the first unified benchmark for eliciting and evaluating hallucinations in interactive LLM-agent scenarios. We begin by introducing a three-part taxonomy to address agentic hallucinations: actions that are unfaithful to (i) task instructions, (ii) execution history, or (iii) environment observations. To analyze, we first elicit such failures by performing a systematic audit of existing agent benchmarks, then synthesize test cases using a snapshot strategy that isolates decision points in deterministic and reproducible manners. To evaluate hallucination behaviors, we adopt a fine-grained-level LLM-as-a-Judge paradigm with tailored risk-aware prompts, enabling scalable, high-fidelity assessment of agent actions without enumerating full action spaces. MIRAGE-Bench provides actionable insights on failure modes of LLM agents and lays the groundwork for principled progress in mitigating hallucinations in interactive environments.