What Do LLM Agents Know About Their World? Task2Quiz: A Paradigm for Studying Environment Understanding

2026-01-14

AI Summary

Current evaluation methods for large language model agents rely excessively on task success rates, failing to adequately assess their genuine understanding of the environment. To address this limitation, this work proposes the Task-to-Quiz (T2Q) evaluation paradigm and introduces T2QBench—a benchmark comprising 30 environments and 1,967 question-answer pairs—that decouples task execution from environmental state comprehension for the first time. The framework systematically evaluates agents’ world models through deterministic question generation, multi-difficulty environment modeling, fine-grained state representations, and an automated evaluation pipeline. Experimental results reveal that even when agents successfully complete tasks, they generally lack transferable environmental understanding, and existing memory mechanisms prove insufficient for effective environment modeling.

Technology Category

• Natural Language Processing: Question Answering
• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large language model (LLM) agents have demonstrated remarkable capabilities in complex decision-making and tool-use tasks, yet their ability to generalize across varying environments remains a under-examined concern. Current evaluation paradigms predominantly rely on trajectory-based metrics that measure task success, while failing to assess whether agents possess a grounded, transferable model of the environment. To address this gap, we propose Task-to-Quiz (T2Q), a deterministic and automated evaluation paradigm designed to decouple task execution from world-state understanding. We instantiate this paradigm in T2QBench, a suite comprising 30 environments and 1,967 grounded QA pairs across multiple difficulty levels. Our extensive experiments reveal that task success is often a poor proxy for environment understanding, and that current memory machanism can not effectively help agents acquire a grounded model of the environment. These findings identify proactive exploration and fine-grained state representation as primary bottlenecks, offering a robust foundation for developing more generalizable autonomous agents.

Problem

• LLM agents
• environment understanding
• generalization
• evaluation paradigm
• world-state representation

Innovation

• Task-to-Quiz
• environment understanding
• LLM agents
• T2QBench
• grounded reasoning