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

• URL: http://arxiv.org/abs/2601.09503v1
• Date: Wed, 14 Jan 2026 14:09:11 GMT
• Title: What Do LLM Agents Know About Their World? Task2Quiz: A Paradigm for Studying Environment Understanding
• Authors: Siyuan Liu, Hongbang Yuan, Xinze Li, Ziyue Zhu, Yixin Cao, Yu-Gang Jiang,
• Abstract summary: Large language model (LLM) agents have demonstrated remarkable capabilities in complex decision-making and tool-use tasks.<n>We propose Task-to-Quiz (T2Q), a deterministic and automated evaluation paradigm designed to decouple task execution from world-state understanding.<n>Our 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.
• Score: 50.35012849818872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Automatic Cognitive Task Generation for In-Situ Evaluation of Embodied Agents [43.01384379901339]
  We propose a dynamic in-situ task generation method for unseen environments inspired by human cognition.<n>In the interaction stage, the agent actively interacts with the environment, creating a loop between task execution and generation.<n>Experiments across 10 unseen scenes demonstrate that TEA automatically generated 87,876 tasks in two cycles.
  arXiv  Detail & Related papers  (2026-02-05T03:07:00Z)
• The Hierarchy of Agentic Capabilities: Evaluating Frontier Models on Realistic RL Environments [0.11586753333439907]
  We present an empirical study evaluating frontier AI models on 150 workplace tasks within a realistic e-commerce RL environment from Surge.<n>Our analysis reveals an empirically-derived emphhierarchy of agentic capabilities that models must master for real-world deployment.<n>Weaker models struggle with fundamental tool use and planning, whereas stronger models primarily fail on tasks requiring contextual inference beyond explicit instructions.
  arXiv  Detail & Related papers  (2026-01-13T23:49:06Z)
• CuES: A Curiosity-driven and Environment-grounded Synthesis Framework for Agentic RL [35.086788669916594]
  Large language model based agents are increasingly deployed in complex, tool augmented environments.<n>Existing approaches typically assume predefined task collections, an assumption that fails in novel environments.<n>We propose CuES, a Curiosity driven and Environment grounded Synthesis framework that autonomously generates diverse, executable, and meaningful tasks.
  arXiv  Detail & Related papers  (2025-12-01T06:11:37Z)
• Scaling Environments for LLM Agents in the Era of Learning from Interaction: A Survey [30.673419015614233]
  A growing consensus is that agents should interact directly with environments and learn from experience through reinforcement learning.<n>We formalize this iterative process as the Generation-Execution-Feedback (GEF) loop, where environments generate tasks to challenge agents, return observations in response to agents' actions during task execution, and provide evaluative feedback on rollouts for subsequent learning.<n>Under this paradigm, environments function as indispensable producers of experiential data, highlighting the need to scale them toward greater complexity, realism, and interactivity.
  arXiv  Detail & Related papers  (2025-11-12T12:56:25Z)
• Grounded Test-Time Adaptation for LLM Agents [75.62784644919803]
  Large language model (LLM)-based agents struggle to generalize to novel and complex environments.<n>We propose two strategies for adapting LLM agents by leveraging environment-specific information available during deployment.
  arXiv  Detail & Related papers  (2025-11-06T22:24:35Z)
• Agent Learning via Early Experience [93.83579011718858]
  A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks.<n>Most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly.<n>We study two strategies of using such data: (1) Implicit world modeling, which uses collected states to ground the policy in environment dynamics; and (2) Self-reflection, where the agent learns from its suboptimal actions to improve reasoning and decision-making.
  arXiv  Detail & Related papers  (2025-10-09T17:59:17Z)
• Towards General Agentic Intelligence via Environment Scaling [78.66355092082253]
  Advanced agentic intelligence is a prerequisite for deploying Large Language Models in real-world applications.<n>We design a scalable framework that automatically constructs heterogeneous environments that are fully simulated.<n>Experiments on agentic benchmarks, tau-bench, tau2-Bench, and ACEBench, demonstrate that our trained model, AgentScaler, significantly enhances the function-calling capability of models.
  arXiv  Detail & Related papers  (2025-09-16T17:57:20Z)
• Agent Planning with World Knowledge Model [88.4897773735576]
  We introduce parametric World Knowledge Model (WKM) to facilitate agent planning.<n>We develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning.<n>Our method can achieve superior performance compared to various strong baselines.
  arXiv  Detail & Related papers  (2024-05-23T06:03:19Z)
• Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies [57.27944046925876]
  We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph. Instead of directly learning a meta-policy, we develop a Meta-learner with Subtask Graph Inference. Our experiment results on two grid-world domains and StarCraft II environments show that the proposed method is able to accurately infer the latent task parameter.
  arXiv  Detail & Related papers  (2020-01-01T17:34:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.