Get Started
Home
Topics
Search
Library
Research questionHow can text-based world models be evaluated for behavioral fidelity in long-horizon agent planning?A world model can predict the next textual state accurately while still changing which actions an agent would choose after several steps. This makes single-step state metrics unreliable for judging its usefulness in long-horizon planning.
AI
AI Agents
Evaluation & Benchmarks
Machine Learning
Natural Language Processing
Reasoning
Reinforcement Learning
Research Paper
Technology
Latest papersRecent research connected to this question, newest first.Beyond State Consistency: Behavior Consistency in Text-Based World ModelsThe evidence concerns text-based world models for interactive-agent assessment in WebShop and TextWorld, covering online lookahead planning and offline surrogate evaluation. Behavioral alignment is measured through changes in logged next-action likelihood under a frozen reference agent; gains are strongest in WebShop, smaller in near-ceiling settings, and modest for inference-time planning.research paper · Sep 2, 2026
Related questions
How can language agents adapt textual world models to evolving behavior in interactive environments without external rewards?How can agents choose task-dependent world-model rollout horizons for effective multi-step planning?When a model-based agent performs well, how can we verify its visual world model supports reliable planning and transfer?How can multimodal language-model agents coordinate hidden prerequisites during long-horizon open-world exploration?