Skip to content

WorldAgent: Verification-Guided Agentic Physical World Construction

Sep 2026 · 0 citations · 36 references
Computer Science

TL;DR

This work presents WorldAgent, an agentic framework for verification-guided physical world construction from a single natural-language prompt, without iterative user debugging, and introduces AgenticSimBench, on which WorldAgent achieves the best scores among the evaluated agent-based methods on five of seven metrics.

Abstract

Constructing complex physical worlds from language requires coordinating extensive 3D environments, detailed structures and objects at different spatial scales, and interacting physical processes under both stated goals and implicit physical constraints. We present WorldAgent, an agentic framework for verification-guided physical world construction from a single natural-language prompt, without iterative user debugging. A world construction layer expands the prompt into a structured world specification and uses physical knowledge to build scenes and run numerical simulations. After every step, a verification layer inspects scene geometry and simulation states alongside rendered views. Failed checks guide automatic revisions to the specification and re-execution of the affected steps. Accepted worlds pass the required checks and remain editable for further inspection and resimulation. We introduce AgenticSimBench, on which WorldAgent achieves the best scores among the evaluated agent-based methods on five of seven metrics. In a 26-participant user study, it receives the highest mean ratings across all four criteria.

View source

Similar papers

Preprint Sep 2026

Structured World-State Reasoning for Agentic Robotic Search

WorLDS: World-state Observation and Reasoning for Language-guided Discovery and Search, a framework that grounds reasoning in a persistent graph initialized from geospatial priors and updated by perception, is presented.

Finley R. Holt, Luis A. Pabon, J. Alora et al. · 0 citations
Preprint Sep 2026

RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation

RoboHarn-Evo is introduced, a dual-loop harness that evolves Hierarchical Physical Knowledge (HPK) from physical experience that demonstrates that physical interaction can be accumulated into reusable knowledge for improving subsequent manipulation.

Shi-Feng Bao, Fan-Ding Huang, Yi-Han Lin et al. · 0 citations
Preprint Aug 2026

Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning

Experiments show that Code-as-World-VL achieves state-of-the-art performance on QuantiPhy and surpasses leading proprietary models, highlighting the potential of executable world representations as a scalable foundation for physical intelligence.

Han-Yang Wang, Yiyang Cai, Wei-Liang Chen et al. · 10 citations · ⚡1
#artificial intelligence Preprint Oct 2026

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?

LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical testbed for reproducing complex physical phenomena with application in embodied AI, games and films. As the workhorse of such simulation, a solver computes how the stat...

Si-Ru Jiang, Yongzhe Lyu, Shuo Lu et al. · 0 citations
Conference Aug 2026

PRISM: Planning with Belief Under Incomplete World Models

Embodied task planning requires reasoning under uncertainty about world states that are partially observed and incrementally revealed through interaction. However, most vision-language model (VLM) planners operate in a reactive sense-plan-act loop without maintaining a persistent representation of object locations. As...

M. S. Yasar, Murugan Sankaradas, Srimat T. Chakradhar · 0 citations
#artificial intelligence Preprint Sep 2026

CodeActionBench: Evaluating Agentic Code-as-Policy for Embodied Manipulation

CodeActionBench is introduced, a benchmark of 25 manipulation tasks that evaluates this capability through agentic Code-as-Policy and provides a controlled testbed for measuring how general-purpose models translate their capabilities into manipulation behavior and for examining typical failure scenarios in that process...

Yiheng Lyu, Xueying Jiang, Wen-Hao Li et al. · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.