The proposed representation and its engineering framework jointly establish a general computational interface between agent reasoning and capability-bound physical transformations, providing a foundation for end-to-end autonomous scientific discovery.
Abstract
Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computable representation of the physical laboratory is established through typed research objects, capability-bound operations and a compositional workflow algebra. It provides the physical-world counterpart to machine-readable knowledge, expressing workflows as programs over evolving laboratory states with explicit dependencies, decisions, iteration and concurrency. The representation was implemented in a modular agentic robotic laboratory by binding formal operations to executable Function Skills. For diverse scientific intents, capability-relative workflows were generated, while stateful simulation propagated object transformations and verified operation preconditions and laboratory constraints before dispatch. The proposed representation and its engineering framework jointly establish a general computational interface between agent reasoning and capability-bound physical transformations, providing a foundation for end-to-end autonomous scientific discovery.
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.
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.
Caoliwen Wang, Meng-Di Wang, Yi-Ge Chen et al.· 0 citations
This review examines how current systems translate materials questions into computational tasks, identifies the settings in which reliability and recovery have been demonstrated, and explains why runnable calculations should be distinguished from scientifically justified conclusions.
This work formalizes the reasoning-execution boundary as a typed contract and constrains language-level decisions through schema-validated tool calls defined by the Model Context Protocol, rejecting malformed commands before they reach the robot.
GRACE is pro-posed, an agent framework that adopts Executable Analytic Concepts (EAC) as a core knowledge annotation paradigm for object understanding and supports automated concept discovery, offering a scalable and modular solution for high-precision embodied intelligence.
Mingyang Sun, Jiu-De Wei, Qi-Chen He et al.· Proceedings of the Thirty-Fi...· 0 citations
Agentic systems offer a promising way to automate embodied benchmark construction, but existing approaches typically cover isolated stages or remain specialized to predefined environments and task families. More importantly, multi-step construction produces dependent intermediate artifacts that are often passed downstr...
Bao-Yang Jiang, Feng-Chun Zhang, Le-Yuan Wang et al.· 0 citations