SDP constructs a task-induced state space from raw text by having agents commit to and certify natural-language predicates as states, enabling structured planning and analysis in unstructured language environments.
Reasoning with language model is planning with world model
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A staged LLM pipeline synthesizes verifiable discrete-event world models from natural language specifications using the DEVS formalism for long-horizon consistency in LLM agents.
This survey frames foundation agents using brain-inspired modular architectures and reviews challenges in evolution, collaboration, and safety.
citing papers explorer
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State-Centric Decision Process
SDP constructs a task-induced state space from raw text by having agents commit to and certify natural-language predicates as states, enabling structured planning and analysis in unstructured language environments.
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Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism
A staged LLM pipeline synthesizes verifiable discrete-event world models from natural language specifications using the DEVS formalism for long-horizon consistency in LLM agents.
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Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
This survey frames foundation agents using brain-inspired modular architectures and reviews challenges in evolution, collaboration, and safety.