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.
Chain-of-thought prompting elicits reasoning in large language models.Advances in neural infor- mation processing systems, 35:24824–24837
3 Pith papers cite this work. Polarity classification is still indexing.
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WaterAdmin uses a bi-level design with LLM agents for dynamic context abstraction and optimization for real-time pump/valve control, achieving better pressure reliability and lower energy use than traditional methods in EPANET simulations of variable community water demands.
Agent-BOM is a unified hierarchical attributed directed graph that models static capability bases and dynamic semantic states of LLM agents for path-level security auditing and risk assessment.
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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WaterAdmin: Orchestrating Community Water Distribution Optimization via AI Agents
WaterAdmin uses a bi-level design with LLM agents for dynamic context abstraction and optimization for real-time pump/valve control, achieving better pressure reliability and lower energy use than traditional methods in EPANET simulations of variable community water demands.
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Towards Security-Auditable LLM Agents: A Unified Graph Representation
Agent-BOM is a unified hierarchical attributed directed graph that models static capability bases and dynamic semantic states of LLM agents for path-level security auditing and risk assessment.