10 stories tagged Planning
Researchers developed a new AI planning method called GATS that reduces computational costs and improves reliability by eliminating LLM calls during inference. It could make AI assistants more efficient at complex tasks.
Researchers studied how AI models make mistakes when planning in networked environments. Their findings could improve AI tools that manage interconnected systems like supply chains or social networks.
Researchers compare two approaches to world models for AI agents—agent-based and parameterized—showing that parameterized models reduce hallucination propagation by using measurable errors, leading to more reliable planning in step-by-step tasks.
Researchers developed a new method to improve AI's ability to plan complex tasks. This could make AI assistants more reliable for long-term decision-making.
Researchers developed LAGO, a system that helps AI robots plan tasks by understanding human language. This could make robots more useful in everyday settings. The framework predicts intermediate goals from language, reducing errors in planning.
Researchers introduced DecomposeR, a new AI framework that improves how large language models plan and execute complex research tasks. By representing research plans as typed directed acyclic graphs (DAGs), DecomposeR enables better credit assignment for planning and execution, potentially leading to more structured and accurate long-form answers.
Researchers introduced SPIN, a new AI planning tool that improves how AI agents handle complex industrial tasks. It ensures plans are structurally sound and cost-effective, reducing failures and unnecessary expenses.
Researchers found that AI models, even when reasoning step-by-step, often plan poorly. They used a board game to show how these models struggle with long-term strategy. This could affect how we rely on AI for planning tasks.
Researchers introduce Object-Oriented World Modeling (OOWM), a framework that enhances embodied reasoning in robots by structuring world modeling through software principles. This approach addresses the limitations of traditional Chain-of-Thought prompting in complex planning tasks.
MMORF is a framework for designing multi-objective retrosynthesis planning systems. It leverages language model-based multi-agent systems to balance quality, safety, and cost objectives.