Neural-NF learns a mapping from intrinsic Laplacian features to local PDE coefficients whose solution yields a collision-free, monotonically descending navigation function with global goal minimum by construction, achieving up to 5x better zero-shot transfer than direct value-function predictors.
Generalizable motion planning via operator learning
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The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.
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Neural Navigation Functions for Zero-Shot Generalizable Motion Planning
Neural-NF learns a mapping from intrinsic Laplacian features to local PDE coefficients whose solution yields a collision-free, monotonically descending navigation function with global goal minimum by construction, achieving up to 5x better zero-shot transfer than direct value-function predictors.
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Agentic Reasoning for Large Language Models
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.