GILP trains a parameterized backbone for valid actions and state predictions, then uses a consistency gate with LLM drafts to reduce hallucinated-state rate from 0.176 to 0.035 on GPT-4o-mini while raising success from 0.668 to 0.838.
Large language models as commonsense knowledge for large-scale task planning
5 Pith papers cite this work. Polarity classification is still indexing.
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BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
The optimal reasoning strategy for LLMs depends on the model's diversity profile rather than the exploration method itself.
Digital twin representations from vision foundation models enable LLM-based planning for robust peg transfer and gauze retrieval on the dVRK surgical platform with claimed generalizability.
A survey that provides a taxonomy of methods for improving planning in LLM-based agents across task decomposition, plan selection, external modules, reflection, and memory.
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Efficient Causal Graph Discovery Using Large Language Models
BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.