GiG uses a Graph-in-Graph architecture with GNN-encoded states, experience memory retrieval, and bounded symbolic lookahead to improve LLM planning on embodied benchmarks with gains up to 37%.
Intelligent document parsing: Towards end-to-end docu- ment parsing via decoupled content parsing and layout grounding
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Embodied Task Planning via Graph-Informed Action Generation with Large Language Models
GiG uses a Graph-in-Graph architecture with GNN-encoded states, experience memory retrieval, and bounded symbolic lookahead to improve LLM planning on embodied benchmarks with gains up to 37%.