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RAGA is an LLM-driven autonomous agent that constructs knowledge graphs via a read-search-verify-construct cognitive loop and integrates hybrid symbolic-vector retrieval for retrieval-augmented generation, with experimental gains on scientific QA datasets.
This paper introduces a neuro-symbolic pipeline using 2.5-D decomposition to improve LLM-based spatial construction accuracy by offloading vertical coordinate calculation to a deterministic executor, achieving high accuracy on benchmarks and edge hardware.