HybridRAG merges keyword, vector, and graph retrieval to let an LLM formulate carbon-emission optimization problems for multi-UAV MEC networks, and R2DSAC solves them with a diffusion-regularized SAC plus neuron pruning, reporting 64 percent lower carbon emissions than SAC in simulation.
Service experience oriented cooperative computing in cache-enabled UA Vs assisted MEC networks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NI 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
HybridRAG-based LLM Agents for Low-Carbon Optimization in Low-Altitude Economy Networks
HybridRAG merges keyword, vector, and graph retrieval to let an LLM formulate carbon-emission optimization problems for multi-UAV MEC networks, and R2DSAC solves them with a diffusion-regularized SAC plus neuron pruning, reporting 64 percent lower carbon emissions than SAC in simulation.