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.
Multi-objective optimization for multi-UA V-assisted mobile edge computing,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.NI 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 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.