COMLLM uses multi-turn LLM reasoning via GRPO and LACS to achieve near-optimal latency, better fairness, and zero-shot generalization to larger unseen network topologies in mobile edge computing task offloading.
Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
QAROO combines quantum neural networks, attention mechanisms, and recurrent networks in a reinforcement learning setup to improve online task offloading performance over baselines in dynamic MEC environments.
citing papers explorer
-
Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing
COMLLM uses multi-turn LLM reasoning via GRPO and LACS to achieve near-optimal latency, better fairness, and zero-shot generalization to larger unseen network topologies in mobile edge computing task offloading.
-
QAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks
QAROO combines quantum neural networks, attention mechanisms, and recurrent networks in a reinforcement learning setup to improve online task offloading performance over baselines in dynamic MEC environments.