A generative-adversarial-exam-based quality-of-memory metric drives memory-centric power allocation that prioritizes high-memory robots and improves multi-agent embodied question answering.
Integrated sensing and communications for low-altitude economy: A deep reinforcement learning approach,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
HDRL-MoE is a hierarchical DRL framework with MoE that decouples slow inference decisions from fast UAV trajectory control in a constrained POMDP to maximize inference accuracy under mission constraints.
GW-HGNN applies heterogeneous graph learning to balance image fidelity and transmission costs in drone-based 3D scene reconstruction, outperforming prior methods on rendering metrics while running 100x faster than MOSEK.
citing papers explorer
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Memory Centric Power Allocation for Multi-Agent Embodied Question Answering
A generative-adversarial-exam-based quality-of-memory metric drives memory-centric power allocation that prioritizes high-memory robots and improves multi-agent embodied question answering.
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UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning
HDRL-MoE is a hierarchical DRL framework with MoE that decouples slow inference decisions from fast UAV trajectory control in a constrained POMDP to maximize inference accuracy under mission constraints.
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LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
GW-HGNN applies heterogeneous graph learning to balance image fidelity and transmission costs in drone-based 3D scene reconstruction, outperforming prior methods on rendering metrics while running 100x faster than MOSEK.