GeMoE adaptively sets the number of experts per token via gating entropy, retaining 99.5% of static-routing performance while raising average sparsity by 36.5%.
AdaMoLE: Adaptive mixture of LoRA experts
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
MoLF routes updates between full fine-tuning and LoRA at the optimizer level to match or exceed the better of the two static methods on SQL, medical QA, and counterfactual tasks while an efficient variant outperforms prior adaptive LoRA by up to 20%.
Sub-token routing in LoRA-adapted transformers adds a finer compression axis for KV caches, with query-independent and query-aware designs that improve efficiency under reduced budgets when combined with token-level selection.
SETA decomposes parameters into task-specific and shared sparse experts with adaptive anchoring and routing regularization to improve retention and backward transfer in LLM continual learning.
LiMoDE uses dynamic MoE pre-training on motion cues followed by lifelong expert addition for continuous robot task adaptation.
Oxygen AIIC is an industrial platform using LLMs and VLMs for scalable item knowledge production and service at JD.com, reporting 94.2% precision and 82.8% recall along with business metric improvements.
citing papers explorer
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GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models
GeMoE adaptively sets the number of experts per token via gating entropy, retaining 99.5% of static-routing performance while raising average sparsity by 36.5%.
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Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation
MoLF routes updates between full fine-tuning and LoRA at the optimizer level to match or exceed the better of the two static methods on SQL, medical QA, and counterfactual tasks while an efficient variant outperforms prior adaptive LoRA by up to 20%.
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Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression
Sub-token routing in LoRA-adapted transformers adds a finer compression axis for KV caches, with query-independent and query-aware designs that improve efficiency under reduced budgets when combined with token-level selection.
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Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
SETA decomposes parameters into task-specific and shared sparse experts with adaptive anchoring and routing regularization to improve retention and backward transfer in LLM continual learning.
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LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective
LiMoDE uses dynamic MoE pre-training on motion cues followed by lifelong expert addition for continuous robot task adaptation.
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JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications
Oxygen AIIC is an industrial platform using LLMs and VLMs for scalable item knowledge production and service at JD.com, reporting 94.2% precision and 82.8% recall along with business metric improvements.