The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
Continual learning with scaled gradient projection
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MANGO combines gradient-gating and meta-learned regularization to balance stability and plasticity in single-pass online continual learning, reporting state-of-the-art accuracy on CLEAR-10, CIFAR-100, and Tiny-ImageNet.
citing papers explorer
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LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
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MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
MANGO combines gradient-gating and meta-learned regularization to balance stability and plasticity in single-pass online continual learning, reporting state-of-the-art accuracy on CLEAR-10, CIFAR-100, and Tiny-ImageNet.