Mochi aligns pre-training with inference via meta-learning for efficient graph foundation models, matching or exceeding prior models on 25 datasets with 8-27x less training time.
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3 Pith papers cite this work, alongside 102 external citations. Polarity classification is still indexing.
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UltraTAG organizes LLM-GNN methods for text-attributed graphs; UltraTAG-S adds LLM text propagation, augmentation, PageRank node selection, and edge reconfiguration to improve robustness on sparse data, with reported gains of 2.12% and 17.47%.
RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.
citing papers explorer
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Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
Mochi aligns pre-training with inference via meta-learning for efficient graph foundation models, matching or exceeding prior models on 25 datasets with 8-27x less training time.
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Toward General and Robust LLM-enhanced Text-attributed Graph Learning
UltraTAG organizes LLM-GNN methods for text-attributed graphs; UltraTAG-S adds LLM text propagation, augmentation, PageRank node selection, and edge reconfiguration to improve robustness on sparse data, with reported gains of 2.12% and 17.47%.
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Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning
RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.