GraphScout trains LLMs to autonomously synthesize structured training data from knowledge graphs via flexible exploration tools, enabling a 4B model to outperform larger LLMs by 16.7% on average with fewer inference tokens and strong cross-domain transfer.
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8 Pith papers cite this work. Polarity classification is still indexing.
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2026 8representative citing papers
S²GR adds stepwise thinking tokens with contrastive supervision on codebook clusters to balance computational focus and ground reasoning paths in generative recommendation.
GRE-MC retrieves relevant subgraphs and uses a graph transformer plus sparse codebook to complete missing modalities, outperforming prior methods on recommendation benchmarks.
TIDE disentangles habitual repurchase from exploratory interest in next-basket recommendation using Hawkes-enhanced Fourier time encoding, dual experts, and item-aware gating, outperforming prior methods on four datasets.
DC4SR improves sequential recommendation denoising by iteratively calibrating LLM semantic priors and model learning posteriors using their disagreement as a signal for better alignment with true user interests.
DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.
Veda and EffVeda partition vector data by role combinations, apply lattice-based copy/merge under storage budget, index large nodes with HNSW and small nodes with linear scan, then use query plans and coordinated search with pruning to answer authorized top-k queries.