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Extrapolative temporal knowledge graph reasoning (TKGR) predicts future facts from historical snapshots. Most existing methods train once on an early prefix of the timeline and then use a frozen model for all future timestamps. We argue that this fixed-prefix protocol is misaligned with extrapolation. It learns from a...
UnpairGR is proposed, which learns a unified semantic-ID space from paired, image-only, and text-only observations, which confines modality-specific processing to lightweight input projections while sharing the subsequent Transformer and residual codebooks across all observation conditions.
Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline. First, semantic ID (SID) construction based on static product information limits the ability of SIDs to encode product-intent associations. Second, although...
This work introduces VisDocAgentBench, a closed-corpus benchmark comparing static and agentic retrieval under a shared ranked-output contract, and motivates retrieval agents that combine modality-preserving discovery with evidence-directed verification.
This work proposes a multi-granularity knowledge refinement approach to prune historical TKGs, which selectively removes irrelevant edges and unnecessary nodes at both the edge and node levels.
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