UWE is a task-agnostic bi-encoder that uses many-to-many InfoNCE and token-level soft late interaction to achieve zero-shot ranking across unseen work-related target spaces while using far fewer parameters than Qwen3-8B and improving MAP by 4.4 points.
arXiv preprint arXiv:2109.01116 (2021)
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Structural diversity—how many disconnected prior collaboration communities a team bridges—predicts disruptive scientific impact better than team freshness or density and can offset the large-team incrementalism penalty.
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Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker
UWE is a task-agnostic bi-encoder that uses many-to-many InfoNCE and token-level soft late interaction to achieve zero-shot ranking across unseen work-related target spaces while using far fewer parameters than Qwen3-8B and improving MAP by 4.4 points.
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SP-GCRL: Influence Maximization on Incomplete Social Graphs
Structural diversity—how many disconnected prior collaboration communities a team bridges—predicts disruptive scientific impact better than team freshness or density and can offset the large-team incrementalism penalty.