Shen et al. present Towards Backward-Compatible Representation Learning
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Presented Towards Backward-Compatible Representation Learning — proposed BCT, which trains a new embedding model to be compatible with the old index, eliminating re-indexing
Why it matters
The first study to tackle the cost of embedding migration head-on. Framed as a problem the structural debt of having to re-embed the entire corpus every time the model is replaced
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Paperarxiv.orghttps://arxiv.org/abs/2003.11942Added in Week 35, 2026 · See that week
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