Paraphrase Geometry for Embedding Enhancement

Sentence embeddings power semantic search, retrieval-augmented generation and clustering — but even strong encoders often confuse surface-form overlap with true meaning. This ongoing project treats a sentence's paraphrases as samples of a local, meaning-preserving manifold, and uses the geometry those paraphrases trace out to refine the embedding.

It all happens at inference time — no labels, no extra training, and compatible with any frozen encoder — aiming for the kind of gains contrastive fine-tuning gives, but without the training cost or the loss of generality, which matters in privacy-, latency- or IP-constrained deployments. (Method details are held back while the work is under submission.)