Anchor Divergence for Semantic Geometry in Contrastive Learning

The paper introduces Anchor Divergences, a technique that leverages contrastive learning, exponential families, and information geometry to define context‑specific semantic geometries on fixed vector representations. By establishing a correspondence between probability distributions over anchors and Bregman geometries, the method allows the geometry itself to be modeled through the anchor distribution. Experiments on retrieval tasks demonstrate that Anchor Divergences effectively and efficiently capture context‑dependent semantic similarity.

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