Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads

A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings. This architecture has been developed independently in operator learning, bipartite matching, contrastive vision-language models, retrieval, and other areas, yet no unified theory guides the basic design decisions: how many interaction modes to represent, how to normalize the encoders, and when the architecture should be avoided. We provide such a foundation b

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ethics.ai (12 August 2026), “Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads,” evidence record 18795, https://ethics.ai/record/18795 (originally published by arXiv).

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