{
  "id": 18795,
  "url": "https://arxiv.org/abs/2608.11661v1",
  "title": "Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads",
  "summary": "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",
  "authors": "Zijian Zhao, Sen Li",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T05:09:44.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18795",
  "original_url": "https://arxiv.org/abs/2608.11661v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}