How Far Do Simple Transformations Translate Across Text Embedding Models?
We investigate whether simple transformations can translate representations across heterogeneous text embedding models. Understanding how independently trained models organize semantic information is an enabler for AI-to-AI latent communication without decoding into human-readable text. Focusing on lightweight translators such as linear mappings, we test the literature hypothesis of latent universality in a realistic text setting beyond simplified benchmarks. Across nine embedding models differing in architecture, pooling strategy, and training objective, we evaluate compatibility using CKA, downstream transfer, fidelity, and retrieval. Simple translators recover meaningful shared structu...
| Authors | Sid Ali Hamideche, Louis-Adrien Dufrène, Quentin Lampin et al. |
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| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 2 September 2026 |
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