LARS: Latent Alignment Residual Scoring
Abstract
LARS is a theoretical formulation for compatibility scoring under an ideal latent space representation. This latent space model assumes an inner-product compatibility of candidate embeddings and an ideal embedding, computed via an encoder and an MLP transformation. In addition, a bias is learned that contributes a scalar offset value to the compatibility scoring, which is normalized via softmax. This work provides a structural formulation and does not assert any empirical claims concerning convergence, correctness, or improvement.
Fields of Study & Subjects
Topic Modeling
Recommender Systems and Techniques
Advanced Neural Network Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 13 February 2026
- Language
- EN
- Document type
- Preprint