# Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations

arXiv: https://arxiv.org/abs/2512.09200 · HTML: https://mlsanity.com/paper/2512.09200/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2025.

## Built on by (most cited first, 5 of 5)

- [GR2: Generative Reasoning Re-ranker](https://mlsanity.com/paper/2602.07774/) (2026)
- [SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling](https://mlsanity.com/paper/2604.12110/) (2026)
- [GR2 Technical Report](https://mlsanity.com/paper/2606.31984/) (2026)
- [ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation](https://mlsanity.com/paper/2607.27744/) (2026)
- [MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction](https://mlsanity.com/paper/2608.10562/) (2026)

## Abstract (arXiv)

The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale, primarily due to data fragmentation across domains and escalating infrastructure costs that hinder sustained quality improvements. To address this challenge, we propose Lattice, a recommendation framework centered around model space redesign that extends Multi-Domain, Multi-Objective (MDMO) learning beyond models and learning objectives. Lattice addresses these challenges through a comprehensive model space redesign that combines cross-domain knowledge sharing, data consolidation, model unification, distillation, and system optimizations to achieve significant improvements in both quality and cost-efficiency. Our deployment of Lattice at Meta has resulted in 10% revenue-driving top-line metrics gain, 11.5% user satisfaction improvement, 6% boost in conversion rate, with 20% capacity saving.