Overview

Latent space optimization maps complex or discrete objects into compact representations where searching for high-performing solutions can be easier and cheaper. Common approaches learn these spaces with variational autoencoders or pretrained language models, then use Bayesian optimization, gradient-based search, evolutionary methods, or constrained and active optimization. Applications include molecular and protein design, robot and physical-system design, neural architecture search, and steering language-model outputs. A central challenge is ensuring that the latent geometry supports useful search while decoded solutions remain valid: representation gaps, imperfect surrogates, and trade-offs between objective quality, diversity, and evaluation cost can limit results.

Latest papers

CardsList

41 papers9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

  1. Using predefined vector systems to speed up neural network multimillion class classification

    Apr 1, 2026Nikita Gabdullin, Ilya AndrosovEfficient Neural Network InferenceMulticlass Classification

  2. Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates

    Mar 28, 2026Yeping Hu, Ruben Glatt, Shusen LiuComputational Fluid DynamicsPDE Surrogate Modeling

  3. Property-Guided Molecular Generation and Optimization via Latent Flows

    Mar 27, 2026Alexander Arjun Lobo, Urvi Awasthi, Leonid ZhukovMolecular GenerationMolecular Optimization

  4. Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization

    Mar 18, 2026Truong-Son HyProtein DesignLatent Space Optimization

  5. Robust Provably Secure Image Steganography via Latent Iterative Optimization

    Mar 10, 2026Yanan Li, Zixuan Wang, Qiyang Xiao +1Latent Space OptimizationImage Corruption Robustness

  6. Time-Aware Latent Space Bayesian Optimization

    Mar 1, 2026Tuan A. Vu, Julien Martinelli, Harri LähdesmäkiMolecule DesignBayesian Optimization

  7. GENSR: Symbolic Regression Based in Equation Generative Space

    Feb 24, 2026Qian Li, Yuxiao Hu, Juncheng Liu +1Symbolic RegressionLatent Space Optimization

  8. BarrierSteer: LLM Safety via Learning Barrier Steering

    Feb 23, 2026Thanh Q. Tran, Arun Verma, Kiwan Wong +3LLM SafetyAdversarial Robustness of Language Models

  9. LEVDA: Latent Ensemble Variational Data Assimilation via Differentiable Dynamics

    Feb 23, 2026Phillip Si, Peng ChenData AssimilationLatent Space Optimization

  10. Latent Regularization in Generative Test Input Generation

    Feb 17, 2026Giorgi Merabishvili, Oliver Weißl, Andrea StoccoAutomated Test GenerationLatent Space Optimization

  11. Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization

    Oct 17, 2025Luo Long, Coralia Cartis, Paz Fink ShustinVariational AutoencodersBayesian Optimization