Mixtures

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Period ending 2026-09-21

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Period ending 2026-09-14

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98 papers

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ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
May 22, 2026Yibin Wang, Muhan Li, Zihan Guo +1Co-Evolution Robot Systems
Mixture of Complementary Agents for Robust LLM Ensemble
May 21, 2026Yichi Zhang, Kevin Lu, Yuang Zhang +3Ensemble Summarization
Closed Loop Dynamic Driving Data Mixture for Real-Synthetic Co-Training
May 20, 2026Hongzhi Ruan, Pei Liu, Weiliang Ma +5Naturalistic Driving Data Iterative Co-Training
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
May 19, 2026Jianan Yang, Yiran Wang, Shuai Li +3Conventional Physics-Informed Neural Networks Partial Differential Equation Benchmarks
DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
May 19, 2026Kieran Wood, Stefan Zohren, Stephen J. RobertsDistribution Shifts Gaussian Process
MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation
May 18, 2026Ronyu Zhang, Aosong Cheng, Gaole Dai +8Continual Test-Time Adaptation Model Pretraining
Mixture of Experts for Low-Resource LLMs
May 17, 2026Ori Bar Joseph, Smadar Arvatz, Noam Kayzer +2Mixture-Of-Expert Language Models Mixture-of-Experts Models
Scaling Laws for Mixture Pretraining Under Data Constraints
May 12, 2026Anastasiia Sedova, Skyler Seto, Natalie Schluter +1Model Pretraining Scaling Laws
From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
May 12, 2026Yajie Li, Bozhou Zhang, Chun Gu +5Latent Actions Action Space
Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts
May 12, 2026Eunho Lee, Rei Kawakami, Youngbae HwangImage Restoration Multimodal Pretraining
Mixture of Layers with Hybrid Attention
May 10, 2026Ivan Ternovtsii, Yurii BilakMixture-of-Experts Models Mixtures
Remix the Timbre: Diffusion-Based Style Transfer Across Polyphonic Stems
May 10, 2026Leduo Chen, Junchuan Zhao, Shengchen LiMultimodal Diffusion Transformers Mixtures
Learning predictive models for combinations of heterogeneous proteomic data sources
May 9, 2026Michal Valko, Richard Pelikan, Miloš HauskrechtMulti-Omic Framework Spatial Transcriptomics
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
May 9, 2026Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3Latent States Mixture-of-Experts Models
Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution
May 9, 2026Jongseok Park, Sunga Kim, Zhenyu Gu +2Sparse Expert Activation Mixture-of-Experts Models
EMO: Pretraining Mixture of Experts for Emergent Modularity
May 7, 2026Ryan Wang, Akshita Bhagia, Sewon MinMixture-Of-Expert Language Models Mixture-of-Experts Models
Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts
May 7, 2026Wenhan Zheng, Yuyi Mao, Ivan Wang-Hei HoHuman Activity Recognition Cross-Sensor Generalization
MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria
May 6, 2026Xuwei Yang, David B. Emerson, Fatemeh Tavakoli +1Federated Reinforcement Learning Multi-Agent Reinforcement Learning
On Computing Total Variation Distance Between Mixtures of Product Distributions
May 5, 2026Weiming Feng, Yucheng Fu, Minji Yang +1Mixture Models Distance
MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
May 5, 2026Shishir Muralidhara, Didier Stricker, René SchusterLow-Rank Adaptation Multimodal Benchmarks
Fine-Grained Graph Generation through Latent Mixture Scheduling
May 4, 2026Nidhi Vakil, Hadi AmiriData Generation Variational Autoencoder
Rethinking LLM Ensembling from the Perspective of Mixture Models
May 1, 2026Jiale Fu, Yuchu Jiang, Peijun Wu +3Large Language Ensemble
Prediction-powered Inference by Mixture of Experts
Apr 30, 2026Yanwu Gu, Linglong Kong, Dong XiaMixture-Of-Expert Inference Probabilistic Inference
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Apr 28, 2026Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9Interatomic Potentials Molecular Dynamics
Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
Apr 27, 2026Takato YasunoMixture Models Variational Inference
Mixture of Heterogeneous Grouped Experts for Language Modeling
Apr 25, 2026Zhicheng Ma, Xiang Liu, Zhaoxiang Liu +5Mixture-of-Experts Models Inference Cost
Domain-Specialized Object Detection via Model-Level Mixtures of Experts
Apr 20, 2026Svetlana Pavlitska, Malte Stüven, Beyza Keskin +1Object Detection Mixtures
How to Approximate Inference with Subtractive Mixture Models
Apr 17, 2026Lena Zellinger, Nicola Branchini, Lennert De Smet +3Variational Inference Mixture Models
Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector
Apr 17, 2026Cristiano Fanelli, James Giroux, Cole Granger +1Wireless Foundation Models Mixture-of-Experts Models
Mixture-Greedy for Online Generative Model Selection: Is UCB Necessary in Diversity-Aware Multi-Armed Bandits?
Mar 23, 2026Bahar Dibaei Nia, Farzan FarniaModel Selection Generative Models
Mixture of Distributions Matters: Dynamic Sparse Attention for Efficient Video Diffusion Transformers
Jan 14, 2026Yuxi Liu, Yipeng Hu, Zekun Zhang +2Pre-Trained Video Diffusion Models Dynamic Sparse Attention
AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages
Jan 10, 2026Hao Yu, Tianyi Xu, Michael A. Hedderich +3Large Language Model Pretraining African Languages
Mixtures of SubExperts for Large Language Continual Learning
Nov 9, 2025Haeyong Kang, Hee Suk Yoon, Dahua Feng +1Mixture-Of-Expert Language Models Efficient Continual Learning
Mixtures Closest to a Given Measure: A Semidefinite Programming Approach
Sep 26, 2025Srećko Đurašinović, Jean-Bernard Lasserre, Victor MagronGaussian Mixture Models Semidefinite Programming
Sanity Checking Causal Representation Learning on a Simple Real-World System
Feb 27, 2025Juan L. Gamella, Simon Bing, Jakob RungeCausal Representation Learning Causal Reasoning
Causal Discovery in Mixtures of Populations
Nov 13, 2023Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1Causal Foundation Models Structural Equation Models
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Jul 14, 2019Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van +1Mixture-of-Experts Models Lasso
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
Date pendingLige Zhang, Ali Maatouk, Jialin Chen +3Time Series Mixture-of-Experts Models

May 22, 2026 · cs.ROJ/K move · Enter open · S save

ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots

Yibin Wang, Muhan Li, Zihan Guo, Sam Kriegman

Northwestern University, Evanston, IL, USA.

In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixture of control experts (neural modules), which are gated by the latent coordinates of each decoded design (phenotype). This provides a scalable alternative to co-design algorithms that either train an individual policy for every robot, which is inefficient, or a monolithic universal controller for all robots, which results in overly conservative structures and behaviors. Our approach lies somewhere between these two extremes, preserving ancestral knowledge in a unified yet modular framework in which different body plans activate and deactivate different combinations of learned sensorimotor circuits for goal-directed behavior. This allows one part of the controller to be overhauled to better suit new species of designs as they emerge without disrupting the hard-earned knowledge contained within other expert modules. It also allows pretrained expert policies to be directly plugged into the mixture, which can steer evolution into otherwise unexplored areas of latent space containing desired morphological traits. We refer to this process as "evo by demo" and explore how it may be used to guide freeform evolution toward canonical structures defined by the pretrained model. Videos and code can be found at: https://eco-moe.github.io.

Co-Evolution Robot Systems Alphaevolve Experts Goal-Directed Behavior Mixtures Latent Variable

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