Mixture-Of-Experts

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Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
Jun 9, 2026Junli Zha, Jinbo Wang, Chao Zhou +1Llm-Driven Experts
Entropy-Aware Domain-Routed Mixture-of-Experts Speech-LLM Framework: A Case Study of Multi-Domain Child-Adult ASR
Jun 9, 2026Mohan Shi, Kaiyuan Zhang, Zilai Wang +3Speech-Llms Mixture-Of-Expert Language Models
Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling
Jun 9, 2026Guodong Lin, Ziqi Chen, Yuxiang Fu +2Llm-Based Automatic Speech Recognition Multilingual
PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning
Jun 9, 2026Xinyue Peng, Yi Qian, Jiaojiao Lin +2Ontology-Amplified Distillation Teacher
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
Jun 9, 2026Jingyi Xie, Yijun Lin, Yinjiang Xiong +2Mixture-Of-Expert Language Models Large Language Model Unlearning
Automating the Expert Eye: A System-Agnostic Deep Learning Framework for Rare Event Discovery in Imbalanced Force Spectroscopy
Jun 8, 2026Jorge Rodriguez-RamosMicroscopy Deep Learning Framework
MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry
Jun 8, 2026Junye Zou, Huiyi Yan, Xinning Xu +4Inertial Odometry Adversarial Motion Priors
FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting
Jun 8, 2026Qianyang Li, Xingjun Zhang, Shaoxun Wang +2Mixture-of-Experts Models Experts
STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning
Jun 7, 2026Sumin Park, Noseong ParkMixture-of-Experts Models Mars
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
Jun 7, 2026Varun Khurana, Vijval Ekbote, Vashu Chauhan +3Feature Engineering Human Annotations
NGram-MoSE: Efficient Remote Sensing Super-Resolution via N-Gram Context and Mixture-of-Experts
Jun 7, 2026Yun-Hsuan Huang, Trong-An Bui, Chih-Hung ChuangDiffusion-Based Super-Resolution Framework Remote Sensing
Variational Proximal Policy Optimization
Jun 6, 2026Ousmane Amadou DiaProximal Policy Optimization Variational Inference
LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
Jun 6, 2026Maxx Richard Rahman, Prakhar Kumar, Wolfgang MaassMultimodal Clinical Data Missing Modalities
Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
Jun 5, 2026Fatema Siddika, Md Anwar Hossen, Tanwi Mallick +1Efficient Continual Learning Continual Learning Methods
Sparsely gated tiny linear experts
Jun 5, 2026Simon SchugFeed-Forward Network Transformer Layers
Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling
Jun 5, 2026Rohan ShravanMixture-Of-Expert Language Models Large Models
Less is MoE: Trimming Experts in Domain-Specialist Language Models
Jun 4, 2026Haoze He, Xinkai Zou, Xuan Jiang +4Mixture-of-Experts Models Large Language Model Compression
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
Jun 3, 2026Yinxiang Yu, Maoxiang Chu, Qi Niu +7Mixture-of-Experts Models
D $^3$ -MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
Jun 3, 2026Renju Feng, Rukang Wang, Ning Xi +4Autonomous Driving Diffusion Planning
Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment
Jun 3, 2026Cong Wang, Hanxin Zhu, Jiayi Luo +6Large-Scale Video Generation Models Video Latents
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
Jun 3, 2026Kailun Huang, Zikang Xie, Yanzhe Xie +8Perceptive Locomotion Policies Long-Tail Humanoid Whole-Body Control
SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs
Jun 3, 2026Yuhao ZhangMixture-of-Experts Models Mixture-Of-Expert Inference
TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts
Jun 3, 2026Jiangyang He, Shaolin Zhu, Deyi XiongMixture-Of-Expert Language Models Mixture-of-Experts Models
Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis
Jun 3, 2026Yuantu Luo, Jun Tao, Linxiao Yu +1Network Traffic Heterogeneous Graph Transformers
LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling
Jun 3, 2026Wenkai Chen, Tianshu Li, Wenyong Huang +3Mixture-Of-Expert Language Models Mixture-of-Experts Models
DLLG: Dynamic Logit-Level Gating of LLM Experts
Jun 3, 2026Bingnan Li, Zhaoyang Zhang, Xiaoze Liu +6Mixture-Of-Expert Language Models Large Language
Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
Jun 2, 2026Yifan Wang, Jinyi Mu, Mayank Jobanputra +5Mixture-of-Experts Models Process Reward Modeling
Expert-Aware Refusal Steering
Jun 2, 2026Anna C. Marbut, Daniel R. Olson, Travis J. WheelerRefusals Mixture-Of-Expert Language Models
UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing
Jun 2, 2026Xinming Wei, Chao Jin, Tuo Dai +10Load Balancing Mixture-Of-Expert Inference
Expert-Aware Causal Tracing of Factual Recall in Sparse MoE Language Models
Jun 2, 2026Yuetian Lu, Ali Modarressi, Yihong Liu +1Mixture-Of-Expert Language Models Mixture-of-Experts Models
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
Jun 2, 2026Tao Xie, Zexi Tan, Haoyi Xiao +5Time-Series Classification Time Series
PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization
Jun 2, 2026Ying Tang, Dong Li, Youjia Zhang +3Vision Foundation Models Mixture-of-Experts Models
When Model Merging Breaks Routing: Training-Free Calibration for MoE
Jun 2, 2026Canbin Huang, Tianyuan Shi, Xiaojun Quan +3Mixture-of-Experts Models Model Merging
CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning
Jun 1, 2026Jun-Tao Tang, Zhen-Hao Xie, Yu-Cheng Shi +1Multimodal Instruction Tuning Instruction Tuning
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Jun 1, 2026Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise +4Spatial Transcriptomics Single-Cell Rna Sequencing
ResMerge: Residual-based Spectral Merging of Large Language Models
Jun 1, 2026Yandu Sun, Zhiyan Hou, Haokai Ma +6Model Merging Mixture-of-Experts Models
The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
Jun 1, 2026Michał Brzozowski, Neo Christopher ChungAuthorship Name
Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
Jun 1, 2026Mingxiao Wang, Xiaozhen Qu, Bolin Gao +2Traffic Sign Recognition Autonomous Driving
DOT-MoE: Differentiable Optimal Transport for MoEfication
Jun 1, 2026Udbhav Bamba, Arnav Chavan, Aryamaan Thakur +2Mixture-Of-Expert Inference Inference Cost
Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs
Jun 1, 2026Zichao Yue, Zhiru ZhangGraph Neural Networks Propagation
Reusing Fusion-Time Spectral Reliability for Adaptive Fusion and Expert Routing in RGB-Infrared Object Detection
May 31, 2026Yefeng WuObject Detection Cross-Modal Learning
Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
May 31, 2026Ya Shen, Gang Chen, Hui Ma +1Scheduling Deadlines
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
May 31, 2026Zemin Yang, Yaoyu He, Yiming Zhong +5Imitation Learning Vector Fields
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
May 31, 2026Jiarui Feng, Hanqing Zeng, Karish Grover +11Mixture-of-Experts Models
Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation
May 31, 2026Zijia Chen, Yuenan Hou, Xinhua Jiang +3Generalizable Robotic Manipulations Trajectory Prediction
Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference
May 31, 2026Zhiyao Xu, Aoxue Liu, Zhanjie Ding +3Mixture-Of-Expert Inference Mixture-of-Experts Models
MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts
May 30, 2026Vinay Edula, Priyanka BagadeBounding Box Object Detection
Confidence-Adaptive SwiGLU for Mixture-of-Experts
May 30, 2026Shaohua Li, Xiuchao Sui, Xiaobing Sun +4Mixture-of-Experts Models Sharpness-Aware Minimization
ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving
May 30, 2026Seokjin Go, Marko Scrbak, Ephrem Wu +2Mixture-Of-Expert Inference Inference Workloads
MESA: Improving MoE Safety Alignment via Decentralized Expertise
May 30, 2026Yitong Sun, Yao Huang, Teng Li +5Safety Alignment Mixture-Of-Expert Language Models
Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models
May 29, 2026Zijie Zhou, Dandan Zhu, Hangxiangpan Wang +3Large Vision Language Models Cross-Modal Learning
PithTrain: A Compact and Agent-Native MoE Training System
May 29, 2026Ruihang Lai, Hao Kang, Haozhan Tang +6Mixture-Of-Expert Language Models Mixture-of-Experts Models
Mellum2 Technical Report
May 29, 2026Marko Kojic, Ivan Bondyrev, Aral de Moor +6Mixture-Of-Expert Language Models Mixture-of-Experts Models
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
May 29, 2026Zheng Yuan, Chuang Zhou, Linhao Luo +4Graph-Based Retrieval Augmented Generation Experts
Eigenvectors of Experts are Training-free Non-collapsing Routers
May 29, 2026Giang Do, Hung Le, Truyen TranMixture-of-Experts Models Experts
dMoE: dLLMs with Learnable Block Experts
May 29, 2026Sicheng Feng, Zigeng Chen, Gongfan Fang +2Mixture-of-Experts Models Diffusion Language Models
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
May 28, 2026Ahmed Abouelazm, Felix Klingebiel, Philip Schörner +1Autonomous Driving Control System Operator Learning
DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning
May 28, 2026Hyuck Lee, Taemin Park, Heeyoung KimExtreme Class Imbalance Mixture-of-Experts Models
Feedback-to-Rubrics: Can We Learn Expert Criteria from Inline Comments?
May 28, 2026Kotaro Yoshida, So Kuroki, Yuki Imajuku +4Reddit Feedback Learning
Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
May 28, 2026Aditi Khandelwal, Marius Mosbach, Verna Dankers +2Multilingual Language Models Mixture-of-Experts Models

Jun 9, 2026 · cs.AIJ/K move · Enter open · S save

Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents

Junli Zha, Jinbo Wang, Chao Zhou, Xiang Song

Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis. We present \textbf{Trace2Policy}, whose core mechanism -- \textbf{EISR} (\textbf{E}rror-driven \textbf{I}terative \textbf{S}kill \textbf{R}efinement) -- maintains a human-readable rule document as its optimization target: each round executes the rules on a validation set, clusters errors by root cause into MISSING, WRONG, or CONFLICT types, applies targeted patches, and commits only those that pass a regression gate. \textbf{For this class of compliance-sensitive, skewed-base-rate decision tasks, we identify rule quality -- not model capability -- as the dominant performance lever}: across five LLMs, one-shot distillation plateaus near

\sim

70% on the deployed pool, while eight EISR rounds lift the same rules to 79.6% when compiled into deterministic Python -- zero LLM calls at inference. \textbf{Execution form compounds the gain: in production, the same EISR-refined content runs 9.8~pp higher as compiled Python than as an LLM prompt, a form-and-engineering bundle the 22-day deployment matured together.} Deployed for 22 days at a major logistics carrier (3,349 audit cases), the compiled pipeline outperforms the pure-LLM baseline it replaced (72.7%); on these calibrated, skewed-base-rate workloads, re-enabling LLM fallback monotonically degrades accuracy. An LLM-driven variant, \textbf{Auto-EISR}, reproduces this refinement at $5--$10 per cycle versus

\sim

70 expert-hours, and transfers to four public benchmarks spanning legal reasoning (LegalBench) and process-mining decisions (BPIC 2012) without re-engineering.

Llm-Driven Experts

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