{"total":14,"rows":[{"id":"2510.24821","title":"Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation","created":"2025-10-28","cited_by_count":22,"area":"moe"},{"id":"2510.00844","title":"Learning Compact Representations of LLM Abilities via Item Response Theory","created":"2025-10-01","cited_by_count":10,"area":"moe"},{"id":"2605.24956","title":"NITP: Next Implicit Token Prediction for LLM Pre-training","created":"2026-05-24","cited_by_count":4,"area":"moe"},{"id":"2604.25578","title":"Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling","created":"2026-04-28","cited_by_count":3,"area":"moe"},{"id":"2608.03275","title":"MoEGen: Mixture-of-Experts for Instance-Adaptive LoRA Generation","created":"2026-08-04","cited_by_count":3,"area":"moe"},{"id":"2608.00916","title":"Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget","created":"2026-08-02","cited_by_count":1,"area":"moe"},{"id":"2601.02144","title":"Routing by Analogy: kNN-Augmented Expert Assignment for Mixture-of-Experts","created":"2026-01-05","cited_by_count":0,"area":"moe"},{"id":"2603.00031","title":"GRIP: Geometric Refinement and Adaptive Information Potential for Data Efficiency","created":"2026-02-04","cited_by_count":0,"area":"moe"},{"id":"2607.12255","title":"Understanding Structured Health Data through Interaction-Aware Mixture-of-Experts","created":"2026-07-14","cited_by_count":0,"area":"moe"},{"id":"2608.23806","title":"Giga-Embeddings: Mixture-of-Experts Encoders for High-Throughput Text Embeddings","created":"2026-08-24","cited_by_count":0,"area":"moe"},{"id":"2608.31118","title":"When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning","created":"2026-08-31","cited_by_count":0,"area":"moe"},{"id":"2609.08663","title":"MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models","created":"2026-09-08","cited_by_count":0,"area":"moe"},{"id":"2609.13486","title":"Mixture-of-Experts Language Models Can Be Strong and Efficient Retrievers","created":"2026-09-11","cited_by_count":0,"area":"moe"},{"id":"2610.01921","title":"Cross-Lingual Alignment for Decoder-Only Models using MoE Routers","created":"2026-10-01","cited_by_count":0,"area":"moe"}],"next_cursor":null}