{"total":20,"rows":[{"id":"2601.02780","title":"MiMo-V2-Flash Technical Report","created":"2026-01-06","cited_by_count":191,"area":"moe"},{"id":"2505.09343","title":"Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures","created":"2025-05-14","cited_by_count":39,"area":"moe"},{"id":"2603.04791","title":"Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling","created":"2026-03-05","cited_by_count":28,"area":"moe"},{"id":"2603.07685","title":"Scalable Training of Mixture-of-Experts Models with Megatron Core","created":"2026-03-08","cited_by_count":19,"area":"moe"},{"id":"2602.04705","title":"ERNIE 5.0 Technical Report","created":"2026-02-04","cited_by_count":11,"area":"moe"},{"id":"2605.08738","title":"SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training","created":"2026-05-09","cited_by_count":7,"area":"moe"},{"id":"2607.14647","title":"D-cut: Adaptive Verification Depth Pruning for Batched Speculative Decoding","created":"2026-07-16","cited_by_count":5,"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":"2507.09955","title":"DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models","created":"2025-07-14","cited_by_count":3,"area":"moe"},{"id":"2607.12696","title":"Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts","created":"2026-07-14","cited_by_count":3,"area":"moe"},{"id":"2505.14173","title":"THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation","created":"2025-05-20","cited_by_count":2,"area":"moe"},{"id":"2510.11001","title":"DND: Boosting Large Language Models with Dynamic Nested Depth","created":"2025-10-13","cited_by_count":1,"area":"moe"},{"id":"2601.09200","title":"A.X K1 Technical Report","created":"2026-01-14","cited_by_count":0,"area":"moe"},{"id":"2605.00604","title":"Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts","created":"2026-05-01","cited_by_count":0,"area":"moe"},{"id":"2605.31268","title":"Mellum2 Technical Report","created":"2026-05-29","cited_by_count":0,"area":"moe"},{"id":"2606.07404","title":"Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling","created":"2026-06-05","cited_by_count":0,"area":"moe"},{"id":"2609.00791","title":"Instella-MoE Technical Report","created":"2026-09-01","cited_by_count":0,"area":"moe"},{"id":"2609.39137","title":"ID Balancing: Stable Training of Extremely Sparse MoE via PID-Based Load Control","created":"2026-09-30","cited_by_count":0,"area":"moe"},{"id":"2610.07219","title":"Cascadia: Resident 975B MoE Inference on Eleven AI PCs","created":"2026-10-05","cited_by_count":0,"area":"moe"},{"id":"2610.18010","title":"Fish Audio Drama 3 Technical Report","created":"2026-10-13","cited_by_count":0,"area":"moe"}],"next_cursor":null}