{"total":20,"rows":[{"id":"2601.02780","title":"MiMo-V2-Flash Technical Report","created":"2026-01-06","cited_by_count":191,"area":"moe"},{"id":"2505.21411","title":"Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity","created":"2025-05-27","cited_by_count":18,"area":"moe"},{"id":"2605.18106","title":"Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers","created":"2026-05-18","cited_by_count":11,"area":"moe"},{"id":"2602.03560","title":"HySparse: A Hybrid Sparse Attention Architecture with Oracle Token Selection and KV Cache Sharing","created":"2026-02-03","cited_by_count":10,"area":"moe"},{"id":"2508.07785","title":"Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts","created":"2025-08-11","cited_by_count":9,"area":"moe"},{"id":"2511.17127","title":"Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design","created":"2025-11-21","cited_by_count":5,"area":"moe"},{"id":"2601.22101","title":"ECO: Quantized Training without Full-Precision Master Weights","created":"2026-01-29","cited_by_count":5,"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":"2504.15527","title":"Compass-V2 Technical Report","created":"2025-04-22","cited_by_count":2,"area":"moe"},{"id":"2512.16248","title":"Sigma-MoE-Tiny Technical Report","created":"2025-12-18","cited_by_count":2,"area":"moe"},{"id":"2604.01152","title":"Brainstacks: Cross-Domain Cognitive Capabilities via Frozen MoE-LoRA Stacks for Continual LLM Learning","created":"2026-04-01","cited_by_count":1,"area":"moe"},{"id":"2607.09424","title":"A Sovereign, Open-Source Foundation Model for German and English","created":"2026-07-10","cited_by_count":1,"area":"moe"},{"id":"2608.06398","title":"EntropyMoE: Entropy-Aware Sparse Expert Routing for Tokenizer-Free LLMs","created":"2026-07-31","cited_by_count":1,"area":"moe"},{"id":"2506.18349","title":"SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation","created":"2025-06-23","cited_by_count":0,"area":"moe"},{"id":"2601.09200","title":"A.X K1 Technical Report","created":"2026-01-14","cited_by_count":0,"area":"moe"},{"id":"2605.07731","title":"Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs","created":"2026-05-08","cited_by_count":0,"area":"moe"},{"id":"2605.31268","title":"Mellum2 Technical Report","created":"2026-05-29","cited_by_count":0,"area":"moe"},{"id":"2609.06155","title":"ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers","created":"2026-09-05","cited_by_count":0,"area":"moe"},{"id":"2609.15126","title":"MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup","created":"2026-09-14","cited_by_count":0,"area":"moe"},{"id":"2610.12495","title":"Kairos: Toward a Byte-Level Foundation Model for Every Modality","created":"2026-09-26","cited_by_count":0,"area":"moe"}],"next_cursor":null}