{"total":37,"rows":[{"id":"2404.07413","title":"JetMoE: Reaching Llama2 Performance with 0.1M Dollars","created":"2024-04-11","cited_by_count":57,"area":"moe"},{"id":"2505.22323","title":"Advancing Expert Specialization for Better MoE","created":"2025-05-28","cited_by_count":39,"area":"moe"},{"id":"2507.13575","title":"Apple Intelligence Foundation Language Models: Tech Report 2025","created":"2025-07-17","cited_by_count":28,"area":"moe"},{"id":"2511.00279","title":"LongCat-Flash-Omni Technical Report","created":"2025-10-31","cited_by_count":26,"area":"moe"},{"id":"2507.23279","title":"Unveiling Super Experts in Mixture-of-Experts Large Language Models","created":"2025-07-31","cited_by_count":17,"area":"moe"},{"id":"2601.21204","title":"Scaling Embeddings Outperforms Scaling Experts in Language Models","created":"2026-01-29","cited_by_count":15,"area":"moe"},{"id":"2507.00390","title":"MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE","created":"2025-07-01","cited_by_count":13,"area":"moe"},{"id":"2505.04519","title":"Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs","created":"2025-05-07","cited_by_count":12,"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":"2506.05767","title":"dots.llm1 Technical Report","created":"2025-06-06","cited_by_count":5,"area":"moe"},{"id":"2602.14159","title":"Synergistic Intra- and Cross-Layer Regularization Losses for MoE Expert Specialization","created":"2026-02-15","cited_by_count":5,"area":"moe"},{"id":"2605.15403","title":"$\\phi$-Balancing for Mixture-of-Experts Training","created":"2026-05-14","cited_by_count":5,"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":"2602.00879","title":"Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs","created":"2026-01-31","cited_by_count":4,"area":"moe"},{"id":"2605.18643","title":"Post-Trained MoE Can Skip Half Experts via Self-Distillation","created":"2026-05-18","cited_by_count":4,"area":"moe"},{"id":"2510.14853","title":"Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models","created":"2025-10-16","cited_by_count":3,"area":"moe"},{"id":"2606.15453","title":"A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference","created":"2026-06-13","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":"2505.21191","title":"Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities","created":"2025-05-27","cited_by_count":2,"area":"moe"},{"id":"2509.06346","title":"Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing","created":"2025-09-08","cited_by_count":2,"area":"moe"},{"id":"2605.30876","title":"dMoE: dLLMs with Learnable Block Experts","created":"2026-05-29","cited_by_count":2,"area":"moe"},{"id":"2607.21978","title":"MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation","created":"2026-07-24","cited_by_count":1,"area":"moe"},{"id":"2608.02989","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","created":"2026-08-04","cited_by_count":1,"area":"moe"},{"id":"2505.23184","title":"Two Is Better Than One: Rotations Scale LoRAs","created":"2025-05-29","cited_by_count":0,"area":"moe"},{"id":"2510.02194","title":"UpSafe$^\\circ$C: Upcycling for Controllable Safety in Large Language Models","created":"2025-10-02","cited_by_count":0,"area":"moe"},{"id":"2601.14327","title":"Yuan3.0 Ultra: A Trillion-Parameter Enterprise-Oriented MoE LLM","created":"2026-01-20","cited_by_count":0,"area":"moe"},{"id":"2601.16863","title":"Mixture-of-Models: Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation","created":"2026-01-23","cited_by_count":0,"area":"moe"},{"id":"2605.11277","title":"Sieve: Dynamic Expert-Aware PIM Acceleration for Evolving Mixture-of-Experts Models","created":"2026-05-11","cited_by_count":0,"area":"moe"},{"id":"2606.18304","title":"Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression","created":"2026-06-16","cited_by_count":0,"area":"moe"},{"id":"2607.08782","title":"Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement","created":"2026-06-13","cited_by_count":0,"area":"moe"},{"id":"2608.28511","title":"Training Communication-Efficient Mixture-of-Experts Language Models with Layer Re-Configuration","created":"2026-08-28","cited_by_count":0,"area":"moe"},{"id":"2609.20974","title":"Attention-Aware Routing: Coupling Routing and Attention in MoEs","created":"2026-09-17","cited_by_count":0,"area":"moe"},{"id":"2609.25809","title":"You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs","created":"2026-09-22","cited_by_count":0,"area":"moe"},{"id":"2609.27284","title":"Hunyuan-A13B Technical Report","created":"2026-09-23","cited_by_count":0,"area":"moe"},{"id":"2609.34117","title":"SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving","created":"2026-09-28","cited_by_count":0,"area":"moe"},{"id":"2610.05744","title":"CIPHER-MoE: Balancing Efficiency and Routing Fidelity in Trillion-Scale MoE Training","created":"2026-10-05","cited_by_count":0,"area":"moe"},{"id":"2610.12582","title":"One Model to Loop Them All: Elastic Latent Recurrence for Language Models","created":"2026-10-08","cited_by_count":0,"area":"moe"}],"next_cursor":null}