{"total":50,"rows":[{"id":"2411.11217","title":"MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs","created":"2024-11-18","cited_by_count":73,"area":"moe"},{"id":"2310.16795","title":"QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models","created":"2023-10-25","cited_by_count":42,"area":"moe"},{"id":"2410.22134","title":"ProMoE: Fast MoE-based LLM Serving using Proactive Caching","created":"2024-10-29","cited_by_count":40,"area":"moe"},{"id":"2406.05955","title":"Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters","created":"2024-06-10","cited_by_count":38,"area":"moe"},{"id":"2505.19645","title":"MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE","created":"2025-05-26","cited_by_count":19,"area":"moe"},{"id":"2506.20675","title":"Utility-Driven Speculative Decoding for Mixture-of-Experts","created":"2025-06-17","cited_by_count":13,"area":"moe"},{"id":"2603.06003","title":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","created":"2026-03-06","cited_by_count":9,"area":"moe"},{"id":"2508.21706","title":"Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding","created":"2025-08-29","cited_by_count":8,"area":"moe"},{"id":"2410.10846","title":"Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models","created":"2024-10-01","cited_by_count":7,"area":"moe"},{"id":"2501.09410","title":"MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models","created":"2025-01-16","cited_by_count":6,"area":"moe"},{"id":"2507.08771","title":"BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity","created":"2025-07-11","cited_by_count":6,"area":"moe"},{"id":"2603.09983","title":"MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios","created":"2026-02-12","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":"2510.08055","title":"From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill","created":"2025-10-09","cited_by_count":4,"area":"moe"},{"id":"2602.09721","title":"Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems","created":"2026-02-10","cited_by_count":4,"area":"moe"},{"id":"2604.10152","title":"SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding","created":"2026-04-11","cited_by_count":4,"area":"moe"},{"id":"2605.00342","title":"Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding","created":"2026-05-01","cited_by_count":4,"area":"moe"},{"id":"2602.06154","title":"MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models","created":"2026-02-05","cited_by_count":3,"area":"moe"},{"id":"2605.08639","title":"ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning","created":"2026-05-09","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":"2508.16700","title":"GPT-OSS-20B: A Comprehensive Deployment-Centric Analysis of OpenAI's Open-Weight Mixture of Experts Model","created":"2025-08-22","cited_by_count":2,"area":"moe"},{"id":"2512.21911","title":"Accelerate Speculative Decoding with Sparse Computation in Verification","created":"2025-12-26","cited_by_count":2,"area":"moe"},{"id":"2605.02960","title":"MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving","created":"2026-05-03","cited_by_count":2,"area":"moe"},{"id":"2608.15018","title":"S2-MoE: Enabling Efficient Self-Speculative Decoding for Mixture-of-Experts on Edge Devices","created":"2026-08-15","cited_by_count":2,"area":"moe"},{"id":"2502.02040","title":"M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference","created":"2025-02-04","cited_by_count":1,"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":"2607.04371","title":"Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs","created":"2026-07-05","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":"2608.18261","title":"Cacheable by Design? Training Mixture-of-Experts Routers for Locality Against the Edge Memory-Bandwidth Wall: A Pre-Registered Negative Result with a Systems Measurement Study","created":"2026-08-18","cited_by_count":1,"area":"moe"},{"id":"2609.22156","title":"The Limits of Speculation: Bounding Speculative Decoding in Mixture-of-Experts","created":"2026-08-26","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":"2603.10031","title":"Architecture-Aware LLM Inference Optimization on AMD Instinct GPUs: A Comprehensive Benchmark and Deployment Study","created":"2026-02-27","cited_by_count":0,"area":"moe"},{"id":"2604.14626","title":"ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving","created":"2026-04-16","cited_by_count":0,"area":"moe"},{"id":"2605.00254","title":"Rethinking Network Topologies for Cost-Effective Mixture-of-Experts LLM Serving","created":"2026-04-30","cited_by_count":0,"area":"moe"},{"id":"2605.31268","title":"Mellum2 Technical Report","created":"2026-05-29","cited_by_count":0,"area":"moe"},{"id":"2607.08215","title":"On the Limitations of Non-GPU AI Accelerators for Large-Model Inference: A Field Study of MoE and Multimodal Serving on Huawei Ascend","created":"2026-07-09","cited_by_count":0,"area":"moe"},{"id":"2607.09686","title":"MawForge: Memory-Bounded Expert Materialization for Local Mixture-of-Experts Inference","created":"2026-06-17","cited_by_count":0,"area":"moe"},{"id":"2608.05303","title":"EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding","created":"2026-08-05","cited_by_count":0,"area":"moe"},{"id":"2608.07964","title":"EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference","created":"2026-08-08","cited_by_count":0,"area":"moe"},{"id":"2608.21693","title":"Benchmarking Composable Compression Techniques in Mixture-of-Experts LLMs","created":"2026-08-22","cited_by_count":0,"area":"moe"},{"id":"2609.09748","title":"Epoch: Compiling Diffusion Blocks for Sparse MoE Serving","created":"2026-09-09","cited_by_count":0,"area":"moe"},{"id":"2609.12550","title":"Quality-Constrained Routing over a Fixed Pool of Quantized Mixture-of-Experts Instances","created":"2026-09-11","cited_by_count":0,"area":"moe"},{"id":"2609.14237","title":"OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving","created":"2026-09-13","cited_by_count":0,"area":"moe"},{"id":"2609.14643","title":"BigMoMo: Efficient Inference of Large-Scale MoE with Speculative Decoding on Mobile Devices","created":"2026-09-13","cited_by_count":0,"area":"moe"},{"id":"2609.22471","title":"Efficient Mixture-of-Experts with Speculative Decoding via Expert Coactivation","created":"2026-09-18","cited_by_count":0,"area":"moe"},{"id":"2610.05774","title":"AdaSpark: Adaptive DSpark with Online Learning for Tree Verification and N-gram Fill","created":"2026-10-05","cited_by_count":0,"area":"moe"},{"id":"2610.07606","title":"VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models","created":"2026-10-06","cited_by_count":0,"area":"moe"},{"id":"2610.12599","title":"STAPLE: Static Sparse Upcycling of Pretrained Checkpoints via Embedding Modules","created":"2026-10-08","cited_by_count":0,"area":"moe"},{"id":"2610.14074","title":"One Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns","created":"2026-10-08","cited_by_count":0,"area":"moe"},{"id":"2610.15417","title":"SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference","created":"2026-10-11","cited_by_count":0,"area":"moe"}],"next_cursor":null}