{"total":18,"rows":[{"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":"2511.04805","title":"PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference","created":"2025-11-06","cited_by_count":9,"area":"moe"},{"id":"2509.23638","title":"LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers","created":"2025-09-28","cited_by_count":8,"area":"moe"},{"id":"2511.15015","title":"Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference","created":"2025-11-19","cited_by_count":7,"area":"moe"},{"id":"2605.23078","title":"GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs","created":"2026-05-21","cited_by_count":7,"area":"moe"},{"id":"2606.04980","title":"AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization","created":"2026-06-03","cited_by_count":7,"area":"moe"},{"id":"2601.21198","title":"ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling","created":"2026-01-29","cited_by_count":4,"area":"moe"},{"id":"2512.17073","title":"Bandwidth-Efficient Adaptive Mixture-of-Experts via Low-Rank Compensation","created":"2025-12-18","cited_by_count":3,"area":"moe"},{"id":"2608.30564","title":"Q-Strata: Hierarchical Bit Allocation for Mixed-Precision Quantization of Mixture-of-Experts LLMs","created":"2026-08-31","cited_by_count":3,"area":"moe"},{"id":"2607.16184","title":"PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization","created":"2026-07-17","cited_by_count":2,"area":"moe"},{"id":"2604.16930","title":"CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering","created":"2026-04-18","cited_by_count":1,"area":"moe"},{"id":"2605.09281","title":"TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling","created":"2026-05-10","cited_by_count":1,"area":"moe"},{"id":"2605.05899","title":"VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading","created":"2026-05-07","cited_by_count":0,"area":"moe"},{"id":"2606.00079","title":"BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization","created":"2026-05-22","cited_by_count":0,"area":"moe"},{"id":"2608.15383","title":"Every Expert Counts: ExactMoE for Memory-Efficient W4A16 Inference","created":"2026-08-15","cited_by_count":0,"area":"moe"},{"id":"2609.18131","title":"Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing","created":"2026-09-16","cited_by_count":0,"area":"moe"},{"id":"2610.13328","title":"DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving","created":"2026-10-09","cited_by_count":0,"area":"moe"},{"id":"2610.20353","title":"Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization","created":"2026-10-14","cited_by_count":0,"area":"moe"}],"next_cursor":null}