AIMER (Mixture-of-experts routing): current frontier — recent, not yet superseded in the knowledge base. 0 paper(s) critique it, 0 beat it on benchmarks — not ranked as a superseded baseline. Sub-problem: cluster led by MC-SMoE. Newer alternatives in the same sub-problem include Less is MoE, TIDE, CoX-MoE, HodgeCover, dynamic expert replication strategy.

Tracked

AIMER

AIMER: Calibration-Free Task-Agnostic MoE Pruning

Mixture-of-experts routing · first seen Mar 19, 2026

current frontier — recent, not yet superseded in the knowledge base

0 papers critique it · 0 beat it on benchmarks

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.

Less is MoE Less is MoE: Trimming Experts in Domain-Specialist Language Models
Jun 4, 2026
TIDE TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload
May 19, 2026
CoX-MoE CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
May 18, 2026
HodgeCover HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts
May 13, 2026
dynamic expert replication strategy Fast MoE Inference via Predictive Prefetching and Expert Replication
May 12, 2026
Temporally Extended Mixture-of-Experts Temporally Extended Mixture-of-Experts Models
Apr 22, 2026
MoEITS MoEITS: A Green AI approach for simplifying MoE-LLMs
Apr 12, 2026
Alloc-MoE Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
Apr 9, 2026
AIMER AIMER: Calibration-Free Task-Agnostic MoE Pruning
Mar 19, 2026
LightMoE LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
Mar 13, 2026
SliceMoE SliceMoE: Bit-Sliced Expert Caching under Miss-Rate Constraints for Efficient MoE Inference
Mar 12, 2026
EvoESAP EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
Mar 6, 2026