AIMER: Calibration-Free Task-Agnostic MoE Pruning

AIMER addresses a specific bottleneck in MoE deployment: the memory overhead of storing experts that are rarely utilized. Its primary innovation is the 'calibration-free' aspect—using weight-magnitude heuristics rather than data-dependent routing statistics. While technically clever, the defensibility is low (score 3) because the core logic is essentially a weight-norm-based selection heuristic applied to experts. Once the paper is published, this can be trivially re-implemented in production inference engines like vLLM, TensorRT-LLM, or DeepSpeed-Inference. The quantitative signals (0 stars, 5 forks) suggest this is currently in the research-sharing phase with minimal developer adoption outside of the immediate academic niche. The platform domination risk is high because companies like NVIDIA or Microsoft (DeepSpeed) are likely to integrate such pruning techniques directly into their optimization toolchains if they prove effective on flagship models like Mixtral or DeepSeek. It competes with other pruning methods like SparseGPT and expert-dropping strategies, but its lack of data dependency is its main competitive differentiator.