arXiv Machine Learning
PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning
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Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing
The paper introduces Ban&Pick, a post‑training, plug‑and‑play routing strategy for Sparse Mixture‑of‑Experts large language models. It identifies and reinforces a small group of highly influential experts while dynamically pruning redundant ones, leading to accuracy gains across math, code, and reasoning benchmarks. Experiments on DeepSeek and Qwen3 show notable performance improvements and a 1.25× inference speedup without retraining or architectural changes.
arXiv Machine Learning
Sep 22
Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving
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arXiv AI
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Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices
arXiv:2609.35833v1 Announce Type: new Abstract: Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on...
arXiv Machine Learning
Jun 16
A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference
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arXiv Machine Learning
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Share First, Route What Remains: A Unified Framework for Token-Adaptive MoE Computation
arXiv:2608. 10392v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts.
arXiv AI
Aug 25
SAEM: Stage-Aware Expert Management for Memory-Efficient MoE Inference in Chain-of-Thought Reasoning
arXiv:2608.21614v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting improves LLM reasoning by decomposing complex problems into intermediate steps, but its sequential nature increases de...