ODP (Mixture-of-experts routing): superseded — cited as a baseline and beaten by newer methods. 0 paper(s) critique it, 2 beat it on benchmarks — #86 of 1370 most-superseded. Sub-problem: cluster led by MoEQuant. Newer alternatives in the same sub-problem include BitsMoE, GEMQ, KBVQ-MoE, MC# (Mixture-Compressor-sharp).

Mixture-of-experts routing

ODP

Superseded baseline#86 of 1,370 most-superseded

Superseded — cited as a baseline and beaten by newer methods

0 papers critique it · 2 beat it on benchmarks

Beaten on benchmarks

Head-to-head results where a newer method reports beating ODP. Values are copied from the source paper's tables — verify against the cited paper.

Ban beats ODP
83.33 vs 73.33
GSM8K/AIME24 · [Qwen3-235B-A22B]
Ban&Pick: Ehancing Performance and Efficiency of MoE-LLMs via Smarter Routing
PESF beats ODP
69.27 vs 68.92
Avg · [Phi3.5-moe, α=0.3]
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.

BitsMoEBitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
May 22, 2026
GEMQGEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
May 21, 2026
KBVQ-MoEKBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Jan 30, 2026
MC# (Mixture-Compressor-sharp)MC#: Mixture Compressor for Mixture-of-Experts Large Models
Oct 13, 2025