Mixture-of-experts routing
SafeX
SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification
Superseded baseline#42 of 1,370 most-superseded · first seen Jun 20, 2025
Superseded — cited as a baseline and beaten by newer methods
2 papers critique it · 1 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites SafeX as a baseline.
SafeX requires computationally expensive fine-tuning and leaves routing-based safety shortcuts unexamined.
“SafeX attempts to protect general reasoning by applying additive weight merging to localized safety pathways; however, this constrained intervention limits the diffusion of safety responsibility, resulting in incomplete defenses.”
Beaten on benchmarks
Head-to-head results where a newer method reports beating SafeX. Values are copied from the source paper's tables — verify against the cited paper.
MESA beats SafeX
90.90 vs 64.00
WildJB · [Model: DeepSeek-v2-Lite]
MESA: Improving MoE Safety Alignment via Decentralized Expertise
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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- PADDPADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student LearningJun 9, 2026
- May 30, 2026
- May 29, 2026
- May 1, 2026
- Apr 30, 2026
- Feb 9, 2026
- SocialNav-MoESocialNav-MoE: A Mixture-of-Experts Vision Language Model for Socially Compliant Navigation with Reinforcement Fine-TuningDec 15, 2025
- OrdMoEOrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMsNov 24, 2025
- Mix- and MoE-DPOMix- and MoE-DPO: A Variational Inference Approach to Direct Preference OptimizationOct 9, 2025