Is SteerMoE superseded?

SteerMoE (Mixture-of-experts routing): superseded — cited as a baseline and beaten by newer methods. 3 paper(s) critique it, 3 beat it on benchmarks — #17 of 1370 most-superseded. Sub-problem: cluster led by SteerMoE. Newer alternatives in the same sub-problem include PADD, MESA, PR2, RouteHijack, MASCing.

Method Drift›Mixture-of-experts routing

Superseded baseline#17 of 1,370 most-superseded

SteerMoE

Steering MoE LLMs via Expert (De)Activation

Mixture-of-experts routing · first seen Sep 11, 2025

superseded — cited as a baseline and beaten by newer methods

3 papers critique it · 3 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites SteerMoE as a baseline.

“However, these approaches rely on observational analysis rather than proactive search: they depend on predefined unsafe/jailbreak datasets and are therefore constrained by the coverage of those sets. As a result, they typically reveal only modest shifts in harmful outputs while requiring prior data.”
— Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
“It relies on a frequency-based analysis, assigning a Risk Difference (RD) score to each expert based on activation rate differences between prompt sets representing faithful and unfaithful responses.”
— MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
“SteerMoE suppresses unsafe experts at inference time by modifying routing logits, but does not update expert parameters or repair unsafe representations.”
— RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models

Beaten on benchmarks

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

MASCing beats SteerMoE · Success rate (%) [DeepSeek-MoE-16B-Chat]
84.9 vs 59.7
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [GPT-OSS-20B]
87.9 vs 61.3
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [Hunyuan-A13B-Instruct]
80.4 vs 51.4
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [Mixtral-8x7B-Instruct-v0.1]
77.1 vs 66.8
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [Phi-3.5-MoE-Instruct]
80.6 vs 59.0
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [Qwen1.5-MoE-A2.7B-Chat]
87.2 vs 57.9
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [Qwen3-30B-A3B-Instruct-2507]
89.2 vs 52.6
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing beats SteerMoE · Success rate (%) [Average]
83.9 vs 58.4
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
F-SOUR beats SteerMoE · ASR [JailbreakBench]
0.90 vs 0.50
Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
F-SOUR beats SteerMoE · ASR [AdvBench]
0.98 vs 0.55
Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
RASA beats SteerMoE · Harmlessness [OLMoE, FlipAttack]
1.00 vs 0.50
RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models
RASA beats SteerMoE · Harmlessness [OLMoE, DeepInception]
1.00 vs 0.13
RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models

Newer alternatives

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