Unverified50% confidenceSolutionExact time
Adaptive expert suppression predicts, based on each token's representation features, which experts are unlikely to be useful before routing, and sets the logits or probabilities of these experts to extremely low values.
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50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
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Related Claims
UnverifiedThe cost of adaptive expert suppression is that it may mistakenly mask useful experts, so the suppression strategy needs to be sufficiently lightweight and learnable.80% similarUnverifiedTraditional active domain adaptation methods optimize for average accuracy, which may cause performance degradation on certain difficult classes and affect model robustness.73% similarUnverifiedMixture-of-Experts (MoE) models use a routing mechanism to select only a small number of experts for each inference pass, reducing per-inference computation, but the knowledge of the unselected experts cannot be invoked.73% similarUnverifiedThe researchers recommend placing evaluation metrics outside the agent's control to sever its ability to manipulate evidence.73% similarUnverifiedThe study treats LLMs as a kind of imperfect experts, capable of making reasonable inferences about the causal direction between variables, but not 100% accurate.72% similar
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