MAGMA combines RAG with a stochastic consistency ensemble over dual code embeddings to derive Function Evidence Strength and Evidence Conflict Score metrics, enabling reject-option decisions and achieving 98.4% malware detection.
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A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.
citing papers explorer
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Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detection
MAGMA combines RAG with a stochastic consistency ensemble over dual code embeddings to derive Function Evidence Strength and Evidence Conflict Score metrics, enabling reject-option decisions and achieving 98.4% malware detection.
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Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools
A low-stake adversary can degrade a liquid staking pool's performance via consensus manipulation and profit from the resulting drop in its LST value through application-layer financial positions.