Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detection

While contemporary deep learning malware detectors define a dominant defense paradigm, their sophistication also exposes them to novel structural evasion attacks, a limitation we attribute to their inherent inability to express epistemic uncertainty. To address this challenge, we present MAGMA, a Retrieval-Augmented Generation (RAG) framework that decouples malware analysis into semantic code retrieval and probabilistic verification. In contrast to monolithic classifiers, MAGMA employs a dual-stream embedding scheme over assembly and pseudo-code representations to isolate Decision-Critical Functions (DCFs) from the noise of dead code. We further introduce a Stochastic Consistency Ensemble, in which multiple instances of the same reasoning agent independently evaluate the retrieval set under non-deterministic sampling. From this ensemble, we derive two complementary metrics: Function Evidence Strength (FES), a weighted aggregation of retrieval confidence, and the Evidence Conflict Score (ECS), defined as the Shannon entropy of the ensemble's predictive distribution. We show that elevated ECS values serve as an effective proxy for structural ambiguity, enabling the system to implement a principled ``reject-option'' policy. Extensive evaluation demonstrates that MAGMA achieves a 98.4% detection rate, substantially exceeding existing solutions.