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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AsmRAG detects malware at 96% F1 and attributes families at 95% F1 by retrieving functionally similar assembly code via LLM embeddings and density-weighted anchor selection, remaining robust to metamorphic obfuscation.
CGCMA separates text-conditioned grounding from lag-aware trust gating to fuse asynchronous price and web data, yielding the highest Sharpe ratio of +0.449 on a new crypto news corpus.
Orthonormal Data Collaboration (ODC) enforces orthonormal secret and target bases so that alignment reduces to the Orthogonal Procrustes problem, yielding O(acl^2) complexity, orthogonal concordance, and downstream performance invariant to the choice of target basis.
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
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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AsmRAG: LLM-Driven Malware Detection by Retrieving Functionally Similar Assembly Code
AsmRAG detects malware at 96% F1 and attributes families at 95% F1 by retrieving functionally similar assembly code via LLM embeddings and density-weighted anchor selection, remaining robust to metamorphic obfuscation.
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CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion
CGCMA separates text-conditioned grounding from lag-aware trust gating to fuse asynchronous price and web data, yielding the highest Sharpe ratio of +0.449 on a new crypto news corpus.
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Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Orthonormal Data Collaboration (ODC) enforces orthonormal secret and target bases so that alignment reduces to the Orthogonal Procrustes problem, yielding O(acl^2) complexity, orthogonal concordance, and downstream performance invariant to the choice of target basis.
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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.