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.
In: International Conference on Learning Representations (ICLR) (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
Small instruction-tuned language models cannot reliably estimate graph-theoretic properties from textual encodings, though adjacency-list formats and multi-branch reasoning reduce errors relative to edge lists and single-path inference.
citing papers explorer
-
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.
-
Graph Property Inference in Small Language Models: Effects of Representation and Reasoning Strategy
Small instruction-tuned language models cannot reliably estimate graph-theoretic properties from textual encodings, though adjacency-list formats and multi-branch reasoning reduce errors relative to edge lists and single-path inference.