# Graph Autoencoder

High-dimensional PDE forecasting on unstructured meshes has run on superconducting quantum processors for the first time. A graph autoencoder compresses mesh fields into a low-dimensional latent state, a fixed, untrained quantum reservoir network supplies features, and a classical linear readout predicts the next state; on a 33,824-cell cylinder flow and a Gray–Scott reaction–diffusion problem, adding those features lowers normalized root mean square error by roughly 21–43% for one-step and pressure-observer prediction against a linear baseline on the same latent space, with the pressure observer demonstrated only on the 26-qubit cylinder configuration and the 52-qubit hardware used for Gray–Scott one-step forecasting.

High-dimensional PDE forecasting on unstructured meshes has run on superconducting quantum processors for the first time. A graph autoencoder compresses mesh fields into a low-dimensional latent state, a fixed, untrained quantum reservoir network supplies features, and a classical linear readout predicts the next state; on a 33,824-cell cylinder flow and a Gray–Scott reaction–diffusion problem, adding those features lowers normalized root mean square error by roughly 21–43% for one-step and pressure-observer prediction against a linear baseline on the same latent space, with the pressure observer demonstrated only on the 26-qubit cylinder configuration and the 52-qubit hardware used for Gray–Scott one-step forecasting.

Provenance-based intrusion detection systems face a precision-recall bind: suppressing false alerts misses attack entities, while raising recall buries analysts. Targeter stops modeling all processes with one detector, sorting processes into behavioral categories by command-line semantics and training a lightweight graph autoencoder per category, with deterministic routing at inference. Across six DARPA Transparent Computing benchmarks it reports a better F2 balance than existing node-level systems on most datasets; on THEIA-E5 recall is 0.61 versus 0.17 for ORTHRUS, though on CADETS-E3 its F2 trails ORTHRUS.

Provenance-based intrusion detection systems face a precision-recall bind: suppressing false alerts misses attack entities, while raising recall buries analysts. Targeter stops modeling all processes with one detector, sorting processes into behavioral categories by command-line semantics and training a lightweight graph autoencoder per category, with deterministic routing at inference. Across six DARPA Transparent Computing benchmarks it reports a better F2 balance than existing node-level systems on most datasets; on THEIA-E5 recall is 0.61 versus 0.17 for ORTHRUS, though on CADETS-E3 its F2 trails ORTHRUS.

A federated LLM server now treats each agent's LoRA update as a node in a hypergraph and passes messages along hyperedges to spot and suppress poisoned knowledge, beating Multi-Krum, FoolsGold and FLTrust on AG News and Yahoo! Answers.

A federated LLM server now treats each agent's LoRA update as a node in a hypergraph and passes messages along hyperedges to spot and suppress poisoned knowledge, beating Multi-Krum, FoolsGold and FLTrust on AG News and Yahoo! Answers.