AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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180 papers
Structural reasoning, the ability to recognize and make inferences over the relational structure between objects and concepts, is a hallmark of human cognition, yet prevailing methods often collapse relational topology into flat embeddings, cannot discover hidden structure and lack interpretability. We introduce Neural...
Conditioned DFA (nDFA), a family that adapts established inverse-moment preconditioning to either side of this update, is studied, establishing practical benefits and important limits of conditioning learning with fixed random feedback.
Through a four-speaker attention decoding benchmark, it is shown that combining behavioral and physiological signals improves decoding performance over EEG-only approaches, enabling future advances in multimodal auditory attention decoding.
NeuronSifter treats decision quality as a property of the intervention interface, not of controller placement, and selects measurements by their expected reduction in intervention loss, assimilating typed outcomes into the same posterior.
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NeuronDiscover is an Agent-in-Twin framework whose shared, mechanism-grounded World Action Model (WAM) couples prediction, intervention proposals, and observation design; independently adjudicated outcomes revise a scoped Mechanism--Intervention--Observation--Outcome (MIOY) graph, whose supported relations compile into...
Personal adaptation of three frozen EEG foundation models was evaluated in 235 held-out subjects in 235 held-out subjects from three motor-imagery datasets, comparing each subject's adapter with the population model and with adapters fitted to other subjects.
FAST-Brain is proposed, a unified flow-aligned spatio-temporal surrogate brain model that addresses all three challenges of resting-state functional magnetic resonance imaging data, and achieves state-of-the-art performance in recovering functional connectivity, effective connectivity, and the implicit low-dimensional...
The Temporal Simplicial Neural Network is introduced, which represents EEG recordings as sequences of evolving simplicial complexes, and jointly learns higher-order interactions and their temporal evolution by combining simplicial convolutions with recurrent updates.
A distribution-aware, information-theoretic measure of effective connectivity based on channel capacity under general residual distributions based on normalizing flows is introduced, and a principled distribution-aware framework for effective-connectivity estimation beyond Gaussian residual modeling is established.
This work introduces a symbolic distillation pipeline that converts trained neural likelihoods into explicit, interpretable expressions optimized for efficient parameter estimation, and uses a recovery-directed objective to guide symbolic regression toward expressions that preserve parameter-recovery accuracy.
The results suggest that depth and feedforward blocks enable transformers to implement adaptive inference, and this is advantageous when the distribution over latent variables has hierarchical structure.
A new model family (CortexMAE) trained using the masked autoencoder framework is introduced, and in this setting the CortexMAE family outperforms prior models by a large margin, and the first open evaluation suite (Brainmarks) for fMRI foundation models is released.
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