AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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134 papers
The pulling illusion induced by asymmetric vibration stimuli has attracted attention for its potential applications in rehabilitation and sensory assessment. However, the underlying mechanism of the pulling illusion remains unclear. This study addressed the central question of whether peripheral vibrotactile sensitivit...
Multimodal large language models predict brain activity, but brain alignment has been a measurement, not a design tool. We propose the Platonic brain bridge hypothesis: omni models, multimodal large language models that process video, audio and text jointly, converge on brain-like representations usable in both directi...
EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification, is presented and results support EEG-centric multimodal binding as a practical strategy for source-level IED classification.
XAI-Refine is proposed, an automated explanation-knowledge loop for brain-age prediction from resting-state functional connectivity that consolidates complementary post-hoc analyses across repeated training runs into reliable, structured model explanations and proposes a structured route from post-hoc analysis to evide...
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We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures a broad range of human intelligence behaviors with analytical mechan...
Recurrent neural networks are canonical models of biological memory. In these models, memories are represented by distributed patterns of neural activity that are stored in the recurrent connections between neurons, such that they become attractors of the network's dynamics. During memory recall, network dynamics thus...
This work reformulates temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state, which enables an online feedback learning framework for NMCs through a gradient tunneling algorithm and the lead-lag expansion...
Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single scalar. These scalars are learned by models through gradient-descent on error, where predictive features acquire more salience. We show that under multi-outcome learning, w...
EmoMind is presented, the first end-to-end pipeline for decoding affective captions directly from fMRI signals and establishes continuous brain-decoded affect as a viable control signal for individualized affective caption generation and open new directions for studying individual affective brain organisation.
It is hypothesized that neuroimaging data could open a window into elements of human cognition that are not accessible through observable actions, and it is argued that this additional knowledge could be used, alongside classical training data, to overcome some of the current limitations of foundation models.
A minimal analytical model distinguishing functional rules Phi_t = {R0, ..., Rk}, modification mechanisms Mt, and evaluation criteria Nt is proposed, which yields a conditional crossed-opacities hypothesis: humans often have richer endogenous self-description at abstract and strategic levels than at implementation leve...
The results demonstrate the mathematical consistency and computational feasibility of the proposed model, while the neurobiological hypotheses outline potential directions for its experimental verification.
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