Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time–frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures’ onset. These trends pointed to a slow decrease of the epileptic brain’s global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.

The increasing deployment of large language models (LLMs) in enterprise environments creates reliability challenges related to hallucination, factual inconsistency, limited knowledge traceability, uncertainty, and operational efficiency. This study develops a literature-based architectural framework for reliable knowledge retrieval systems that separates external knowledge management from LLM-based reasoning and generation. The framework synthesizes architectural mechanisms for knowledge representation, hybrid retrieval, reranking, evidence selection, context construction, response verification, provenance tracking, uncertainty handling, guardrails, and computational efficiency. The resulting architecture organizes these mechanisms into coordinated layers that control the flow of external evidence from knowledge sources to generated responses while supporting traceability and controlled abstention when sufficient evidence is unavailable. The architectural synthesis further identifies complementary strategies for enterprise deployment, including semantic caching, model routing, and human oversight, to balance reliability, scalability, and operational cost. The analysis indicates that reliable LLM deployment should be treated as an end-to-end architectural problem rather than solely a model-performance problem, with knowledge access, evidence quality, verification, provenance, and governance functioning as integrated system components. The proposed framework provides a structured foundation for designing maintainable, auditable, and reliable knowledge retrieval systems for enterprise and other high-stakes applications