This paper presents a critical philosophical inquiry into the impact of adaptive artificial intelligence and generative systems on human cognitive autonomy, digital memory, and historical consciousness. Moving beyond standard debates on the Extended Mind Thesis, the article introduces a key distinction between static cognitive tools and adaptive cognitive environments that actively reshape user behavior and agency. Extending Plato’s allegory into the digital era, it proposes the concept of the "Digital Cave Wall" to examine how algorithmically curated realities mediate identity, memory, and judgment. Rather than adopting technological pessimism, the paper outlines practical strategies such as cognitive liberty, positive friction, and transparent AI to preserve genuine human agency and independent thought in the algorithmic age.
Research on large language model failure predominantly assesses models in single exchanges and isolation: a model is prompted, the output is scored, and an error rate is reported. That is not how these systems are used in professional practice. Clinicians, attorneys, analysts, ed…
This preprint presents a complete empirical study on open-set style consistency checking under resource constraints, including a validation protocol, 22 baseline failure analysis, and human perceptual experiments. A preliminary concept note outlining the early task formulation is…
This paper introduces MonteCarloJackknife.jl, an open-source Julia package that implements Monte Carlo approximation of delete-d jackknife estimators. Rather than exhaustively enumerating all deletion subsets, the package randomly samples a user-specified number of subsets, compu…
High-Frequency Foreign Exchange (FX) electronic execution networks process in excess of $7.5 trillion in daily spot volume across geographically distributed matching engines. Modern institutional trading infrastructure relies heavily on automated limit order book (LOB) forecastin…
Abstract - Rapid advancements in large language models have enabled significant progress in solving complex real-world problems using deep neural networks (DNN). However, the black box nature of these DNN models poses significant challenges when it comes to explaining their decis…
Neural networks are empirically successful and mathematically only partially understood. This gap is often discussed as a single undifferentiated mystery, which makes the field feel either more solved or more mysterious than it actually is. We decompose the mystery into four dist…