Tensor Times
An academic-grade journal of artificial intelligence research — peer-reviewed analysis of large language models, machine learning frameworks, and the engineering behind today’s most consequential AI systems.
Latest Analysis
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Causal Inference in Large Language Models: Structural Causal Models for Counterfactual Reasoning and Intervention Analysis
Introduction: From Correlation to Causation in the Age of LLMs The remarkable fluency and knowledge of Large Language Models (LLMs) are built upon a foundation of statistical correlation.…
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AI-Assisted Synthetic Data Generation: Techniques for Privacy-Preserving Dataset Augmentation in Sensitive Domains
The proliferation of data-driven machine learning has created a paradox in sensitive domains such as healthcare, finance, and public sector analytics. While the potential for transformative insights is…
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The Role of Epistemic Uncertainty Quantification in Large Language Model Safety: Bayesian Methods for Confidence Calibration
As large language models (LLMs) are deployed in increasingly high-stakes domains—from medical diagnostics and legal analysis to autonomous systems and financial advising—the question of their reliability moves from…
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AI-Powered Supply Chain Optimization: Reinforcement Learning Approaches for Logistics and Inventory Management
Introduction: The Modern Supply Chain as a Complex Adaptive System The contemporary global supply chain is a paradigm of complexity, characterized by volatile demand, geopolitical uncertainties, multi-echelon networks,…
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Algorithmic Transparency in Public Sector AI: Policy Frameworks for Governmental Decision-Making Systems
The integration of artificial intelligence (AI) into public sector operations represents a paradigm shift in governance, promising unprecedented efficiency in service delivery, resource allocation, and regulatory enforcement. From…
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Neuroscience-Inspired Architectures for Continual Learning: Biological Plausibility in Artificial Neural Networks
The quest to build artificial intelligence that can learn continuously and adaptively from a non-stationary stream of data—a capability known as continual or lifelong learning—remains one of the…