explainable ai

AI-Powered Smart Contract Security Optimization Using Expert Predictive Learning

Peer Review Report For: Beyond Technology-Centrism: The Role of Behavioral HR Competencies in Shaping Employee Performance through AI Integration [version 2; peer review: 2 approved with reservations, 1 not approved]

Background The use of artificial intelligence (AI) in Human Resource (HR) is often linked to HR employee performance. This view, however, does not fully explain how performance emerges within AI-integrated work environments, where outcomes depend on how HR employees actually behave. Although a few emerging research has...

Why Analytical Minds Treat Nonsense with Dignity: A Two-Stage Self-Referential Elicitation Study of Hermeneutic Persistence in AI-Generated Audio Dialogue

This record documents a two-stage self-referential elicitation experiment using AI-generated NotebookLM Audio Overview dialogue. The experiment examines whether an analytical dialogue format continues to generate interpretive structure around deliberately low-information material after explicitly recognizing that the m...

A Negative Result on State-Chaining over MITRE ATLAS

Method note reporting the pre-registered retrospective test of AI-RISKPATH: whether a mechanical chaining engine, driven only by precondition/effect labels attached to the 208 techniques of MITRE ATLAS v2026.09, can reconstruct the 73 attack chains ATLAS documents. The case studies were split by a public randomness bea...

When Deliberation Hurts: Inverse Test-Time Scaling, Unfaithful Traces, and the Case Against a Unified System-2 in LLM Reasoning

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. The dominant frame for large reasoning models borrows a label from dual-process psychology: a fast, intuitive System 1 and a slower, deliberate System 2, with longer chains of thought read as more of the latter and therefore, on average, more reliable. A survey of...

Explainable Multi-Modal Deep Learning for Early-Stage Pan-Cancer Detection and Survival Prognostication: Empirical Benchmarking of Gigapixel Whole-Slide Histopathology and Genomic Biomarkers Across Multi-Center Clinical Cohorts

Early and accurate cancer diagnosis combined with robust survival risk prognostication remains the paramount determinant of therapeutic success in precision oncology. While routine clinical workflows evaluate hematoxylin and eosin (H&E) stained gigapixel Whole Slide Images (WSI) for morphological staging and transcript...

When Deliberation Hurts: Inverse Test-Time Scaling, Unfaithful Traces, and the Case Against a Unified System-2 in LLM Reasoning

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. The dominant frame for large reasoning models borrows a label from dual-process psychology: a fast, intuitive System 1 and a slower, deliberate System 2, with longer chains of thought read as more of the latter and therefore, on average, more reliable. A survey of...

Explainable Multi-Modal Deep Learning for Early-Stage Pan-Cancer Detection and Survival Prognostication: Empirical Benchmarking of Gigapixel Whole-Slide Histopathology and Genomic Biomarkers Across Multi-Center Clinical Cohorts

Early and accurate cancer diagnosis combined with robust survival risk prognostication remains the paramount determinant of therapeutic success in precision oncology. While routine clinical workflows evaluate hematoxylin and eosin (H&E) stained gigapixel Whole Slide Images (WSI) for morphological staging and transcript...

Enterprise AI Governance Architecture: Integrating Security, Risk, Compliance, and Human Oversight

Large Language Models (LLMs) and other generative forms of artificial intelligence are rapidly moving into enterprise environments. Challenges surround ensuring the security of such models, managing risks associated with their use, ensuring that all use meets organizational and external compliance requirements, and ens...

Adaptive Human-Centric Representation Learning for Robust and Fair Identity Systems under Distribution Shift

Human-centric identity systems deployed in real-world environments continuously encounter non-stationary conditions, such as changing illumination, background noise, device transitions, and physiological aging. These environmental and temporal variations introduce distribution shift, degrading system performance, eleva...

Adaptive Human-Centric Representation Learning for Robust and Fair Identity Systems under Distribution Shift

Human-centric identity systems deployed in real-world environments continuously encounter non-stationary conditions, such as changing illumination, background noise, device transitions, and physiological aging. These environmental and temporal variations introduce distribution shift, degrading system performance, eleva...

Faithful Faithfulness Evaluations: Challenges&Pitfalls Learned from a Breast MRI Case Study

Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect a model's true decision process and may therefore mislead clinicians. We investigate this problem using a Vision Transformer-based breast MRI classifier trained on the OD...