This week I want to look at where we are with psychedelics, the mind-altering substances that have somehow made the leap from counterculture to major focus of clinical research. Compounds like psilocybin—which is found in magic mushrooms—are being explored for all sorts of health applications, including treatments for depression, PTSD, addiction, and even obesity. Over […]
Development and Evaluation of a Hallucination Awareness Scale for Healthcare Professionals and its impact on diagnostic confidence
Generative artificial intelligence (Gen AI) has gained immense significance in recent years, particularly in the field of healthcare. Despite its significant role in streamlining healthcare-related tasks, there remain unanswered concerns regarding the challenges of incorporating this technology into healthcare settings and it effect on diagnostic confidence. The purpose of this research is to address this […]
Development and interpretable machine learning models for classification of pancreatic pseudocyst risk in acute pancreatitis
IntroductionPancreatic pseudocysts (PPC) are a late local complication of acute pancreatitis (AP). Persistent PPC carry a high risk of severe outcomes. Existing models, which are predominantly based on logistic regression, exhibit limited predictive performance and have not undergone temporal validation. This study aimed to develop and validate an interpretable machine learning model using routinely available […]
Measuring and Exploiting Confirmation Bias in LLM-Assisted Security Code Review
arXiv:2603.18740v1 Announce Type: cross Abstract: Security code reviews increasingly rely on systems integrating Large Language Models (LLMs), ranging from interactive assistants to autonomous agents in CI/CD pipelines. We study whether confirmation bias (i.e., the tendency to favor interpretations that align with prior expectations) affects LLM-based vulnerability detection, and whether this failure mode can be exploited […]
Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs
arXiv:2603.18911v1 Announce Type: cross Abstract: Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning on external knowledge sources. However, most existing approaches focus exclusively on English, lack explicit citation mechanisms for verifying factual claims, and offer limited transparency into model decision-making. We present XKD-Dial, a progressive four-stage training pipeline for explainable, knowledge-grounded […]
Page image classification for content-specific data processing
arXiv:2507.21114v2 Announce Type: replace-cross Abstract: Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data […]
Sheaf Neural Networks and biomedical applications
arXiv:2602.00159v2 Announce Type: replace-cross Abstract: The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN can effectively answer to biomedical questions in a concrete case study and outperform the most popular graph neural networks (GNNs) as graph convolutional networks (GCNs), […]
How Uncertainty Estimation Scales with Sampling in Reasoning Models
arXiv:2603.19118v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended chain-of-thought reasoning. We study parallel sampling as a fully black-box approach using verbalized confidence and self-consistency. Across three reasoning models and 17 tasks spanning mathematics, STEM, and humanities, we characterize how these signals scale. Both […]
Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
arXiv:2603.18032v1 Announce Type: cross Abstract: Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the […]
MCP-38: A Comprehensive Threat Taxonomy for Model Context Protocol Systems (v1.0)
arXiv:2603.18063v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software systems or generic LLM deployments, do not adequately cover. This paper presents MCP-38, a protocol-specific threat taxonomy consisting of 38 threat categories (MCP-01 through MCP-38). The taxonomy was derived through a […]
MineDraft: A Framework for Batch Parallel Speculative Decoding
arXiv:2603.18016v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model. However, the performance of standard SD is often limited by the strictly sequential execution of these drafting and verification stages. To address this, […]
Engineering Verifiable Modularity in Transformers via Per-Layer Supervision
arXiv:2603.18029v1 Announce Type: cross Abstract: Transformers resist surgical control. Ablating an attention head identified as critical for capitalization produces minimal behavioral change because distributed redundancy compensates for damage. This Hydra effect renders interpretability illusory: we may identify components through correlation, but cannot predict or control their causal role. We demonstrate that architectural interventions can expose […]