arXiv:2506.00416v2 Announce Type: replace-cross Abstract: Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual’s unique physiology and usage patterns. Recently, […]

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We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable perturbations into a standard Krasnosel’skii-Mann iteration to improve its average-case performance over a specific distribution of problems while retaining its convergence guarantees. Under a metric sub-regularity assumption, we prove that the proposed parametrization includes only iterations that locally achieve linear convergence-up to a vanishing bias term-and that it encompasses all iterations that do so […]

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Background/Objectives: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and is frequently associated with chronic hepatitis C virus (HCV) infection. Early prediction of HCC in HCV patients remains challenging due to complex clinical patterns. This study aims to develop and evaluate machine learning models for the early prediction of hepatocellular carcinoma in patients with HCV. Methods: Clinical and laboratory data from HCV patients were analyzed using a machine learning–based framework. The dataset was preprocessed, and […]

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arXiv:2405.16828v4 Announce Type: replace-cross Abstract: In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the challenge of establishing a conditional coverage guarantee for non-exchangeable data under strong mixing conditions on the non-conformity scores. We demonstrate the superior performance of KOWCPI on real […]

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arXiv:2601.15476v1 Announce Type: new Abstract: This paper examines how to make large language models reliable for high-stakes legal work by reducing hallucinations. It distinguishes three AI paradigms: (1) standalone generative models (“creative oracle”), (2) basic retrieval-augmented systems (“expert archivist”), and (3) an advanced, end-to-end optimized RAG system (“rigorous archivist”). The authors introduce two reliability metrics -False Citation Rate (FCR) and Fabricated Fact Rate (FFR)- and evaluate 2,700 judicial-style answers from 12 LLMs across 75 legal tasks using expert, […]

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arXiv:2601.12181v1 Announce Type: new Abstract: AI companions enable deep emotional relationships by engaging a user’s sense of identity, but they also pose risks like unhealthy emotional dependence. Mitigating these risks requires first understanding the underlying process of identity construction and negotiation with AI companions. Focusing on Character.AI (C.AI), a popular AI companion, we conducted an LLM-assisted thematic analysis of 22,374 online discussions on its subreddit. Using Identity Negotiation Theory as an analytical lens, we identified a three-stage process: […]

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Author: Dev Pragad, CEO, Newsweek As artificial intelligence platforms increasingly mediate how people encounter news, media leaders are confronting an important change in the relationship between journalism and the public. AI-driven search and conversational interfaces now influence how audiences discover and trust information, often before visiting a publisher’s website. According to Dev Pragad, the implications for journalism extend beyond traffic metrics or platform optimisation. “AI has effectively become a front door to information, That changes how journalism is […]

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Active learning (AL) strategies aim to train high-performance models with minimal labeling efforts, only selecting the most informative instances for annotation. Current approaches to evaluating data informativeness predominantly focus on the data’s distribution or intrinsic information content and do not directly correlate with downstream task performance, such as mean average precision (mAP) in object detection. Thus, we propose Performance-guided (i.e. mAP-guided) Reinforced Active Learning for Object Detection (MGRAL), a novel approach that leverages the concept of expected model […]

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Byline: Keith Belanger AI projects have a way of surfacing data problems that data teams used to be able to work around. That’s because analytical data allowed for a wide margin of error, and AI simply doesn’t. AI models don’t tolerate ambiguity, and decisions made at machine speed magnify every flaw hiding upstream. What once failed quietly now fails loudly, and often publicly. AI failures are often dismissed as experimental growing pains. In reality, they’re revealing the weakness […]

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arXiv:2602.20338v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting significantly improves reasoning in Large Language Models, yet the temporal dynamics of the underlying representation geometry remain poorly understood. We investigate these dynamics by applying Manifold Capacity Theory (MCT) to a compositional Boolean logic task, allowing us to quantify the linear separability of latent representations without the confounding factors of probe training. Our analysis reveals that reasoning manifests as a transient geometric pulse, where concept manifolds are untangled into linearly […]

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