arXiv:2602.22280v1 Announce Type: new Abstract: Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot and few-shot reasoning capabilities, even though machine learning (ML) algorithms, especially ensemble approaches like Random Forest, XGBoost, LightGBM, and CatBoost, are excellent at modeling complex, non-linear patient data and routinely beat logistic regression. This research predicts cardiovascular disease using a merged dataset of 1,190 […]

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arXiv:2607.08777v1 Announce Type: new Abstract: Multiple Access (MA) technology has consistently served as the core driving force behind the evolution of mobile communications. As a promising paradigm for next-generation communications, Semantic Communication explores entirely new semantic spatial resources by mining the deep meaning of information. However, the inherent spatial correlation and importance heterogeneity of semantic features often cause semantic collisions and semantic collapse in multi-user concurrent transmission scenarios. To address these challenges, this paper proposes a Semantic Interleaved […]

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In the first part of this series, here, I feature a click fraud case that we are working on, litigated by one of the largest law firms in the US.  The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a […]

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One of the factors of financial management that is important is the external audit since it gives an independent assurance that the financial statement of an organization is accurate and reliable. The process of finding the audit planning was traditionally carried out by using past workpapers, assumptions and sampling. The general approach adopted by these teams was the experience and gut feeling, which was inefficient and often overlooked risks. But this is changing. Data analytics is also making […]

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arXiv:2602.17678v1 Announce Type: new Abstract: Reproducible container builds promise a simple integrity check for software supply chains: rebuild an image from its Dockerfile and compare hashes. We build a Docker measurement pipeline and apply it to a stratified sample of 2,000 GitHub repositories that contained a Dockerfile. We found that only 56% produce any buildable image, and just 2.7% of those are bitwise reproducible without any infrastructure configurations. After modifying infrastructure configurations, we raise bitwise reproducibility by 18.6%, […]

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arXiv:2603.16930v1 Announce Type: new Abstract: Facial beauty prediction (FBP) is an important and challenging problem in the fields of computer vision and machine learning. Not only it is easily prone to overfitting due to the lack of large-scale and effective data, but also difficult to quickly build robust and effective facial beauty evaluation models because of the variability of facial appearance and the complexity of human perception. Transfer Learning can be able to reduce the dependence on large […]

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For the past year, we at the AI Futures Project have been sinking most of our time into our next big scenario. Now it’s done!  It’s called AI 2040: Plan A. It’s called Plan A because it’s a recommendation, not a prediction. It’s what we think should happen, not what will happen, though we think it’s plausible enough to aim for. It’s called AI 2040 because in it, they delay the creation of superintelligence to 2040. It would have happened much sooner (in 2030, to be precise) if not […]

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Adapting Large Multimodal Models (LMMs) to real-world scenarios poses the dual challenges of learning from sequential data streams while handling frequent modality incompleteness, a task known as Continual Missing Modality Learning (CMML). However, existing works on CMML have predominantly relied on prompt tuning, a technique that struggles with this task due to cross-task interference between its learnable prompts in their shared embedding space. A naive application of Low-Rank Adaptation (LoRA) with modality-shared module will also suffer modality interference […]

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Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As regulations and governance frameworks increasingly require deleting sensitive data from deployed models, machine unlearning is emerging as a practical alternative to full retraining. However, unlearning introduces a software quality-assurance challenge: under realistic deployment constraints and imperfect oracles, how can we test that a model no longer relies on targeted information? This paper frames unlearning testing as a first-class […]

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