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#artificial intelligence Review Open access

How is artificial intelligence being used to discover new treatments for leishmaniasis? A Scoping Review

Nicolas Rodrigues Blaskovski F. Fanhani Gustavo Scheiffer Marcos Anthony Mallqui Rondan R. Lazo B. K. Hammerschmitt Marcel Henrique Marcondes Sari R. Pontarolo L. Ferreira
Nov 2026 · Chemometrics and Intelligent Laboratory Systems · 0 citations · 47 references
DOI Semantic Scholar
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#artificial intelligence Review Dec 2025

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.

Ruanqianqian Huang, Avery Reyna, Sorin Lerner et al. · 19 citations · ⚡1
#artificial intelligence Review Open access Jan 2026

A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.

Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al. · 4 citations · ⚡1
#artificial intelligence Apr 2026

Convergent Evolution: How Different Language Models Learn Similar Number Representations

This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.

Deqing Fu, Tianyi Zhou, Mikhail Belkin et al. · 3 citations
#artificial intelligence Open access Feb 2025

Iterative Flow Matching - Path Correction and Gradual Refinement for Enhanced Generative Modeling

This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.

Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al. · 3 citations
#artificial intelligence Preprint Aug 2026

Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions

Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to $2^{70}$ configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.

Xiao Wang, Tomohiro Hashizume, Pia Siegl et al. · 2 citations
#artificial intelligence May 2026

When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition

A benchmark built on the Speech Accessibility Project (SAP) dataset is introduced that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech, finding that current models do not meaningfully use this context.

P. Moure, Niclas Pokel, Bilal Bounajma et al. · 2 citations

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