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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 Apr 2026

AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems

This work presents AutoOR, a scalable synthetic data generation and reinforcement learning pipeline that trains LLMs to autoformulate optimization problems specified in natural language across linear, mixed-integer, and non-linear categories and introduces a curriculum RL strategy that bootstraps from limited initial training data to make this class tractable for post-training.

S. Motwani, Chuan Du, A. Petrov et al. · 2 citations
#artificial intelligence Review Jun 2026

Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective

This survey model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites to provide a compact structural lens for designing and governing self-evolving agents.

Yuanyuan Xu, Wenjie Zhang, Yin Chen et al. · 2 citations

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