A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Shirui Pan, Linhao Luo, Yufei Wang, et al.
This paper presents a roadmap for unifying large language models and knowledge graphs through three frameworks: KG-enhanced LLMs, LLM-augmented KGs, and synergized LLMs+KGs.
Muthukuda Arachchige Dona Shiroma Jeeva Shirajanie Niriella
This paper examines AI's integration into Sri Lankan sentencing, highlighting risks of bias and proposing regulatory frameworks for equitable judicial use.
Giang Nguyen, Štefan Dlugolinský, Martin Bobák, et al.
This survey provides a comprehensive overview and comparison of Machine Learning and Deep Learning frameworks and libraries for large-scale data mining, highlighting trends and massive parallelism support in the era of Big Data.
Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.
This survey systematically defines Small Language Models (SLMs), provides a taxonomy of methods for their acquisition, application, enhancement, and reliability, and offers frameworks for their effective use in resource-constrained settings.
A. G. Wilson
This paper argues that deep learning's anomalous generalization behaviors are not unique or mysterious, and can be explained by soft inductive biases within classical frameworks like PAC-Bayes.
Unknown
A comparative study evaluating DeepSeek, ChatGPT, and Claude on scientific computing and scientific machine learning tasks using Python frameworks.
Unknown
This survey provides a comprehensive overview of efficient diffusion models, covering acceleration techniques, architectural optimizations, and specialized frameworks.
Sajjad Khan
This paper introduces the RESUME CONTRACT, a machine-checked conformance contract for checkpoint, interrupt, and resume semantics in workflow persistence layers, and evaluates five agent frameworks against it.
Unknown
This review surveys the status, challenges, and future prospects of agent data evaluation as of 2025, highlighting the lack of dedicated privacy-preserving frameworks.
Unknown
Curriculum temperature for knowledge distillation (CTKD) is an easy-to-use plug-in technique that improves existing knowledge distillation frameworks with negligible additional cost.
Unknown
This survey provides a comprehensive overview of multimodal large language models (MLLMs), covering their frameworks, training strategies, and evaluation methods.
Unknown
This paper provides a comprehensive guide to building agentic AI systems, covering frameworks, prompt engineering, and multi-agent orchestration for real-world applications.