A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This survey provides a fine-grained analysis of diffusion model design fundamentals, categorizing design factors for different purposes and implementation strategies.
Bowei Tian, Xuntao Lyu, Meng Liu, et al.
This paper provides a theoretical and empirical analysis of why representation engineering works in vision-language models, offering a unified framework and practical insights.
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This paper proposes a hierarchical, session-isolated detection framework to identify benchmark contamination in LLM coding benchmarks, addressing solution leakage and test quality issues.
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This paper systematically categorizes and explores KV cache compression techniques for transformer-based models, providing a structured analysis of methods to reduce memory overhead.
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A comprehensive survey of multimodal RAG covering all combinations of input and output modalities, providing a taxonomy and analysis of recent work.
Zohaib Salahuddin, Henry C. Woodruff, Avishek Chatterjee, et al.
This review categorizes nine interpretability methods for deep neural networks in medical image analysis, evaluates progress in explanation assessment, and provides guidelines and future directions for trustworthy AI.
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This paper provides a comprehensive analysis of when to use graphs in retrieval-augmented generation, offering a decision framework and empirical comparisons of GraphRAG versus vector RAG.
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This paper provides a historical and socio-technical analysis of foundation models, contrasting them with prior deep learning models and tracing the evolution of machine learning leading to their emergence.
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This paper argues that the standard reasoning paradigm in LLM agents is fundamentally incompatible with reliable long-horizon planning, explaining why reasoning fails to plan.
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review distinguishes AI Agents from Agentic AI, providing a taxonomy, application mapping, and analysis of challenges and solutions.
Hongming Chen, Ola Engkvist, Yinhai Wang, et al.
This paper reviews the first wave of deep learning applications in drug discovery, covering bioactivity prediction, de novo molecular design, synthesis prediction, and biological image analysis.
Andrew Walker, Jerik Leung, Aishwarya Alagappan, et al.
This study uses NLP and LLMs to analyze Reddit lupus narratives, extracting multidimensional biopsychosocial pain insights to support patient-centered rheumatology care.