A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Pete Burnap, Matthew Williams
This paper applies supervised machine learning and statistical modeling to classify and forecast cyber hate speech on Twitter following a real-world event, demonstrating policy applications.
Paul Bergmann, Kilian Batzner, Michael Fauser, et al.
Introduces MVTec AD, a comprehensive dataset with 5354 high-resolution images across 15 categories for unsupervised anomaly detection, and benchmarks state-of-the-art methods.
Vito Latora, Massimo Marchiori
Introduces network efficiency as a measure of information exchange, showing small-world networks are both globally and locally efficient.
Emilio Ferrara
This survey explores early trends, datasets, and challenges in applying large language models to wearable sensor data for human activity recognition, health monitoring, and behavioral modeling.
Jialong Li, Mingyue Zhang, Nianyu Li, et al.
This paper surveys the potential benefits and challenges of using generative AI, particularly LLMs, to enhance self-adaptive systems, and outlines a research roadmap.
Volker Bilgram, Felix Laarmann
Generative AI, particularly LLMs like GPT, can augment early innovation phases—exploration, ideation, and digital prototyping—by enabling faster iterations and reduced costs.
Xinyi Li, S. Wang, Siqi Zeng, et al.
A comprehensive survey of LLM-based multi-agent systems, proposing a unified five-component workflow framework and reviewing applications in problem-solving and world simulation.
Jochen Wirtz, Paul G. Patterson, Werner H. Kunz, et al.
This conceptual paper defines service robots, contrasts them with human frontline employees, and proposes a service robot acceptance model while exploring ethical implications.
J. P. HUGHES, S. Rees, S. Barret Kalindjian, et al.
This review outlines the key preclinical stages of drug discovery, from target identification to candidate selection, emphasizing the complexity and cost of developing new drugs.
Zorik Gekhman, G. Yona, Roee Aharoni, et al.
This paper shows that supervised fine-tuning introducing new factual knowledge to LLMs is learned slowly and linearly increases hallucination, supporting that factual knowledge is acquired during pre-training.
Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.
Reinforcement learning with verifiable reward using a single training example dramatically improves LLM math reasoning, matching or exceeding results from thousands of examples.
Juncheng Wu, Sheng Liu, Haoqin Tu, et al.
This paper introduces a fine-grained evaluation framework decomposing LLM reasoning into knowledge correctness and reasoning quality, revealing that SFT improves accuracy but degrades reasoning, while RL enhances medical reasoning by pruning inaccura