A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Artem R Cherkasov, Eugene N. Muratov, Denis Fourches, et al.
A comprehensive perspective on QSAR modeling's history, challenges, and best practices for building rigorously validated and externally predictive models.
Baoguang Shi, Xiang Bai, Cong Yao
A novel end-to-end trainable neural network integrates feature extraction, sequence modeling, and transcription for scene text recognition without character segmentation.
Aidan P. Thompson, Hasan Metin Aktulga, Richard A. Berger, et al.
This paper serves as the definitive reference for LAMMPS, describing its fundamental algorithms, design strategies for flexibility, and recent capabilities enabled by its extensible architecture.
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A survey of recent advances in using large language models as judges for evaluation, critique, preference modeling, and feedback on text and code.
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FaithfulRAG introduces fact-level conflict modeling to improve context faithfulness in retrieval-augmented generation, with potential extensions to multimodal RAG and trustworthy AI.
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A closed-loop long-horizon robotic planning method using equilibrium sequence modeling that more than doubles baseline success rates on long-horizon tasks.
Noah Snavely, Steven M. Seitz, Richard Szeliski
Presents a system for interactively browsing and exploring large unstructured photo collections using a novel 3D interface with image-based modeling and rendering.
Masahiro Kato, Daiki Honma, Taka Kato
GMMM is a causal inference framework that estimates the business impact of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM) by combining generated answers, question counts, system shares, and notice probabilities.
Wei Li, Rui Shao, Jie He, et al.
UniMPA unifies memory, prediction, and action via an action-grounded transition interface to resolve transition realizability gaps in robotic manipulation.
Narges Ahmadi, Yubo Jiao, Jônatas Augusto Manzolli, et al.
This paper proposes a three-agent workflow integrating conversational data collection, structured processing, and LLM-based prediction for travel behavior, achieving 71.5% accuracy with vision-based LLMs.
Mark V. Pauly, Richard Keiser, Leif Kobbelt, et al.
A hybrid geometry representation combining unstructured point clouds with moving least squares implicit surfaces enables versatile free-form shape modeling with boolean operations and topology control.
Qingxiu Dong, Li Dong, Yao Tang, et al.
Introduces Reinforcement Pre-Training (RPT), reframing next-token prediction as a reasoning task trained with RL and verifiable rewards, improving language modeling accuracy and providing a strong foundation for RL fine-tuning.