A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Nuo Chen, Yicheng Tong, Yuzhe Yang, et al.
This paper systematically studies diversity collapse in multi-agent LLMs, showing that interaction structures, not model insufficiency, primarily cause reduced exploration diversity.
Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, et al.
This exploratory case study evaluates the quality of simplified radiology reports generated by ChatGPT, finding that while most radiologists rated them factually correct and complete, instances of errors and potential harm remain.
David S. Wishart
DrugBank is a comprehensive bioinformatics/cheminformatics database combining detailed drug data with drug target information for in silico drug discovery.
Sheng Li, Jung Ho Ahn, Richard Strong, et al.
McPAT is an integrated power, area, and timing modeling framework for multicore/manycore architectures, enabling design space exploration from 90nm to 22nm.
Yuren Mao, Yuhang Ge, Yijiang Fan, et al.
A comprehensive survey categorizing LoRA variants for LLMs into downstream adaptation, cross-task generalization, efficiency, privacy, and applications.
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.
Leslie Pack Kaelbling, Michael L. Littman, Andrew Moore
A comprehensive survey of reinforcement learning from a computer-science perspective, covering exploration-exploitation trade-offs, Markov decision theory, delayed reinforcement, and practical systems.
Unknown
RLVR improves low-k pass@k efficiency but restricts high-k reasoning capacity by reducing exploration of successful paths already present in base models.
Unknown
LongLoRA enables efficient context extension for large language models via sparse local attention and parameter-efficient fine-tuning.
Unknown
QLoRA enables fine-tuning of large language models on a single GPU by combining 4-bit NormalFloat quantization, double quantization, and paged optimizers, matching 16-bit performance.
Unknown
LoRA freezes pretrained weights and injects low-rank trainable matrices into Transformer layers, drastically reducing parameters for fine-tuning.
Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, et al.
Jina Embeddings v3 is a 570M parameter multilingual embedding model supporting 8192-token contexts, task-specific LoRA adapters, and flexible dimension reduction via Matryoshka learning.