Using thousands of AI agents to uncover new leads in biological data
A research overview of distributed agents linking large biological datasets with scientific literature. Proposed connections still require experimental follow-up.
Reasoning · Open weights · Alignment. Selected primary papers, reports, model cards and official technical articles. Checked 2026-10-25. Product announcements are not presented as papers.
A research overview of distributed agents linking large biological datasets with scientific literature. Proposed connections still require experimental follow-up.
Official research discussion of improving coding capabilities in languages with less available training material.
An official account of progress in computer-use agents. Read the original methods and evaluations separately from deployment claims.
An open-source toolkit uses language models to help turn qualitative text and image data into measurable variables.
Open-weight reasoning models are described through their architecture, training, capabilities and safety evaluations.
The report describes GPT-4’s capabilities, evaluation results and limitations while leaving important training details undisclosed.
Human demonstrations and preference feedback are used to improve how a language model follows instructions.
Contrastive training aligns images with natural-language descriptions, enabling transfer to new visual categories.
GPT-3 studies how larger autoregressive language models perform new tasks through examples in their input context.
This index currently holds 9 records attributed to OpenAI (2020–2026). Each entry links to the primary source; product announcements and news posts are not counted as papers.
These records come from other institutions but address the same topics, so the lab can be read in context.