university-of-oxford-aa12955c·3 events·first seen Aliases: University of Oxford
FutureHouse, University of Oxford, and Fordham University released Robin, an open-source AI agent that autonomously identifies existing drugs that could treat a given disease by iteratively hypothesizing mechanisms, designing experiments, and ranking drug candidates using literature-search sub-agents. In a demonstration targeting dry age-related macular degeneration, Robin identified two drugs (Y-27632 and Ripasudil) that produced roughly 1.75–2x increases in RPE phagocytosis in human cell experiments. The pipeline uses GPT o4-mini for most language tasks and Claude 3.7 Sonnet for pairwise ranking, with human involvement limited to naming the disease and running lab experiments. The work represents a concrete, experimentally validated instance of agentic AI accelerating drug repurposing research.
A research paper from arXiv evaluates three NLP approaches — Named Entity Recognition, Keyword Extraction, and Topic Modelling — for automating keyword assignment in crowdsourced digital collections, using the University of Oxford's Their Finest Hour Online Archive as a case study. The study spans traditional statistical methods through modern generative AI, finding that no single method is complete and that model choice substantially shapes results. Open-weight, extractive models are recommended for responsible deployment, while generative AI is flagged for accountability risks specific to crowdsourced metadata contexts. The paper also raises stewardship ethics around automated metadata generation when contributors are living participants.
Researchers from MATS, Oxford, and Anthropic introduced the 'assistant axis,' a vector derived from LLM layer outputs that quantifies how closely a model adheres to its trained assistant persona. They developed 'activation capping,' an inference-time method that corrects deviations from this axis when similarity falls below a threshold. Testing on Gemma 2 27B, Qwen3 32B, and Llama 3.3 70B showed harmful response rates to jailbreak prompts dropped by roughly half (e.g., 83% to 41% for Qwen3 32B) without degrading benchmark performance. The technique targets character-based jailbreaks that bypass system prompts by manipulating a model's internal representational state.