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cs.LG2026
Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery
Gilles Wainrib, Barbara Bodinier, Haitem Dakhli +5
Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with prior studies suggesting that…
cs.LG2025
OwkinZero: Accelerating Biological Discovery with AI
Nathan Bigaud, Vincent Cabeli, Meltem Gürel +4
While large language models (LLMs) are rapidly advancing scientific research, they continue to struggle with core biological reasoning tasks essential for translational and biomedi…
cs.LG2025
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Arthur Pignet, Chiara Regniez, John Klein
Despite the increasing demand for safer machine learning practices, the use of Uncertainty Quantification (UQ) methods in production remains limited. This limitation is exacerbated…