# Active Learning

EvoDiff recasts active-learning sample selection as generate-then-retrieve: diffusion sampling in feature space produces synthetic probes, and only the real unlabeled ultrasound images those probes retrieve are sent to experts for annotation. Across 15 datasets and 16,696 images spanning six organ tasks, it beat random sampling and several active-learning baselines in most settings on both ResNet-50 and the USFM foundation model, and a model-based annotation-effort metric estimated a 24% to 84% workload reduction relative to random sampling.

EvoDiff recasts active-learning sample selection as generate-then-retrieve: diffusion sampling in feature space produces synthetic probes, and only the real unlabeled ultrasound images those probes retrieve are sent to experts for annotation. Across 15 datasets and 16,696 images spanning six organ tasks, it beat random sampling and several active-learning baselines in most settings on both ResNet-50 and the USFM foundation model, and a model-based annotation-effort metric estimated a 24% to 84% workload reduction relative to random sampling.

Late-stage functionalization in medicinal chemistry is limited by scarce reaction data, especially when several similar C–H bonds compete for borylation. A Roche and ETH team used an XGBoost-ensemble active learning loop to pick 50 previously unreported substrates from a 22,253-molecule pool across three rounds of high-throughput experiments, expanding C–H borylation data to 6,865 reactions over 568 substrates. Geometric graph neural networks trained on that set beat XGBoost in most cases on random, Butina and Bemis–Murcko scaffold splits, and in a prospective test on ten unseen substrates with N-heteroaryl motifs the top predicted site matched experiment in all cases.

Late-stage functionalization in medicinal chemistry is limited by scarce reaction data, especially when several similar C–H bonds compete for borylation. A Roche and ETH team used an XGBoost-ensemble active learning loop to pick 50 previously unreported substrates from a 22,253-molecule pool across three rounds of high-throughput experiments, expanding C–H borylation data to 6,865 reactions over 568 substrates. Geometric graph neural networks trained on that set beat XGBoost in most cases on random, Butina and Bemis–Murcko scaffold splits, and in a prospective test on ten unseen substrates with N-heteroaryl motifs the top predicted site matched experiment in all cases.

UCB and Cradle announced a strategic collaboration that puts Cradle's AI-driven protein design platform inside selected UCB antibody discovery projects, spanning hit generation, lead optimization and preclinical readiness. UCB's digital antibody engineering program had relied on its own antibody discovery platform combined with computational science and a network of technology and research partners. Under the collaboration, experimental results feed back into program-specific models in a compounding "lab-in-the-loop" cycle used to optimize candidates across the full set of required properties.

UCB and Cradle announced a strategic collaboration that puts Cradle's AI-driven protein design platform inside selected UCB antibody discovery projects, spanning hit generation, lead optimization and preclinical readiness. UCB's digital antibody engineering program had relied on its own antibody discovery platform combined with computational science and a network of technology and research partners. Under the collaboration, experimental results feed back into program-specific models in a compounding "lab-in-the-loop" cycle used to optimize candidates across the full set of required properties.

Boundaries of subjective categories such as "healthy food" shift with culture and person, yet conventional workflows demand stable labels from the start. Google Research's Agile Deliberation splits classifier building into concept scoping and concept iteration: users break a broad concept into sub-categories and explicit exclusions, then a vision-language model surfaces semantically borderline images for the user to judge, with feedback routed into an automatic prompt-optimization loop. Across 18 live 1.5-hour sessions, classifiers built this way substantially outperformed the compared standard baselines on the most challenging subjective images, and participants reported less fatigue and anxiety.

Boundaries of subjective categories such as "healthy food" shift with culture and person, yet conventional workflows demand stable labels from the start. Google Research's Agile Deliberation splits classifier building into concept scoping and concept iteration: users break a broad concept into sub-categories and explicit exclusions, then a vision-language model surfaces semantically borderline images for the user to judge, with feedback routed into an automatic prompt-optimization loop. Across 18 live 1.5-hour sessions, classifiers built this way substantially outperformed the compared standard baselines on the most challenging subjective images, and participants reported less fatigue and anxiety.

Volumetric spatial transcriptomics is expensive, and fixed-grid sampling measures redundant spots while missing informative regions in 3D tissue. STRATA co-optimizes sampling and prediction under a budget: on a 3D graph combining H&E morphology with single-cell priors, a reinforcement-learning agent picks morphologically sparse, transcriptomically informative spots, and a predictor fuses retrieval, regression and cross-layer imputation with per-spot weights. Across HER2, Breast, cSCC and a private Kidney cohort, STRATA has the highest PCC and lowest MSE; on HER2 its PCC is 0.361 and MSE 0.356, while the strongest-baseline figures stated in the text disagree with the table's baseline values, so that comparison is not quoted here.

Volumetric spatial transcriptomics is expensive, and fixed-grid sampling measures redundant spots while missing informative regions in 3D tissue. STRATA co-optimizes sampling and prediction under a budget: on a 3D graph combining H&E morphology with single-cell priors, a reinforcement-learning agent picks morphologically sparse, transcriptomically informative spots, and a predictor fuses retrieval, regression and cross-layer imputation with per-spot weights. Across HER2, Breast, cSCC and a private Kidney cohort, STRATA has the highest PCC and lowest MSE; on HER2 its PCC is 0.361 and MSE 0.356, while the strongest-baseline figures stated in the text disagree with the table's baseline values, so that comparison is not quoted here.

Active learning for materials and molecular property prediction treats high-uncertainty samples as the information a model is missing. Across formation energy, bandgap and solubility tasks, uncertainty came from loss-landscape sampling and from ensembles retrained with different seeds, then was calibrated with linear calibration factors and a neural network. Calibrated uncertainty generally did not reduce the data a model needed to improve on out-of-distribution data, showing no advantage over random sampling or uncalibrated estimates, and error bars overlapped.

Active learning for materials and molecular property prediction treats high-uncertainty samples as the information a model is missing. Across formation energy, bandgap and solubility tasks, uncertainty came from loss-landscape sampling and from ensembles retrained with different seeds, then was calibrated with linear calibration factors and a neural network. Calibrated uncertainty generally did not reduce the data a model needed to improve on out-of-distribution data, showing no advantage over random sampling or uncalibrated estimates, and error bars overlapped.

Optimizing biomass torrefaction experimentally is limited by cost and time, since one-factor-at-a-time trials miss synergistic effects and full factorial designs quickly become prohibitive. Using 486 torrefaction observations, Gaussian process regression with automatic relevance determination kernels reached cross-validated R² of 0.854 for mass yield, 0.971 for higher heating value and 0.869 for energy yield, outperforming random forest, support vector regression and a neural network. An acquisition function weighting uncertainty, mass-yield improvement and HHV improvement at 0.4/0.3/0.3, plus a diversity penalty, then recommended 10 experiments spanning 200–315 °C and 30–1089 s, all inside the conventional torrefaction window.

Optimizing biomass torrefaction experimentally is limited by cost and time, since one-factor-at-a-time trials miss synergistic effects and full factorial designs quickly become prohibitive. Using 486 torrefaction observations, Gaussian process regression with automatic relevance determination kernels reached cross-validated R² of 0.854 for mass yield, 0.971 for higher heating value and 0.869 for energy yield, outperforming random forest, support vector regression and a neural network. An acquisition function weighting uncertainty, mass-yield improvement and HHV improvement at 0.4/0.3/0.3, plus a diversity penalty, then recommended 10 experiments spanning 200–315 °C and 30–1089 s, all inside the conventional torrefaction window.