Large language models display the identifiable victim effect at roughly twice the human baseline, strongly amplified by instruction tuning and chain-of-thought prompting but inverted by reasoning-specialized models.
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ODRPO decomposes discrete rewards into ordinal binary indicators to create robust, variance-aware advantage estimators for noisy RLAIF in LLM alignment.
TaskLens uses LLMs to generate task-specific scaffolded interfaces that reduce perceived workload and improve performance and concept learning for novices using professional 3D software.
Machine learning on task-based EEG outperforms resting-state for ADHD classification, while diffusion and structural MRI link white-matter integrity and grey-matter volume in fronto-parietal regions to effort-reward parameters and subclinical apathy.
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.
citing papers explorer
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ODRPO: Ordinal Decompositions of Discrete Rewards for Robust Policy Optimization
ODRPO decomposes discrete rewards into ordinal binary indicators to create robust, variance-aware advantage estimators for noisy RLAIF in LLM alignment.
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TaskLens: Generating Task-Conditioned Scaffolded Interfaces for Learning Professional Creative Software
TaskLens uses LLMs to generate task-specific scaffolded interfaces that reduce perceived workload and improve performance and concept learning for novices using professional 3D software.
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Machine learning approaches to uncover the neural mechanisms of motivated behaviour: from ADHD to individual differences in effort and reward sensitivity
Machine learning on task-based EEG outperforms resting-state for ADHD classification, while diffusion and structural MRI link white-matter integrity and grey-matter volume in fronto-parietal regions to effort-reward parameters and subclinical apathy.
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Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.