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Human feedback can vary in cost and informativeness. Strong feedback can reveal severe errors but is costly, so cheaper quality ratings can help decide which items to annotate. We propose MICRO (Multi-Fidelity Impact Clustered Rollout), an active search framework that allocates a shared budget to these feedback types t...
This work introduces Self-Contrastive Residual Alignment (SCRA), a one-pass inference-time edit that learns a low-rank linear corrector for the prompt-induced residual shift and slightly but significantly improves over the audio-only baseline on difficult samples.
A deep learning framework is presented that leverages a bidirectional cortical implant to causally shape stimulation-evoked population activity in the human visual cortex and provides a population-level foundation for linking microstimulation, cortical activity, and perception in the human visual system.
A benchmark built on the Speech Accessibility Project (SAP) dataset is introduced that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech, finding that current models do not meaningfully use this context.
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