Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-scale datasets exhibit striking representational alignment with human neural responses, learning image-computable models of visual cortex relies on individual-level, large-scale fMRI datasets. The necessity for expensive, time-intensive, and often impractical data acquisition limits the generalizability of encoders to new subjects and stimuli. **BraInCoRL** uses in-context learning to predict voxelwise neural responses from few-shot examples *without any additional finetuning* for novel subjects and stimuli. We leverage a transformer architecture that can flexibly condition on a variable number of in-context image stimuli, learning an inductive bias over multiple subjects. During training, we explicitly optimize the model for in-context learning. By jointly conditioning on image features and voxel activations, our model learns to directly generate better performing voxelwise models of higher visual cortex. We demonstrate that BraInCoRL consistently outperforms existing voxelwise encoder designs in a low-data regime when evaluated on entirely novel images, while also exhibiting strong test-time scaling behavior. The model also generalizes to an entirely new visual fMRI dataset, which uses different subjects and fMRI data acquisition parameters. Further, BraInCoRL facilitates better interpretability of neural signals in higher visual cortex by attending to semantically relevant stimuli. Finally, we show that our framework enables interpretable mappings from natural language queries to voxel selectivity.

Low-count Positron Emission Tomography reconstruction is critical for maintaining high imaging quality while minimizing tracer doses and radiation exposure. Although integrating structural information from CT and MR data has been shown to enhance PET reconstruction, this typically requires simultaneous PET and CT/MRI scans, complicating workflows and increasing radiation exposure. Recent advancements in foundation models offer a promising alternative to in-person CT/MRI imaging, potentially overcoming these limitations. However, the use of foundation models' segmentation masks as semantic guides has been observed to introduce erroneous structures in low-count PET reconstructions. To address this challenge, this work introduces an innovative prompting agent-based framework that dynamically interacts with the foundation model to retrieve and refine priors, minimizing undue influence on the reconstruction process. Specifically, a box agent is designed for single-instance local area information retrieval, while a point agent is introduced to progressively prompt broader semantic structures globally, utilizing history point prompts. Additionally, an MDP paradigm has been developed to address the challenges of utilizing historical point prompts while maintaining the independence required by MDPs. Evaluated on both simulated and real datasets, the proposed method demonstrates superior qualitative and quantitative performance compared to state-of-the-art methods, even those leveraging in-person CT/MRI priors.