Itiswellknownthatmultiple objectsinnatural scenes compete forneural resources and attentional guidance helps to resolve the ensuing competition [5]. Due to the limited information processing capacity ofthevisual system, neural activity isbiased infavorofthe attended location [6,7].

Adaptive testing and sequential estimation problems have recently gained substantial attention due to their foundational role in modern artificial intelligence and interactive systems. Prominent applications include online preference learning, where systems dynamically adapt to user feedback to refine personalized recommendations, and reinforcement learning from human feedback (RLHF), which aims to align AI agents with human values by adaptively querying users. In these contexts, the main focus is to efficiently extract maximal information from human responses, which are inherently stochastic and limited in quantity. Among various types of such problems, this work particularly considers a fundamental yet illustrative case involving stochastic binary responses. Here, a decision-maker sequentially selects questions of varying difficulty from a continuous pool to pose to a candidate and aims to efficiently estimate the candidate's ability (represented by an unknown continuous parameter) by utilizing the binary feedback (e.g., correct/incorrect) collected, which depends probabilistically on the candidate's ability and the question's difficulty. This setup is arguably the simplest scenario that captures the essence of continuous parameter estimation under uncertainty, making it an ideal benchmark for developing fundamental theoretical insights and practical algorithms. Variants of this fundamental adaptive estimation problem have been studied in several communities.

Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.

The Hessian is ubiquitous in applied mathematics, statistics, and machine learning (ML).

Mean correlation values across the synchronous, (i.e., stimulus-driven) cortex defined at a range of synchrony thresholds ([0.15,0.75]). We employed the HCP MMP parcellation for all ROI-level analysis. Figure 3 depicts the center-weighted saliency map used in all center-weighted attention models. Thus, the output is a 160900-D vector corresponding to the fMRI response. FDR corrected) for each method are colored on the surface.

Visual perception is critically influenced by the focus of attention.

Aligning LLMs with user preferences is crucial for real-world use but often requires costly fine-tuning or expensive inference, forcing trade-offs between alignment quality and computational cost. Existing inference-time methods typically ignore this balance, focusing solely on the optimized policy's performance. We propose HIA (Heuristic-Guided Inference-time Alignment), a tuning-free, black-box-compatible approach that uses a lightweight prompt optimizer, heuristic reward models, and two-stage filtering to reduce inference calls while preserving alignment quality. On real-world prompt datasets, HelpSteer and ComPRed, HIA outperforms best-of-N sampling, beam search, and greedy search baselines in multi-objective, goal-conditioned tasks under the same inference budget. We also find that HIA is effective under low-inference budgets with as little as one or two response queries, offering a practical solution for scalable, personalized LLM deployment.

Conformal inference seeks rigorous uncertainty quantification for the predictions of any black-box machine learning model, without requiring parametric assumptions (Vovk et al., 2005). In classification, these methods aim to construct a prediction set for the label of a new test point while guaranteeing a specified coverage level. The split-conformal approach achieves this by leveraging residuals (or non-conformity scores) from a pre-trained model applied to an independent calibration data set, assuming exchangeability with the test data. Perfect exchangeability, however, may not always hold in practice, due for example to possible distribution shifts between the available data and the future test points of interest, creating a need to relax the assumptions underlying conformal inference (Barber et al., 2023).