Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However, they are predominantly trained on mathematical data and their generalizability to non-mathematical domains has not been rigorously studied. In response, this work first shows that current PRMs have poor performance in other domains. To address this limitation, we introduce ***VersaPRM***, a multi-domain PRM trained on synthetic reasoning data generated using our novel data generation and annotation method. VersaPRM achieves consistent performance gains across diverse domains. For instance, in the MMLU-Pro category of Law, VersaPRM via weighted majority voting, achieves a 7.9% performance gain over the majority voting baseline–surpassing Qwen2.5-Math-PRM's gain of 1.3%. We further contribute to the community by open-sourcing all data, code and models for VersaPRM.

Kirigami Haptic Swatches demonstrate how kirigami and origami based structures enable sophisticated haptic feedback through simple cut-and-fold fabrication techniques. We leverage four types of geometric patterns: rotational erection system (RES), split-fold waterbomb (SFWB), the overlaid structure of SFWB and RES (SFWB+RES), and cylindrical origami, to render different sets of haptic feedback (i.e. linear, bistable, bouncing snap-through, and rotational force behaviors, respectively). In each structure, not only the form factor but also the force feedback properties can be tuned through geometric parameters. We experimentally analyzed and modeled the structures, and implemented software to automatically generate 2D patterns for desired haptic properties. We also demonstrate five example applications including an assistive custom keyboard, rotational switch, multi-sensory toy, task checklist, and phone accessories. We believe the Kirigami Haptic Swatches helps tinkerers, designers, and even researchers to create interactions that enrich our haptic experience.