VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding.In this work, we propose MIRTH, a unified framework designed to address these challenges.MIRTH augments a pretrained VLA backbone with three key innovations: (1) dualscale temporal memory hubs that compress long-term scene evolution and short-term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutualinformation objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput.Extensive evaluations on the LIBERO simulation benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities.The codes and collected datasets are released at http://github.com/kiva12138/mirth.

Arabidopsis is a widely used model plant to study physiology and development. Live imaging is an important technique to visualize and quantify processes in plant growth and cell division, where accurate cell tracking is essential. The commonly used software TrackMate adopts a tracking-by-detection approach, applying Laplacian of Gaussian (LoG) for blob detection and a Linear Assignment Problem (LAP) tracker for tracking. However, its performance declines when cells are densely arranged. To overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA) that incorporates knowledge of Arabidopsis root cellular patterns and spatial relationships among volumes. Our method follows a coarse-to-fine strategy: first performing relatively simple line-level tracking of nuclei, then refining associations based on the linear arrangement of cell files and their spatial relationships. We evaluated the method on long-term live imaging datasets of Arabidopsis root tips, and with minor manual correction, it achieved accurate nuclear tracking. To the best of our knowledge, this represents the first successful attempt to address a long-standing problem in time-lapse microscopy of the root meristem by providing an accurate tracking method for Arabidopsis root nuclei.

Computer-aided automatic cephalometric landmark localization has been a hot topic since last century. Recent proposed deep learning-based methods have made great contributions to this research topic. Among them, convolutional neural networks (CNN)-based regression is widely used, where ground-truth (GT) information is mainly used in the calculation of loss function, thus, mimics the difference between the predicted landmarks ' locations and the ground-truth locations through backpropagation. However, considering the limited number of annotated cephalometric data, we believe the performance can be better improved by better utilizing ground-truth information. In this paper, we propose a teacher-student learning method using GT images for accurate cephalometric detection. We first use images composed with GT landmarks as input images to train a detection model, which is treated as a teacher model. Then the teacher model is used to guide a student model, which is trained by original images, by transferring useful features. We believe the features between GT images and original images have similar domain distribution since they both represent same structure. We validate our method on public grand challenge dataset. Our method achieves better performance compared with state-of-the-art methods.