Peeling Context from Cause for Multimodal Molecular Property Prediction

Deep models are used for molecular property prediction, yet they are often difficult to interpret and may rely on spurious context rather than causal structure, which reduces reliability under distribution shift and harms predictive performance. We introduce CLaP (Causal Layerwise Peeling), a framework that separates causal signal from context in a layerwise manner and integrates diverse graph representations of molecules. At each layer, a causal block performs a soft split into causal and non-causal branches, fuses causal evidence across modalities, and progressively removes batch-coupled context to focus on label-relevant structure, thereby limiting shortcut signals and stabilizing layerwise refinement. Across four molecular benchmarks, CLaP consistently improves MAE, MSE, and $R^2$ over competitive baselines. The model also produces atom-level causal saliency maps that highlight substructures responsible for predictions, providing actionable guidance for targeted molecular edits. Case studies confirm the accuracy of these maps and their alignment with chemical intuition. By peeling context from cause at every layer, the model yields predictors that are both accurate and interpretable for molecular design.

AIRA: A Low-cost IR-based Approach Towards Autonomous Precision Drone Landing and NLOS Indoor Navigation

Automatic drone landing is an important step for achieving fully autonomous drones. Although there are many works that leverage GPS, video, wireless signals, and active acoustic sensing to perform precise landing, autonomous drone landing remains an unsolved challenge for palm-sized microdrones that may not be able to support the high computational requirements of vision, wireless, or active audio sensing. We propose AIRA, a low-cost infrared light-based platform that targets precise and efficient landing of low-resource microdrones. AIRA consists of an infrared light bulb at the landing station along with an energy efficient hardware photodiode (PD) sensing platform at the bottom of the drone. AIRA costs under 83 USD, while achieving comparable performance to existing vision-based methods at a fraction of the energy cost. AIRA requires only three PDs without any complex pattern recognition models to accurately land the drone, under $10$cm of error, from up to $11.1$ meters away, compared to camera-based methods that require recognizing complex markers using high resolution images with a range of only up to $1.2$ meters from the same height. Moreover, we demonstrate that AIRA can accurately guide drones in low light and partial non line of sight scenarios, which are difficult for traditional vision-based approaches.

RASP: A Drone-based Reconfigurable Actuation and Sensing Platform Towards Ambient Intelligent Systems

Realizing consumer-grade drones that are as useful as robot vacuums throughout our homes or personal smartphones in our daily lives requires drones to sense, actuate, and respond to general scenarios that may arise. Towards this vision, we propose RASP, a modular and reconfigurable sensing and actuation platform that allows drones to autonomously swap onboard sensors and actuators in only 25 seconds, allowing a single drone to quickly adapt to a diverse range of tasks. RASP consists of a mechanical layer to physically swap sensor modules, an electrical layer to maintain power and communication lines to the sensor/actuator, and a software layer to maintain a common interface between the drone and any sensor module in our platform. Leveraging recent advances in large language and visual language models, we further introduce the architecture, implementation, and real-world deployments of a personal assistant system utilizing RASP. We demonstrate that RASP can enable a diverse range of useful tasks in home, office, lab, and other indoor settings.