Large Language Models (LLMs) have shown remarkable capabilities in text and multimodal processing, yet they fundamentally lack physical awareness--understanding of real-world physical phenomena. In this work, we present ACORN, a framework that teaches LLMs physical awareness through sound, focusing on fundamental physical phenomena like the Doppler effect, multipath effect, and spatial relationships. To overcome data scarcity, ACORN introduce a physics-based simulator combining real-world sound sources with controlled physical channels to generate diverse training data. Using this simulator, we build AQA-PHY, a comprehensive Audio Question-Answer dataset, and propose an audio encoder that processes both magnitude and phase information. By connecting our audio encoder to state-of-the-art LLMs, we demonstrate reasonable results in both simulated and real-world tasks, such as line-of-sight detection, Doppler effect estimation, and Direction-of-Arrival estimation, paving the way for enabling LLMs to understand physical world.

On Monday, the National Highway Traffic Safety Administration opened an investigation into Tesla. The agency claims that there have been 11 incidents since 2018 in which Tesla vehicles struck stationary first responder vehicles attending to the scene of an emergency; there's allegedly been 17 injuries and one fatality as a result. The NHTSA is narrowing in on the company's Autopilot system, noting that the Teslas in these incidents "were all confirmed to have been engaged in either Autopilot or Traffic Aware Cruise Control during the approach to the crashes." The investigation will cover Tesla models Y, X, S, and 3 that were released between 2014 and 2021. Autopilot's difficulties with sensing firetrucks and other emergency vehicles has been a known problem for years, and the feature has also been criticized as encouraging drivers to rely on it as though it is a self-driving system when in fact it is only meant to assist an engaged driver.

Black holes could be harnessed for energy, scientists have said. The claim comes after researchers produced an experiment they claim verified a decades-old theory that such black holes could create energy as a result of "extremely odd physics". Scientists at the University of Glasgow's School of Physics and Astronomy set out to validate Roger Penrose's 1969 work. They used sound waves in an attempt to endorse the "extremely odd physics a half-century after the theory was first proposed". British physicist Mr Penrose theorised that energy could be created by dropping objects such as a rocket into a black hole and splitting the object in two.

--Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods -- (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SW AC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).