When a SpaceX rocket failure set the skies aflame over western Europe last February, no-one was sure if the debris was also polluting our atmosphere. Now scientists are directly linking the uncontrolled rocket re-entry to a plume of lithium measured less than 100km above Earth. It is the first time researchers have drawn a direct link between a known piece of space debris crashing to Earth and pollution levels. They warn that as SpaceX chief Elon Musk pledges to launch one million satellites in the coming years, this contamination could be the tip of the iceberg. The scientists were already investigating the problem of pollution from space debris when they realised a SpaceX Falcon 9 had failed in flight.

If you want to put people back on the moon, don't gut the agency in charge of getting them there. The senator wanted a promise. For the last six years--or maybe the last decade or quarter century, depending on how you count it--the United States and China had been locked in a space race, a contest to see which nation could put its people on the moon . Senator Ted Cruz wanted President Donald Trump's nominee to run NASA, Jared Isaacman, to pledge that the US would not lose. Cruz brought a little surprise to Isaacman's confirmation hearing last April. It was a poster of the moon. On one side stood three astronauts and a giant Chinese flag. On the other were two more figures in space suits, with the tiniest Stars and Stripes planted in the lunar soil . Cruz apologized for the imbalance. "My team used ChatGPT," explained the senator, who chairs the committee that oversees NASA. Then Cruz, with a bit more seriousness, asked Isaacman, "Do we have your commitment that you will not allow the scenario on the right of this poster to happen? That China will not beat us to the moon?" Isaacman, a billionaire entrepreneur who had paid for his own missions to space, replied, "Senator, I only see the left-hand portion of that poster."

The billionaire's involvement with the Trump administration and DOGE had deep impacts on Tesla's bottom line. But Elon Musk was still able to turn his attention to SpaceX. What a weird time to be Elon Musk. This year opened with the businessman turned political operator throwing what appeared, to Nazis at least, to be a . This spring, activists frequently congregated outside the showrooms of his automaker, Tesla, to protest his foray into the US federal government and cozy relationship with President Trump.

Kernel-based methods such as Rocket are among the most effective default approaches for univariate time series classification (TSC), yet they do not perform equally well across all datasets. We revisit the long-standing intuition that different representations capture complementary structure and show that selectively fusing them can yield consistent improvements over Rocket on specific, systematically identifiable kinds of datasets. We introduce Fusion-3 (F3), a lightweight framework that adaptively fuses Rocket, Sax, and Sfa representations. To understand when fusion helps, we cluster UCR datasets into six groups using meta-features capturing series length, spectral structure, roughness, and class imbalance, and treat these clusters as interpretable data-structure regimes. Our analysis shows that fusion typically outperforms strong baselines in regimes with structured variability or rich frequency content, while offering diminishing returns in highly irregular or outlier-heavy settings. To support these findings, we combine three complementary analyses: non-parametric paired statistics across datasets, ablation studies isolating the roles of individual representations, and attribution via SHAP to identify which dataset properties predict fusion gains. Sample-level case studies further reveal the underlying mechanism: fusion primarily improves performance by rescuing specific errors, with adaptive increases in frequency-domain weighting precisely where corrections occur. Using 5-fold cross-validation on the 113 UCR datasets, F3 yields small but consistent average improvements over Rocket, supported by frequentist and Bayesian evidence and accompanied by clearly identifiable failure cases. Our results show that selectively applied fusion provides dependable and interpretable extension to strong kernel-based methods, correcting their weaknesses precisely where the data support it.

Like so many things in Elon Musk's orbit, a lot of it may come down to AI. SpaceX is planning to raise tens of billions of dollars through an initial public offering next year, multiple outlets have reported, and Ars can confirm. This represents a major change in thinking from the world's leading space company and its founder, Elon Musk . The Wall Street Journal and The Information first reported about a possible IPO last Friday, and Bloomberg followed that up on Tuesday evening with a report suggesting the company would target a $1.5 trillion valuation. This would allow SpaceX to raise in excess of $30 billion. This is an enormous amount of funding.

Modern work relies on an assortment of digital collaboration tools, yet routine processes continue to suffer from human error and delay. To address this gap, this dissertation extends TheAgentCompany with a finance-focused environment and investigates whether a general purpose LLM agent can complete representative wealth-management tasks both accurately and economically. This study introduces synthetic domain data, enriches colleague simulations, and prototypes an automatic task-generation pipeline. The study aims to create and assess an evaluation set that can meaningfully measure an agent's fitness for assistant-level wealth management work. We construct a benchmark of 12 task-pairs for wealth management assistants spanning retrieval, analysis, and synthesis/communication, with explicit acceptance criteria and deterministic graders. We seeded a set of new finance-specific data and introduced a high vs. low-autonomy variant of every task. The paper concluded that agents are limited less by mathematical reasoning and more so by end-to-end workflow reliability, and meaningfully affected by autonomy level, and that incorrect evaluation of models have hindered benchmarking.

This technical report presents a comprehensive formal verification approach for probabilistic agent systems modeling ballistic rocket flight trajectories using Probabilistic Alternating-Time Temporal Logic (PATL). We describe an innovative verification framework specifically designed for analyzing critical safety properties of ballistic rockets engineered to achieve microgravity conditions for scientific experimentation. Our model integrates authentic flight telemetry data encompassing velocity vectors, pitch angles, attitude parameters, and GPS coordinates to construct probabilistic state transition systems that rigorously account for environmental stochasticity, particularly meteorological variability. We formalize mission-critical safety properties through PATL specifications to systematically identify trajectory deviation states where the rocket risks landing in prohibited or hazardous zones. The verification framework facilitates real-time safety monitoring and enables automated intervention mechanisms, including emergency engine disengagement protocols, when predefined safety thresholds are exceeded. Experimental validation demonstrates the practical effectiveness and reliability of our approach in ensuring mission safety while maintaining scientific mission objectives.

To prove Lemma 5.1, we just need to show that the Pontryagin's Maximum Principle for the auxiliary X, (S.3) and the following matrix trace properties: Tr(A) = Tr( A Since the above obtained PMP equations (S.2) are the same with the differential PMP in (13), we thus Based on Lemma 5.1 and its proof, we known that the PMP of the auxiliary control system, (S.2), is exactly the differential PMP equations (13). From (S.2c), we solve for U Proof by induction: (S.2d) shows that (S.8) holds for This completes the proof. 2 D Algorithms Details for Different Learning Modes SysID Mode, then use the learned dynamics as the initial guess in IRL/IOC Mode. In design of the quadrotor's control objective function, to achieve SE (3) maneuvering In Fig. S1, we show more detailed results of imitation loss versus iteration In Fig. S2, we show more detailed results of SysID loss versus iteration In Fig. S5, we use the On the cart-pole and robot-arm systems (in Figure 1a and Figure 1b), we learn a feedback policy by minimizing given control objective functions. In Fig. S3, we show the detailed results of control loss (i.e. the value of control objective S6, we have the following remarks. This can be seen in Fig. S3 and Fig. S6 (in Fig. S6, PDP results in a simulated trajectory which is closer to the optimal one than that This explains why PDP outperforms GPS in terms of having lower control cost (loss).

This was the last flight of SpaceX's V2 Starship design. Version 3 arrives next year. SpaceX closed a troubled but instructive chapter in its Starship rocket program Monday with a near-perfect test flight that carried the stainless steel spacecraft halfway around the world from South Texas to the Indian Ocean. The rocket's 33 methane-fueled Raptor engines roared to life at 6:23 pm CDT (7:23 pm EDT; 23:23 UTC), throttling up to generate some 16.7 million pounds of thrust, by a large measure more powerful than any rocket before Starship. Moments later, the 404-foot-tall (123-meter) rocket began a vertical climb away from SpaceX's test site in Starbase, Texas, near the US-Mexico border.

Old rocket parts and decommissioned satellites are whizzing around in low Earth orbit, where they risk colliding with the ever-growing constellations of modern satellites being launched. A new listing of the 50 most concerning pieces of space debris in low-Earth orbit is dominated by relics more than a quarter-century old, primarily dead rockets left to hurtle through space at the end of their missions. "The things left before 2000 are still the majority of the problem," said Darren McKnight, lead author of a paper presented Friday at the International Astronautical Congress in Sydney. "Seventy-six percent of the objects in the top 50 were deposited last century, and 88 percent of the objects are rocket bodies. That's important to note, especially with some disturbing trends right now."