Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment andperforming reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.

Science Space Solar System Mars NASA's Mars Reconnaissance Orbiter snaps 100,000th image A high school student suggested the steep sand dunes of Syrtis Major for the milestone image. Breakthroughs, discoveries, and DIY tips sent every weekday. NASA's Mars Reconnaissance Orbiter (MRO) officially went into service above the Red Planet in November 2006. The spacecraft has since spent nearly 20 years circling Earth's closest neighbor, studying its geology and identifying icy evidence of a once watery world . After already sending back more than 450 terabits of data over the course of its ongoing mission, the orbiter recently passed a major milestone: its 100,000th image of the Martian surface.

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.

America's great journey in space is about to face its most consequential moment in half a century. Everyone agrees: It's a complete disaster. I. Artemis, We Have a Problem As you may have heard, NASA plans to send a crew of astronauts around the moon in early 2026, followed by a lunar landing in 2027. Or maybe you haven't heard. When I told one of my daughters about this plan to send people to the moon, she said, after a long silence: "But I thought we already sent a bunch of people there a long time ago." This is a standard response when I quiz people about Artemis, NASA's program to return to the moon, and this time to stay . It's named for Apollo's twin sister and the goddess of the moon and the hunt. The other day, I was in a gaggle with six neighbors, all highly informed professional people--two of them with long careers at the National Science Foundation--and none knew anything about Artemis except one thing: It's a plan to send people to Mars. Artemis is a moon mission. There is no Mars mission NASA has no Mars rocket, no Mars capsule, no Mars mission crew. What it does have is a very troubled moon program. Artemis faces fundamental engineering challenges that have called into question the program's basic architecture. Reconfiguring a mission this important is hard in the best of times, but the agency is being forced to do it during a year of unprecedented internal turmoil. A new administration always means turnover, but NASA has been in an uncontrolled spin every bit as alarming as the one Neil Armstrong famously pulled out of during in 1966. More than a year ago, President-elect Donald Trump nominated a billionaire entrepreneur and Elon Musk ally, Jared Isaacman, to become NASA administrator. It was an unconventional choice, but Isaacman drew support from many quarters in the space community. Then, right before Isaacman was poised for confirmation by the Senate, Trump and Musk had a nasty falling-out, and Trump yanked Isaacman's nomination. Since Inauguration Day, NASA had been run by acting administrator Janet Petro, a veteran agency official, and with Isaacman out, she remained in charge until one day in July when Trump suddenly named Secretary of Transportation Sean Duffy as interim administrator.

With the increasing complexity of black-box machine learning models and their adoption in high-stakes areas, it is critical to provide explanations for their predictions. Local Interpretable Model-agnostic Explanation (LIME) is a widely used technique that explains the prediction of any classifier by learning an interpretable model locally around the predicted instance. However, it assumes that the local decision boundary is linear and fails to capture the non-linear relationships, leading to incorrect explanations. In this paper, we propose a novel method that can generate high-fidelity explanations. Multivariate adaptive regression splines (MARS) is used to model non-linear local boundaries that effectively captures the underlying behavior of the reference model, thereby enhancing the local fidelity of the explanation. Additionally, we utilize the N-ball sampling technique, which samples directly from the desired distribution instead of reweighting samples as done in LIME, further improving the faithfulness score. We evaluate our method on three UCI datasets across different classifiers and varying kernel widths. Experimental results show that our method yields more faithful explanations compared to baselines, achieving an average reduction of 37% in root mean square error, significantly improving local fidelity.

Reinforcement Learning (RL) has shown significant promise in automated portfolio management; however, effectively balancing risk and return remains a central challenge, as many models fail to adapt to dynamically changing market conditions. We propose Meta-controlled Agents for a Risk-aware System (MARS), a novel framework addressing this through a multi-agent, risk-aware approach. MARS replaces monolithic models with a Heterogeneous Agent Ensemble, where each agent's unique risk profile is enforced by a Safety-Critic network to span behaviors from capital preservation to aggressive growth. A high-level Meta-Adaptive Controller (MAC) dynamically orchestrates this ensemble, shifting reliance between conservative and aggressive agents to minimize drawdown during downturns while seizing opportunities in bull markets. This two-tiered structure leverages behavioral diversity rather than explicit feature engineering to ensure a disciplined portfolio robust across market regimes. Experiments on major international indexes confirm that our framework significantly reduces maximum drawdown and volatility while maintaining competitive returns.

Physicists finally know how much faster time moves on the Red Planet. Breakthroughs, discoveries, and DIY tips sent every weekday. Tracking the first astronauts' visit to Mars won't be as simple as watching a clock or marking days off of a calendar. Thanks to relativity, time actually moves faster on the Red Planet than it does here on Earth. For years, scientists have wondered about the exact temporal difference between planets, but physicists at the National Institute of Standards and Technology (NIST) finally have an answer.

The following lemma given in [2] is useful for the proof of Theorem 1. Lemma 1. [2] Given a stochastic matrix H = 0 0 0 h The following propositions are used to prove this theorem. In this case, there is not enough observations to achieve an upper confidence bound using Proposition 2. The randomized UCB for this case has also an exact confidence as illustrated below: Pr{UCB In the second equality, the boundedness of the means of the arms and Proposition 1 were utilized. The steps in this proof closely follows the proof of Theorem 7.1 in [3]. Let us define a'good' event as G We are going to show 1. The next step is to bound the probability of the second set in (3).