What happens if pretending to be someone else becomes your entire life? It is a question at the heart of many of the biggest spy dramas, from Slow Horses to Black Doves - and it is one that TV thriller series The Agency explores more deeply than most. Returning for a second season, the Paramount+ thriller follows CIA operatives living under deep-cover identities. It examines not just the dangers of espionage, but the psychological cost of maintaining a lie for years. Starring Michael Fassbender, Richard Gere and Katherine Waterston, the series is based on acclaimed French drama The Bureau.

Planetary exploration using aerial assets has the potential for unprecedented scientific discoveries on Mars. While NASA's Mars helicopter Ingenuity proved flight in Martian atmosphere is possible, future Mars rotocrafts will require advanced navigation capabilities for long-range flights. One such critical capability is Map-based Localization (MbL) which registers an onboard image to a reference map during flight in order to mitigate cumulative drift from visual odometry. However, significant illumination differences between rotocraft observations and a reference map prove challenging for traditional MbL systems, restricting the operational window of the vehicle. In this work, we investigate a new MbL system and propose Geo-LoFTR, a geometry-aided deep learning model for image registration that is more robust under large illumination differences than prior models. The system is supported by a custom simulation framework that uses real orbital maps to produce large amounts of realistic images of the Martian terrain. Comprehensive evaluations show that our proposed system outperforms prior MbL efforts in terms of localization accuracy under significant lighting and scale variations. Furthermore, we demonstrate the validity of our approach across a simulated Martian day.

In this report, we present a series of math-specific large language models: Qwen2.5-Math and Qwen2.5-Math-Instruct-1.5B/7B/72B. The core innovation of the Qwen2.5 series lies in integrating the philosophy of self-improvement throughout the entire pipeline, from pre-training and post-training to inference: (1) During the pre-training phase, Qwen2-Math-Instruct is utilized to generate large-scale, high-quality mathematical data. (2) In the post-training phase, we develop a reward model (RM) by conducting massive sampling from Qwen2-Math-Instruct. This RM is then applied to the iterative evolution of data in supervised fine-tuning (SFT). With a stronger SFT model, it's possible to iteratively train and update the RM, which in turn guides the next round of SFT data iteration. On the final SFT model, we employ the ultimate RM for reinforcement learning, resulting in the Qwen2.5-Math-Instruct. (3) Furthermore, during the inference stage, the RM is used to guide sampling, optimizing the model's performance. Qwen2.5-Math-Instruct supports both Chinese and English, and possess advanced mathematical reasoning capabilities, including Chain-of-Thought (CoT) and Tool-Integrated Reasoning (TIR). We evaluate our models on 10 mathematics datasets in both English and Chinese, such as GSM8K, MATH, GaoKao, AMC23, and AIME24, covering a range of difficulties from grade school level to math competition problems.

Establishing a human settlement on Mars is an incredibly complex engineering problem. The inhospitable nature of the Martian environment requires any habitat to be largely self-sustaining. Beyond mining a few basic minerals and water, the colonizers will be dependent on Earth resupply and replenishment of necessities via technological means, i.e., splitting Martian water into oxygen for breathing and hydrogen for fuel. Beyond the technical and engineering challenges, future colonists will also face psychological and human behavior challenges. Our goal is to better understand the behavioral and psychological interactions of future Martian colonists through an Agent-Based Modeling (ABM simulation) approach. We seek to identify areas of consideration for planning a colony as well as propose a minimum initial population size required to create a stable colony. Accounting for engineering and technological limitations, we draw on research regarding high performing teams in isolated and high stress environments (ex: submarines, Arctic exploration, ISS, war) to include the 4 basic personality types within the ABM. Interactions between agents with different psychological profiles are modeled at the individual level, while global events such as accidents or delays in Earth resupply affect the colony as a whole. From our multiple simulations and scenarios (up to 28 Earth years), we found that an initial population of 22 was the minimum required to maintain a viable colony size over the long run. We also found that the agreeable personality type was the one more likely to survive. We find, contrary to other literature, that the minimum number of people with all personality types that can lead to a sustainable settlement is in the tens and not hundreds.

One recent afternoon, Tina Seeger and Diana Trujillo were showing off a few snaps from their latest trip. "I have a soft spot for rover selfies," Seeger, a twenty-seven-year-old NASA geologist, said. She was screen-sharing a shot of the Perseverance rover posing at the Jezero Crater on Mars, taken April 6th. Jezero (rhymes with "hetero") is just north of the Martian equator. "It's really special, because it used to have this ancient lake environment with rivers flowing into a delta," Seeger, who has wavy hair and was seated outside a coffee shop in Bellingham, Washington, said.

After the runaway success of his first book The Martian, a science-driven thriller about a stranded astronaut which spawned a blockbuster movie starring Matt Damon, Andy Weir tried to do what many science fiction authors before him have attempted. It was going to be called Zhek. This story originally appeared on WIRED UK. "I thought this was going to be my magnum opus," he says. "My epic science fiction saga that everyone is going to know me for. I got about 70,000 words in and I had to abandon it, because it was just not coming together--the characters weren't interesting, the plot was crawling along. It was going to be this massive tome that nobody wanted to read."

In the early nineteen-eighties, when Kazuo Ishiguro was starting out as a novelist, a brief craze called Martian poetry hit our literary planet. It was launched by Craig Raine's poem "A Martian Sends a Postcard Home" (1979). The poem systematically deploys the technique of estrangement or defamiliarization--what the Russian formalist critics called ostranenie--as our bemused Martian wrestles into his comprehension a series of puzzling human habits and gadgets: "Model T is a room with the lock inside-- / a key is turned to free the world / for movement." Or, later in the poem: "In homes, a haunted apparatus sleeps, / that snores when you pick it up." For a few years, alongside the usual helpings of Hughes, Heaney, and Larkin, British schoolchildren learned to launder these witty counterfeits: "Caxtons are mechanical birds with many wings / And some are treasured for their markings-- / they cause the eyes to melt / or the body to shriek without pain. Teachers liked Raine's poem, and ...

There's been plenty of talk lately about future missions to Mars that will carry human crew members. It would be an incredible achievement to successfully (and safely) land a human on the surface of another planet, but it's something that has never even been attempted, much less accomplished. With a steady stream of advancements in AI technology and robotics it's not entirely out of the question that the first "resident" of Mars might actually not be a human at all, but a machine. One inquisitive Twitter user decided to ask Elon Musk what his thoughts were on the first Martian being an intelligent machine rather than a human. The brief exchange was fairly straightforward, with Musk being asked what he thinks the chances are that the first Martian is an AI rather than a human. Not one to waste valuable tweet characters, Musk responded by saying "30%."

Now that the Insight lander has settled in on Mars, scientists are learning more about the spot it's in. With its 7-foot-wide solar panels fully deployed, it has already set a record for the most energy generated in a single day by any lander or rover on Mars at 4,588 wH. Also, the protective covers are coming off of its cameras, which should enable higher-resolution images. It's been a busy few days and now, a new picture of Mars without the camera lens cover. Fortunately, while the lander is tilted at a 4-degree angle inside its impact crater, it's designed to operate at up to a 15-degree tilt.

Our new Martian friend just landed on Earth and is excited to learn about the latest developments in human technology. In this Q&A series, IBM experts explain complicated topics to a Martian (and you). On Mars, the closest thing to a chatbot is NASA's Opportunity Rover. I was curious to find out more about chatbots, so I asked Vickie Dorris, an IBM solutions leader in global digital customer care to tell me about chatbots. Let's get right to it.