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Evidence for fluvial features and standing liquid water indicate that Mars was a warmer and wetter place in its past; however, climate models have historically been unable to produce conditions to yield a warm early Mars under the faint young sun. Some models invoke thick greenhouse atmospheres to produce continuously warm conditions, but others have argued that available geologic evidence is more consistent with short-duration and transient warming events on an otherwise cold Mars. One possibility of harmonizing these perspectives is that early Mars experienced climate limit cycles that caused the climate to oscillate between short periods of warmth and prolonged periods of glaciation, due to modulation of greenhouse warming by the carbonate-silicate cycle. This study suggests that episodic limit cycling during the Noachian and Hesperian periods provides a hypothetical explanation for the timing and formation of fluvial features on Mars. A schematic time-forward trajectory of the full history of Mars is calculated using an energy balance climate model, which includes an active carbonate-silicate cycle, instellation changes due to the sun's main sequence evolution, variations in th

Long-term records of the Martian atmosphere based on general circulation models and reanalysis of atmospheric state variables are important to understand the diurnal, seasonal, and climatological changes of the planet. Atmospheric dynamics of the Martian atmosphere are strongly influenced by the characterization of dust lifting, solar insolation, and spatial variations in topography. We present ARCO-Mars, a unified Analysis-Ready Cloud-Optimized dataset providing integrated access to three independent Mars atmospheric reanalysis products: EMARS, MACDA, and OpenMARS spanning over Mars Years 24-35. These reanalyses assimilate thermal infrared retrievals from the MGS/TES, ODY/THEMIS, and MRO/MCS instruments, providing both two and three-dimensional surface and atmospheric state variables, including temperature, winds, surface pressure, and dust optical depth. The dataset is stored in Zarr v3 format and hosted on HuggingFace, enabling efficient cloud-based access without requiring local storage of the full archive. We compare the state variables between the three reanalysis products to identify systematic differences, attributed to differences in data assimilation and general circulati

The distribution and origin of serpentine on Mars can provide insights into the planet's aqueous history, habitability, and past climate. In this study, we used dynamic aperture factor analysis/target transformation applied to 15,760 images from the Compact Reconnaissance Imaging Spectrometer for Mars, followed by validation with the radiance ratio method, to construct a map of Mg-serpentine deposits on Mars. Although relatively rare, Mg-serpentine was detected in diverse geomorphic settings across Noachian and Hesperian-aged terrains in the southern highlands of Mars, implying that serpentinization was active on early Mars and that multiple formation mechanisms may be needed to explain its spatial distribution. We also calculated the amount of H2 produced during the formation of the observed deposits and conclude that serpentinization was likely more widespread on Mars than indicated by the observed distribution.

The momentum of human spaceflight initiatives continues to build toward Mars, and technological advances may eventually enable the potential for permanent space settlement. Aspirations for sustaining human life in space must be predicated on human factors, rather than technological constraints alone, and advances in models of governance and ethics are necessary as human civilization becomes a spacefaring species. This paper presents an idealistic but feasible model for economic freedom on Mars, which is situated within a framework in which Mars has been designated as a sovereign juridical peer to Earth. Under such conditions, Mars could maintain monetary stability through full reserve banking and a restriction on exchange with any fractional reserve Earth currencies, with a volume of circulating currency that changes based on the total population within fixed capacity infrastructure. Mars could maintain long-term political stability by diffusing the ownership of capital on Mars, which would allow all citizens of Mars to draw sufficient wealth from a combination of capital ownership and labor to live a good life. This model could also support limited tourism on Mars, in which real g

Foundation models have enabled rapid progress across many specialized domains by leveraging large-scale pre-training on unlabeled data, demonstrating strong generalization to a variety of downstream tasks. While such models have gained significant attention in fields like Earth Observation, their application to Mars science remains limited. A key enabler of progress in other domains has been the availability of standardized benchmarks that support systematic evaluation. In contrast, Mars science lacks such benchmarks and standardized evaluation frameworks, which have limited progress toward developing foundation models for Martian tasks. To address this gap, we introduce Mars-Bench, the first benchmark designed to systematically evaluate models across a broad range of Mars-related tasks using both orbital and surface imagery. Mars-Bench comprises 20 datasets spanning classification, segmentation, and object detection, focused on key geologic features such as craters, cones, boulders, and frost. We provide standardized, ready-to-use datasets and baseline evaluations using models pre-trained on natural images, Earth satellite data, and state-of-the-art vision-language models. Results

Mars provides a critical analog to once habitable exoplanets that have since lost their surface liquid water. The current atmospheric state of Mars retains the chemical fingerprints of that transition, including isotopic signatures of atmospheric escape and climate evolution. As the closest accessible example of a terrestrial world with definitive evidence for once supporting liquid water on its surface, Mars presents a unique opportunity to test hypotheses about planetary habitability and atmospheric evolution in a spatially and temporally resolved way.

Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation. However, imbalanced training dynamics across modalities often lead to suboptimal accuracy due to negative interference, a challenge typically addressed with inefficient heuristic methods such as manually tuning separate learning rates. To overcome this, we introduce MARS (Multimodal Adaptive Rank Search), an approach to discover optimal rank pairs that balance training dynamics while maximizing performance. Our key innovation, a proposed framework of dual scaling laws, enables this search: one law models module-specific convergence time to prune the search space to candidates with aligned dynamics, while the other predicts final task performance to select the optimal pair from the pruned set. By re-purposing the LoRA rank as a controller for modality-specific convergence speed, MARS outperforms baseline methods and provides a robust, automated strategy for optimizing MLLM fine-tuning.

Recent advancements in multimodal large language models and vision-languageaction models have significantly driven progress in Embodied AI. As the field transitions toward more complex task scenarios, multi-agent system frameworks are becoming essential for achieving scalable, efficient, and collaborative solutions. This shift is fueled by three primary factors: increasing agent capabilities, enhancing system efficiency through task delegation, and enabling advanced human-agent interactions. To address the challenges posed by multi-agent collaboration, we propose the Multi-Agent Robotic System (MARS) Challenge, held at the NeurIPS 2025 Workshop on SpaVLE. The competition focuses on two critical areas: planning and control, where participants explore multi-agent embodied planning using vision-language models (VLMs) to coordinate tasks and policy execution to perform robotic manipulation in dynamic environments. By evaluating solutions submitted by participants, the challenge provides valuable insights into the design and coordination of embodied multi-agent systems, contributing to the future development of advanced collaborative AI systems.

Human landing, exploration and settlement on Mars will require local compute resources at the Mars edge. Landing such resources on Mars is an expensive endeavor. Instead, in this paper we lay out how concepts from low-Earth orbit edge computing may be applied to Mars edge computing. This could lower launching costs of compute resources for Mars while also providing Mars-wide networking and compute coverage. We propose a possible Mars compute constellation, discuss applications, analyze feasibility, and raise research questions for future work.

For the first time, the Emirates Mars Infrared Spectrometer (EMIRS) instrument on board the Emirates Mars Mission (EMM) "Hope", is providing us with the temperature measurements of Mars at all local times covering most of the planet. As a result, it is now possible to compare surface temperature measurements made from orbit with those from the surface by rovers during the same time period. We use data of diurnal temperature variation from the Rover Environmental Monitoring Station (REMS) suite on board the Mars Science Laboratory (MSL) "Curiosity" rover, and the Mars Environmental Dynamics Analyzer (MEDA) suite on board the Mars 2020 "Perseverance" rover, between June and August 2021 and compare them with EMIRS observations and estimates of the Mars Climate Database (MCD) model. We show that although the overall trend of temperature variation is in excellent agreement across missions, EMIRS measurements are systematically lower at night compared to Mars 2020. The lower spatial resolution of EMIRS compared to the rovers and consequently lower average thermal inertia of the observed regions in this particular case primarily contributed to this discrepancy, among other factors. We dis

Risk to human astronauts and interplanetary distance causing slow and limited communication drives scientists to pursue an autonomous approach to exploring distant planets, such as Mars. A portion of exploration of Mars has been conducted through the autonomous collection and analysis of Martian data by spacecraft such as the Mars rovers and the Mars Express Orbiter. The autonomy used on these Mars exploration spacecraft and on Earth to analyze data collected by these vehicles mainly consist of machine learning, a field of artificial intelligence where algorithms collect data and self-improve with the data. Additional applications of machine learning techniques for Mars exploration have potential to resolve communication limitations and human risks of interplanetary exploration. In addition, analyzing Mars data with machine learning has the potential to provide a greater understanding of Mars in numerous domains such as its climate, atmosphere, and potential future habitation. To explore further utilizations of machine learning techniques for Mars exploration, this paper will first summarize the general features and phenomena of Mars to provide a general overview of the planet, ela

In the distant future, humans will surely fly to Mars. Even today, probes and vehicles are carrying out measurements on the surface of Mars. However, most landings on Mars cannot be carried out with parachutes alone. Instead, landing modules are deployed that use engines to reduce the speed to acceptable values. Compared to Earth, Mars exerts a lower force of attraction on the probes. However, its atmosphere is much thinner than that of Earth. With simple calculations and some simplifying assumptions, students calculate the speed of a Mars probe with a parachute. This allows them to realise that, due to the thin Mars atmosphere, the final speed does not permit a soft landing. Additional materials at: https://www.haus-der-astronomie.de/raum-fuer-bildung ----- In ferner Zukunft werden sicher Menschen zum Mars fliegen. Schon heute führen Sonden und Fahrzeuge auf der Marsoberfläche Messungen durch. Landungen können auf dem Mars jedoch meistens nicht alleine mit Fallschirmen durchgeführt werden. Stattdessen verwendet man Landemodule, die mit Triebwerken die Geschwindigkeit auf akzeptable Werte verringern. Im Vergleich zur Erde übt der Mars zwar eine geringere Anziehungskraft auf die Son

One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet. To achieve this, Mars exploration has been focusing on regions that may have liquid or frozen water. A set of critical areas may have seen cycles of ice thawing in the relatively recent past in response to periodic changes in the obliquity of Mars. In this work, we use convolutional neural networks to detect surface regions containing "Brain Coral" terrain, a landform on Mars whose similarity in morphology and scale to sorted stone circles on Earth suggests that it may have formed as a consequence of freeze/thaw cycles. We use large images (~100-1000 megapixels) from the Mars Reconnaissance Orbiter to search for these landforms at resolutions close to a few tens of centimeters per pixel (~25--50 cm). Over 52,000 images (~28 TB) were searched (~5% of the Martian surface) where we found detections in over 200 images. To expedite the processing we leverage a classifier network (prior to segmentation) in the Fourier domain that can take advantage of JPEG compression by leveraging blocks of coefficients from a discrete cosine transform in lieu of decoding the entir

Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous planning and safe driving. However, there is a lack of sufficient detailed and high-confidence data annotations, which are exactly required by most deep learning methods to obtain a good model. To address this problem, we propose our solution from the perspective of joint data and method design. We first present a newdataset S5Mars for Semi-SuperviSed learning on Mars Semantic Segmentation, which contains 6K high-resolution images and is sparsely annotated based on confidence, ensuring the high quality of labels. Then to learn from this sparse data, we propose a semi-supervised learning (SSL) framework for Mars image semantic segmentation, to learn representations from limited labeled data. Different from the existing SSL methods which are mostly targeted at the Earth image data, our method takes into account Mars data characteristics. Specifically, we first investigate the impact of current widely used natural image augmentations on Mars images. Based on the analysis, we then proposed two novel and effective au

Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, since interpretability is broadly defined, there are no clear guidelines for assessing the biological plausibility of model interpretations. To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowledge graph (KG), MoA-net. We then develop the MoA Retrieval System (MARS), a NeSy approach for drug discovery which leverages logical rules with learned rule weights. Using this interpretable feature alongside domain knowledge, we find that MARS and other NeSy approaches on KGs are susceptible to reasoning shortcuts, in which the prediction of true labels is driven by "degree-bias" rather than the domain-based rules. Subsequently, we demonstrate ways to identify and mitigate this. Thereafter, MARS achieves performance on par with current state-of-the-art models while

Mars has surely been scrutinised since the dawn of humankind. In the 16th century Tycho Brahe made accurate observations of the position of Mars that enabled Johannes Kepler to obtain his first two laws of planetary motion. In the 17th century the first telescope observations were made, but very little surface detail could be discerned. Throughout the 18th and 19th centuries telescopes improved, revealing many dark areas on the red tinted surface. After the close opposition of 1877 Giovanni Schiaparelli announced about 40 canali on Mars. This led to the saga of the canals of Mars, laid to rest in 1971 when Mariner 9 made observations from Martian orbit showing that the canali/canals of Mars do not exist. Belief that there was life on Mars was widespread in the 19th century, including the view that the dark areas were some form of plant life. This view persisted until Mariner 4 flew past Mars in 1965 and discovered a far thinner atmosphere than previously thought, with impact craters dominating the images. It was Mariner 9 that revealed much more promising landscapes. Thus, the contemporary era of Mars exploration began. Our picture of Mars today is not only much more complete that

The multivariate adaptive regression spline (MARS) is one of the popular estimation methods for nonparametric multivariate regressions. However, as MARS is based on marginal splines, to incorporate interactions of covariates, products of the marginal splines must be used, which leads to an unmanageable number of basis functions when the order of interaction is high and results in low estimation efficiency. In this paper, we improve the performance of MARS by using linear combinations of the covariates which achieve sufficient dimension reduction. The special basis functions of MARS facilitate calculation of gradients of the regression function, and estimation of the linear combinations is obtained via eigen-analysis of the outer-product of the gradients. Under some technical conditions, the asymptotic theory is established for the proposed estimation method. Numerical studies including both simulation and empirical applications show its effectiveness in dimension reduction and improvement over MARS and other commonly-used nonparametric methods in regression estimation and prediction.

Robotic crop phenotyping has emerged as a key technology to assess crops' morphological and physiological traits at scale. These phenotypical measurements are essential for developing new crop varieties with the aim of increasing productivity and dealing with environmental challenges such as climate change. However, developing and deploying crop phenotyping robots face many challenges such as complex and variable crop shapes that complicate robotic object detection, dynamic and unstructured environments that baffle robotic control, and real-time computing and managing big data that challenge robotic hardware/software. This work specifically tackles the first challenge by proposing a novel Digital-Twin(DT)MARS-CycleGAN model for image augmentation to improve our Modular Agricultural Robotic System (MARS)'s crop object detection from complex and variable backgrounds. Our core idea is that in addition to the cycle consistency losses in the CycleGAN model, we designed and enforced a new DT-MARS loss in the deep learning model to penalize the inconsistency between real crop images captured by MARS and synthesized images sensed by DT MARS. Therefore, the generated synthesized crop images

A technologically mature colony on Mars can produce and deliver at least 1 million tons of liquid hydrogen per year to one or more propellant depots at Low Earth Orbit (LEO). Production of 1 $kg$ of hydrogen at Marian colony and its delivery to LEO requires an energy expenditure of 1.4 $GJ$ on Mars. LEO propellant depot contains hydrogen produced on Mars and oxygen produced on the Moon or near-Earth asteroids. This propellant is used to deliver payload from LEO to many destinations in the Solar System including Mars. Delivery of 1 $kg$ payload from LEO to Mars requires an energy expenditure of 3.5 $GJ$ on Mars, Moon, and near-Earth asteroids. The use of liquid hydrogen produced on Mars to deliver astronauts and payload to Mars ensures exponential bootstrap growth of the Martian colony. Martian Colony and delivery of millions of tons of liquid hydrogen to LEO is the key to Colonization of Solar System. %% Martian Colony starts transporting liquid hydrogen to LEO only after it grows to significant size. It should contain about 20 million tons of steel and 3 million tons of plastic in structures and material as well as several thousand astronauts. Prior to that time, LEO hydrogen depo