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Deep learning approach using Earth observation embeddings and meteorological data to predict flood peak depths from spaceborne terrain without relying on post-event HWM surveys.
OpenAI releases GPT-6.1 Terra, emphasizing visual reasoning and computer use capabilities for workplace tasks like form-filling and video editing.
OpenAI releases GPT-6.1 Terra, a model optimized for fast, capable computer use and task automation.
TerraNostra-Bench evaluates land-use reconstruction at 1-km resolution via semantic segmentation, separating spatial downscaling from temporal rollout.
Relevance-filtered context reduces conversation history to 5% text while maintaining accuracy with GPT-5.6-terra and Claude Opus 5.5 as judges.
Geospatial foundation models (TerraMind, Prithvi-EO-2.0, DOFA, DINOv3) show significant accuracy drops under held-out disaster evaluation, revealing shortcut learning in damage assessment.
Multi-objective RL policy (PAMORT) enables quadrupedal robots to transport unsecured stacked payloads over uneven terrain.
OpenAI has spent the last few years planting flags across the increasingly difficult terrain in mathematics. This week, it claimed one of its biggest prizes yet: a solution to a legendary Millennium Prize problem. In normal circumstances, this would have been celebrated as a historic achievement. Instead, many mathematicians have watched OpenAI's relentless advance with growing unease. To them, the company appears less like an enthusiastic newcomer than an impossibly well-resourced interloper, charging into problems they have dedicated their lives to studying with little apparent regard for l...
I got access to GPT-6 Astra this afternoon, so naturally I used it to generate SVGs of pelicans riding bicycles - at low, medium, high, xhigh and max reasoning levels (Astra doesn't support reasoning=none). Then I rendered those pelicans in a comparison grid with GPT-5.6 Sol, Terra, and Luna, and beyond being fun the result was surprisingly useful. See the grid for full quality images. Here's the transcript that created the GPT-6 Nova pelicans. There are a few interesting things that stand out from this grid. The Astra pelicans are much better . The very best GPT-5.6-Sol pelican (I liked xhig...
TerraPower's nuclear power plant possesses a strategic advantage over competitors, especially when chasing after data center deals.
Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain. Yet, pixel-level annotation of glacier surfaces is costly and requires domain experts. We introduce CrevasseSeg, a framework for binary segmentation over the terminus of Borebreen, Svalbard, comprising 1,938 unlabelled UAV orthomosaic tiles for self-supervised/unsupervised fine-tuning, 24 labelled tiles for validation and 176 labelled tiles for testing. Using CrevasseSeg, we benchmark five self-supervised objectives -- BYOL, a Jensen-Shannon Divergence (JSD...
Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties. Existing probabilistic downscalers address this gap using hand-crafted topographic descriptors. We ask instead whether Earth observation foundation models can provide transferable sub-grid surface representations for probabilistic weather downscaling. We augment a convolutional conditional neural process that downscales coarse ERA5 re...
Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning. Traditional (empirical) propagation models often exhibit limited accuracy in these scenarios. We investigate machine learning models for LoRa path loss prediction, systematically analyzing how prediction accuracy scales with training set size using real-world measurements from an urban deployment. Our approach employs a Random Forest with LiDAR-derived terrain features and k-Nearest Neighbors with coordinate data, comparing their...
TerraNova foundation model integrates Earth system physics and societal data across continuous and administrative geometries.
OpenAI reduces GPT-5.6 pricing for Luna and Terra variants, emphasizing efficiency gains for enterprise AI deployment.
TerraZero procedural simulator enables self-play RL for autonomous driving at 1.3M steps/sec, scaling training without high-fidelity rendering.
OpenAI releases GPT-5.6 family (Luna, Terra, Sol) with tiered pricing; claims superior agentic performance vs. Claude Opus/Fable on benchmarks.
Multi-modal 3D terrain reconstruction for wildfire-prone regions using image-based methods with DEM priors as geometric guidance.
Forterra has deployed more than 100
Oddly tiered releases to both OAI and ANT on the same day.
OpenAI launches GPT-5.6 series (Sol, Terra, Luna) with tiered performance/cost; limited preview underway, general availability in weeks.
Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a "fast and affordable" everyday model. OpenAI says it's especially skilled at coding, cybersecurity, and biology, as well as staying focused during long-horizon agentic AI tasks. Per million tokens, GPT-5.6 Sol is priced at $5 input / $30 output (nearly half the cost of Anth...
Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term Memory (LSTM) networks can successfully predict forecast errors in the High-Resolution Rapid Refresh (HRRR) model using mesonet observations, but we believe performance degradation is linked to periods of complex vertical atmospheric evolution. To address this limitation, we develop a hybrid LSTM-Visio...
Score-based diffusion models typically use Brownian perturbations, which provide tractable reverse-time dynamics but impose memoryless noising. We introduce Volterra generative models, a continuous-time score-based framework whose forward process injects path-dependent noise through fractional kernels. To handle the non-Markovian and non-semimartingale dynamics, we construct finite-dimensional Markovian lifts using Gaussian quadrature in both regimes and a hybrid finite-difference exponential approximation in the smooth regime. We prove squared error bounds, derive an augmented linear-Gaussia...
Aerial wildfire suppression requires not only predicting fire spread, but also designing effective intervention strategies under operational and environmental uncertainty. We present a modeling and optimization framework for aerial wildfire suppression that combines a hybrid neural-cellular automaton wildfire model with gradient-based design of targeted aerial drops. The wildfire model predicts spatially varying spread behavior from terrain, fuel, and wind data, while the intervention module determines binary drop actions with continuous-valued location and orientation parameters mapped to th...
This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained adversarial conditions. We develop a multi-agent geopolitical wargame, the Cerulean Sea Crisis, a synthetic maritime territorial dispute designed to mirror the structural dynamics of Eastern Mediterranean conflicts. Six frontier models (GPT-4o, Llama-4, Mistral-Large, Gemini-3.1-Pro, Qwen3.6-Plus, and DeepSeek-R1) participate in a between-groups experiment (N = 10 games per arm, K = 5 rounds per game) in which the sole manipulation is the language o...
SwAIther-Precip: lead-time-aware bias correction for kilometer-scale statistical downscaling of global AI weather forecasts over Swiss terrain.
Momentum integrates runtime procedural content generation and autonomous agent evaluation in endless-runner gameplay to assess generated terrain balance and solvability.