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Internal memos: Meta said Avocado is its "most capable pre-trained base model" and achieves 10x compute efficiency "wins" on text tasks vs. Llama 4 Maverick (Jyoti Mann/The Information)
https://www.theinformation.com/articles/meta-memo-new…

Meta Memo: New Avocado Model ‘Most Capable’ to Date
Meta Platforms is sounding an increasingly bullish note about the first major AI model expected to emerge from its new AI group. The company recently told some staffers in the group, known as Meta Superintelligence Labs, that its next generation large language model, codenamed Avocado, is “now ...

Mitigating Forgetting in Low Rank Adaptation
Joanna Sliwa, Frank Schneider, Philipp Hennig, Jose Miguel Hernandez-Lobato
https://arxiv.org/abs/2512.17720 https://arxiv.org/pdf/2512.17720 https://arxiv.org/html/2512.17720
arXiv:2512.17720v1 Announce Type: new
Abstract: Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), enable fast specialization of large pre-trained models to different downstream applications. However, this process often leads to catastrophic forgetting of the model's prior domain knowledge. We address this issue with LaLoRA, a weight-space regularization technique that applies a Laplace approximation to Low-Rank Adaptation. Our approach estimates the model's confidence in each parameter and constrains updates in high-curvature directions, preserving prior knowledge while enabling efficient target-domain learning. By applying the Laplace approximation only to the LoRA weights, the method remains lightweight. We evaluate LaLoRA by fine-tuning a Llama model for mathematical reasoning and demonstrate an improved learning-forgetting trade-off, which can be directly controlled via the method's regularization strength. We further explore different loss landscape curvature approximations for estimating parameter confidence, analyze the effect of the data used for the Laplace approximation, and study robustness across hyperparameters.
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HeatMat: Simulation of City Material Impact on Urban Heat Island Effect
Marie Reinbigler, Romain Rouffet, Peter Naylor, Mikolaj Czerkawski, Nikolaos Dionelis, Elisabeth Brunet, Catalin Fetita, Rosalie Martin
https://arxiv.org/abs/2601.22796 https://arxiv.org/pdf/2601.22796 https://arxiv.org/html/2601.22796
arXiv:2601.22796v1 Announce Type: new
Abstract: The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult to study in real cities using sensor data (satellites or in-situ stations) due to their coarse spatial and temporal resolution. Among the factors contributing to this effect are the properties of urban materials, which differ from those in rural areas. To analyze their individual impact and to test new material configurations, a high-resolution simulation at the city scale is required. Estimating the current materials used in a city, including those on building facades, is also challenging. We propose HeatMat, an approach to analyze at high resolution the individual impact of urban materials on the UHI effect in a real city, relying only on open data. We estimate building materials using street-view images and a pre-trained vision-language model (VLM) to supplement existing OpenStreetMap data, which describes the 2D geometry and features of buildings. We further encode this information into a set of 2D maps that represent the city's vertical structure and material characteristics. These maps serve as inputs for our 2.5D simulator, which models coupled heat transfers and enables random-access surface temperature estimation at multiple resolutions, reaching an x20 speedup compared to an equivalent simulation in 3D.
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