Protein language models (PLMs) have shown remarkable capabilities in various protein function prediction tasks. However, while protein function is intricately tied to structure, most existing PLMs do not incorporate protein structure information. To address this issue, we introduce ProSST, a Transformer-based protein language model that seamlessly integrates both protein sequences and structures. ProSST incorporates a structure quantization module and a Transformer architecture with disentangled attention.

Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation and video prediction. Modern techniques for the solution of these problems typically combine Convolution Neural Networks (CNN) architecture with a time prediction mechanism. However, oftentimes, such approaches underperform in the long-range propagation of information and lack explainability. In this work, we introduce a physically inspired architecture for the solution of such problems. Namely, we propose to augment CNNs with advection by designing a novel semi-Lagrangian push operator. We show that the proposed operator allows for the non-local transformation of information compared with standard convolutional kernels. We then complement it with Reaction and Diffusion neural components to form a network that mimics the Reaction-Advection-Diffusion equation, in high dimensions. We demonstrate the effectiveness of our network on a number of spatio-temporal datasets that show their merit.

Physics-Informed Neural Networks (PINNs) are infamous for being hard to train.Recently, second-order methods based on natural gradient and Gauss-Newton methods have shown promising performance, improving the accuracy achieved by first-order methods by several orders of magnitude. While promising, the proposed methods only scale to networks with a few thousand parameters due to the high computational cost to evaluate, store, and invert the curvature matrix.We propose Kronecker-factored approximate curvature (KFAC) for PINN losses that greatly reduces the computational cost and allows scaling to much larger networks.Our approach goes beyond the popular KFAC for traditional deep learning problems as it captures contributions from a PDE's differential operator that are crucial for optimization. To establish KFAC for such losses, we use Taylor-mode automatic differentiation to describe the differential operator's computation graph as a forward network with shared weights which allows us to apply a variant of KFAC for networks with weight-sharing. Empirically, we find that our KFAC-based optimizers are competitive with expensive second-order methods on small problems, scale more favorably to higher-dimensional neural networks and PDEs, and consistently outperform first-order methods.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

PCWorld reports that Nvidia's DLSS 5 announcement caught major game developers from Ubisoft and Capcom off-guard, who were unaware their games would be featured in demonstrations. The generative AI technology faces significant backlash from gamers who criticize it as an "AI filter" that potentially devalues game aesthetics and may require two high-end GPUs. Despite being planned for fall 2026 release, DLSS 5 already raises concerns about artistic control and whether developers want this AI-enhanced visual processing in their games. Nvidia DLSS 5 is coming later this year, adding generative "AI" features to the performance-enhancing tech . Gamers are calling the tool an "Instagram yaas filter" and "AI slop," among other, less kind terms. The way that it adds detail to faces and seems to hijack -- or replace?

Instant to reduce annoying "if you want" and teaser-style phrasing that users found intrusive. This change addresses widespread user complaints about persistent, clickbait-like follow-up prompts that negatively impacted the AI interaction experience. The update aims to create more natural, direct conversations by making ChatGPT less chatty and eliminating the bothersome response teasers. It wasn't all that long ago that ChatGPT was a constant nag, persistently dropping "Would you like me to?"-style questions at the end of its responses. OpenAI eventually tweaked the phrasing, dropping the question marks and going for "if you want"-style teasers that invited users to extend their chat sessions. Now, OpenAI has acknowledged that it went too far with the clickbaity follow-ups, noting in a recent update for one of its newest models that it's now cutting back on the teasers. "We're rolling out an update to GPT-5.3 Instant that improves follow-up tone and reduces teaser-style phrasing," reads a recent ChatGPT release note, which adds that users should soon see fewer follow-ups like "if you want," "you'll never believe," and "I can tell you three things that " Those teasers are, of course, a way for ChatGPT to keep subscribers chatting, but users have been complaining that the persistent follow-ups are more annoying than they are intriguing. "I hated it with a passion and hope it's completely gone," wrote one user on Reddit .

Fine-tuning is a crucial process for adapting large language models (LLMs) to diverse applications. In certain scenarios, such as multi-tenant serving, deploying multiple LLMs becomes necessary to meet complex demands. Recent studies suggest decomposing a fine-tuned LLM into a base model and corresponding delta weights, which are then compressed using low-rank or low-bit approaches to reduce costs. In this work, we observe that existing low-rank and low-bit compression methods can significantly harm the model performance for task-specific fine-tuned LLMs (e.g., WizardMath for math problems). Motivated by the long-tail distribution of singular values in the delta weights, we propose a delta quantization approach using mixed-precision. This method employs higher-bit representation for singular vectors corresponding to larger singular values. We evaluate our approach on various fine-tuned LLMs, including math LLMs, code LLMs, chat LLMs, and even VLMs. Experimental results demonstrate that our approach performs comparably to full fine-tuned LLMs, surpassing both low-rank and low-bit baselines by a considerable margin. Additionally, we show that our method is compatible with various backbone LLMs, such as Llama-2, Llama-3, and Mistral, highlighting its generalizability.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration. Our method employs weighted sampling within a contrastive learning framework, assigning lower penalties to samples with similar survival outcomes. This aligns well with the assumption that patients with similar event times share similar clinical statuses. Consequently, when augmented with the commonly used negative log-likelihood loss, our approach significantly improves discrimination performance without directly manipulating the model outputs, thereby achieving better calibration.Experiments on multiple real-world clinical datasets demonstrate that our method outperforms state-of-the-art deep survival models in both discrimination and calibration.

The Fight to Hold AI Companies Accountable for Children's Deaths After a series of suicides allegedly linked to AI chatbots, one lawyer is trying to hold companies like OpenAI accountable. Cedric Lacey relied on a camera to check on his kids while he was working as a commercial van driver going to and back from Alabama. Each morning, he would tune into the feed of his living room to make sure his teenage son, Amaurie, and his 14-year-old daughter were packing up their bags and getting ready to leave for school. But one morning last June, Lacey didn't see Amaurie up and about. Concerned, he called home, only to find out that his 17-year-old had hanged himself.

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. Over the course of the next few months, we'll be talking to some of the 2026 AAAI Fellows . In this interview, we met with Yan Liu, University of Southern California, who was elected as a Fellow . We found out about how time series research has progressed, the vast range of applications, and what the future holds for this field. Could you start with a quick introduction to your area of research?