Protein engineering is a field primed for artificial intelligence research. Each protein is made up of amino acids; to optimize a protein function, researchers modify proteins by switching out one of 20 different amino acids for another. For a protein that is just 50 amino acids in length, this leads to approximately 1.13 10 potential combinations to test. This number of potential combinations, impossible to test in the lab, makes protein engineering an ideal challenge for AI. Modeling which of these combinations will give the best results is a perfect problem for the technology's massive computing power.

Neural rendering techniques, including NeRF and Gaussian Splatting (GS), rely on photometric consistency to produce high-quality reconstructions. However, in real-world driving scenarios, it is challenging to guarantee perfect photometric consistency in acquired images. Appearance codes have been widely used to address this issue, but their modeling capability is limited, as a single code is applied to the entire image. Recently, the bilateral grid was introduced to perform pixel-wise color mapping, but it is difficult to optimize and constrain effectively. In this paper, we propose a novel multi-scale bilateral grid that unifies appearance codes and bilateral grids. We demonstrate that this approach significantly improves geometric accuracy in dynamic, decoupled autonomous driving scene reconstruction, outperforming both appearance codes and bilateral grids. This is crucial for autonomous driving, where accurate geometry is important for obstacle avoidance and control. Our method shows strong results across four datasets: Waymo, NuScenes, Argoverse, and PandaSet. We further demonstrate that the improvement in geometry is driven by the multi-scale bilateral grid, which effectively reduces floaters caused by photometric inconsistency.

Large language models (LLMs) have emerged as a cornerstone in real-world applications with lengthy streaming inputs (e.g., LLM-driven agents). However, existing LLMs, pre-trained on sequences with a restricted maximum length, cannot process longer sequences due to the out-of-domain and distraction issues. Common solutions often involve continual pre-training on longer sequences, which will introduce expensive computational overhead and uncontrollable change in model capabilities. In this paper, we unveil the intrinsic capacity of LLMs for understanding extremely long sequences without any fine-tuning. To this end, we introduce a training-free memory-based method, InfLLM. Specifically, InfLLM stores distant contexts into additional memory units and employs an efficient mechanism to lookup token-relevant units for attention computation.

Learning byassociating ambiguously labeled images.

Can ChatGPT really help you change your life - or just flatter you? Can ChatGPT really help you change your life - or just flatter you? AI as a life coach: experts share what works, what doesn't and what to look out for It's becoming more common for people to use AI chatbots for personal guidance - but this doesn't come without risks Setting goals is hard; keeping them is harder - and failure can bring about icky feelings about yourself. This year, in an effort to game the system and tilt the scales toward success, some people used AI for their 2026 resolutions. It's the latest step in an ongoing trend: in September 2025, OpenAI, the company behind ChatGPT, released findings showing that using the AI chatbot for personal guidance is very common.

Tech giant Meta has announced it will buy artificial intelligence startup Manus in a rare crossover of US and Chinese technology amid Washington and Beijing's heated tech rivalry. Meta said the acquisition would see it take over the operation of Manus's self-directing AI agent and integrate the technology into its own products. Meta, the parent company of Facebook and Instagram, said the deal would bring one of the "leading autonomous general-purpose agents" to billions of people worldwide. "Manus's exceptional talent will join Meta's team to deliver general-purpose agents across our consumer and business products, including in Meta AI," the California-based firm said in a statement on Monday. "We're excited to welcome the Manus team and help improve the lives of billions of people and millions of businesses with their technology."

Generating large and diverse obstacle datasets to learn motion planning in environments with dynamic obstacles is challenging due to the vast space of possible obstacle trajectories. Inspired by hallucination-based data synthesis approaches, we propose Learning from Hallucinating Critical Points (LfH-CP), a self-supervised framework for creating rich dynamic obstacle datasets based on existing optimal motion plans without requiring expensive expert demonstrations or trial-and-error exploration. LfH-CP factorizes hallucination into two stages: first identifying when and where obstacles must appear in order to result in an optimal motion plan, i.e., the critical points, and then procedurally generating diverse trajectories that pass through these points while avoiding collisions. This factorization avoids generative failures such as mode collapse and ensures coverage of diverse dynamic behaviors. We further introduce a diversity metric to quantify dataset richness and show that LfH-CP produces substantially more varied training data than existing baselines. Experiments in simulation demonstrate that planners trained on LfH-CP datasets achieves higher success rates compared to a prior hallucination method.

We consider the problem of fairly allocating sequentially arriving items to a set of individuals. For this problem, the recently-introduced P ACE algorithm leverages the dual averaging algorithm to approximate competitive equilibria and thus generate online fair allocations. P ACE is simple, distributed, and parameter-free, making it appealing for practical use in large-scale systems. However, current performance guarantees for P ACE require i.i.d.