May 2026

Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features are extracted for representation learning. This paper investigates a simple yet effective EEG representation, i.e., microstates. Microstates represent the building blocks of brain activity patterns at a microscopic time scale. We build a universal microstate tokenizer from a large medical EEG dataset by clustering […]

Ver mais

Hi all, I wanted to get some insights on solving a problem that I’m trying to model as a bandit. I’m fairly new to the subject, so if I’m saying nonsensical things, please explain. Basically, the idea is that pulling an arm gets you a reward, but that reward depends on some factors that change, so pulling the same arm again won’t give the same reward. I tried to use epsilon greedy, and things sort of make sense. […]

Ver mais

The proliferation of generative artificial intelligence has given rise to an interactive learning environment, where model parameters are continuously updated using not only data generated by natural processes, but also synthetic outputs produced by other models. This paradigm introduces two major challenges: (1) training data are no longer drawn exclusively from the target population, undermining a core assumption of classical statistical learning, and (2) model training processes become inherently correlated, as models interact with one another through repeated […]

Ver mais

I’m working with synthetic 2D navigation environments for testing learning-based path planning methods, where the agent must trade off between different criteria like efficiency, safety, and smoothness. One issue I keep running into is that the structure of the environment itself can unintentionally create shortcuts in learning. For example, if certain geometric patterns (like narrow corridors or open spaces) consistently align with specific outcomes, the model tends to pick up on those correlations rather than learning the underlying […]

Ver mais

Learning generalizable trajectory representations from raw GPS traces remains difficult because the data is continuous, noisy, and irregularly sampled. Spatial tokenization is also challenging: fine grids yield sparse cells with weak embeddings, while coarse grids merge heterogeneous movement patterns into the same token. We present TrajTok, a trajectory encoder with a simple pretraining recipe for transferable trajectory embeddings. TrajTok first learns a multi-resolution hexagonal cell partition from the spatial distribution of GPS points, converting noisy GPS sequences into […]

Ver mais

Manus is ramping up its automation game with Scheduled Tasks 2.0 that is a system built to make recurring AI workflows more persistent and context-aware. The big idea is that scheduled tasks keep working within the same environment, remembering past conversations, uploaded files, instructions, and outputs. So, every scheduled run isn’t treated like a brand-new process anymore. Now, the platform supports workflow continuity across tasks and projects. Manus is saying the new scheduling system helps users automate repetitive […]

Ver mais

Artificial intelligence is changing pharmaceutical research fast, but this collaboration between SandboxAQ and Anthropic is a real shift in how all these tools might end up being used day-to-day in labs and research centers. SandboxAQ, a quantum and AI tech company that spun out of Alphabet, announced they’re integrating their LQMs with Claude. Their plan is to open up powerful computational chemistry and molecular analysis tools to scientists who don’t necessarily have backgrounds in coding, quantum computing, or […]

Ver mais

Google I/O 2026 is almost upon us. In exactly an hour from now, Sundar Pichai will take the stage to kick off Google’s biggest developer event of the year. And just like everyone expected, AI is reportedly going to dominate almost every part of the keynote once again. Given that Google is pouring billions of dollars into AI, the company seems ready to double down on Gemini even further. For those who missed it, Google already held a […]

Ver mais

With this release, Cursor’s taking another big step in the fast-moving world of AI for coding. Competition is heating up among companies building these software engineering assistants. The new generation of models acts more like independent agents, they navigate huge projects, understand complex context across files, and keep long programming workflows running with less hands-on support. Cursor says Composer 2.5 delivers more reliability, better reasoning, and higher efficiency, all while cutting token costs for developers and businesses. This […]

Ver mais

Backpropagation with gradient descent is a common optimization strategy employed by most neural network architectures in machine learning. However, finding optimal hyperparameters to guide training has proven challenging. While it is widely acknowledged that selecting appropriate parameters is crucial for avoiding overfitting and achieving unbiased outcomes, this choice remains largely based on empirical experiments and experience. This paper presents a new probabilistic framework for the learning rate, a key parameter in stochastic gradient descent. The framework develops classic […]

Ver mais