AI
AI meets messy spreadsheets New research shows that large language models (LLMs) struggle when tables are subtly distorted—through small layout or labeling tweaks—even if the data is otherwise standard. * LLMs rarely notice or fix these distortions on their own. * Giving an explicit "watch out for table errors&
AI
LLMs with Retrieval-Augmented Generation (RAG) work better when they read lots of context — but long prompts are slow and pricey. Compressing the context helps, yet models often lose the thread and answer worse. ArcAligner is a lightweight module that helps models make sense of highly compressed context. It "aligns&
AI
Sometimes, vision-language AIs see what the prompt says, not what is in the picture. New research maps how that happens—and how to dial it down. Rudman and colleagues tested object counting with prompts that intentionally overstate what is in an image (for example, "describe four waterlilies" when
AI
As AI models start learning from their own outputs, they risk entering an echo chamber. A new study names this cycle the Self-Consuming Performative Loop (SCPL) and shows how it can warp model behavior over time. * What’s the loop? Deployed models shape what people ask and which data gets
AI
Personalized AI you can read, edit, and take anywhere Most AI systems hide your preferences in opaque vectors. This work makes them plain English instead. Your "profile" becomes a short text summary the model can use across apps and tasks. * Interpretable: you can see and revise what the
AI
Arabic AI users are hitting a hidden speed bump LLM-powered agents are everywhere—but most tests are English-first. Kubrak et al. present the first benchmark to evaluate tool-calling and agentic skills when users prompt in Arabic. * What they built: a standardized way to measure functional accuracy and robustness in Arabic
AI
AI assistants that remember our past chats can feel personalized, but there is a catch. Using all history can anchor them to old habits; using none makes them forget who you are. SteeM (Steerable Memory Agent) introduces a simple idea: a controllable memory dial. * Fresh-start mode: prioritize new ideas and
BCI
Brain-to-Vehicle, in Real Time What if your car knew you were about to turn before you moved the wheel? This study turns EEG brainwaves into steering intentions using deep learning, hinting at faster, more intuitive driver assistance. * Task: Predict left, right, or straight intentions while people “drove” in a simulator.
AI
AI model aims to boost stock price forecasting Can AI forecast stock prices more reliably? A new study introduces NP-DNN, which pairs NeuralProphet (a time-series forecasting tool) with a deep neural network (multi-layer perceptron) to learn hidden patterns in market data. * Clean the data: Z-score normalization removes scale differences so
AI
Many headlines say AI chatbots "leak" your personal info. This study asks: is that true memorization, or are prompts giving the answer away? The authors find many "leaks" are driven by surface cues in the prompt. They introduce Cue-Resistant Memorization (CRM): test models under low-cue conditions
AI
Smarter AI means knowing when to think harder LLMs can improve by thinking longer, but they rarely know how much thinking a task really needs. ROI-Reasoning trains models to plan their effort under a hard token budget, like students managing time on an exam. * Meta-Cognitive Fine-Tuning: Before answering, the model
AI
Teaching AI to Follow the Rules—While Staying Creative GANs can generate realistic images and data, but they often ignore the “rules of the world.” Enter LTN‑GAN: a new framework that blends Generative Adversarial Networks with Logic Tensor Networks, so the generator learns to produce samples that look real