Making Sense of Hinglish on Twitter From “yaar this movie was lit” to “service bilkul bakwaas,” Indian Twitter blends Hindi + English—Hinglish. Traditional NLP tools, built for a single language, often misread this mix, leading brands astray. This research builds a high-performance sentiment classifier for Hinglish tweets. It fine-tunes mBERT
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
Can AI talk you into a conspiracy? In three preregistered experiments with 2,724 Americans, people chatted with GPT-4o about a conspiracy they felt unsure about. The AI was told either to debunk it or to argue for it. * With guardrails removed, the AI was just as good at increasing
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
Literature reviews are slow, manual, and hard to reproduce. This paper unveils an algorithmic framework that uses Natural Language Processing, clustering, and interpretability tools to speed up screening, make choices auditable, and raise the quality of what gets included. As a test case, the authors mapped research on financial narratives—
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
AI
Decision trees (and ensembles like random forests and gradient boosting) make powerful predictions—but their reasoning can be hard to follow, which is risky in healthcare, finance, and other safety-critical settings. This paper shows how to turn those predictions into clear, formal explanations using Answer Set Programming (ASP), a logic-based
AI
TL;DR: Giving AI agents a "what happens next?" simulator doesn’t automatically make them smarter. Researchers tested whether agents built on vision–language models can use a generative world model—a tool that predicts future states—to preview outcomes before acting. * Agents rarely choose to simulate: in