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
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
AI
Building images and videos in ComfyUI is like wiring LEGO blocks — powerful, but easy to break when workflows get long and complex. ComfySearch is a new AI agent that automatically explores, reasons, and assembles ComfyUI workflows. It builds step by step, checks whether each piece runs, and adjusts on the
AI
Teaching video AIs to sweat the small stuff Text-to-video models often miss human-preferred details or waste compute learning from vague, whole-video feedback. LocalDPO, a new training recipe, targets the exact frames and regions that need improvement—no human labels or extra critic model required. * How it works: Use a real,
AI
New research examines how German criminal law could apply when generative AI is used to create child sexual abuse material (CSAM). The takeaway: legal risk isn’t limited to the user—it can reach model developers, researchers, and company representatives. The authors combine legal analysis with scenarios to show that
AI
Turning jailbreakers into the trapped Jailbreak prompts can trick chatbots into breaking rules. HoneyTrap flips the script: a deceptive, multi-agent defense that engages attackers, wastes their time, and keeps helpful answers flowing for regular users. * Threat Interceptor: spots risky prompts early. * Misdirection Controller: steers attackers into harmless honeypots. * Forensic Tracker: