Smarter AI Teams, Smaller Bills: Meet BAMAS Large language model (LLM) agent teams can tackle tough problems—but their cloud bills add up fast. BAMAS is a new method for building multi-agent systems that keeps performance high while respecting a budget. How it works: * Pick the right mix of models.
Matrix: Faster, flexible synthetic data—without a central bottleneck Training AI often needs synthetic data, especially when real data is scarce, pricey, or private. But most generators rely on a central “traffic cop,” slowing things down. Matrix flips the script with a peer‑to‑peer design. Tiny specialized agents pass
AI
Extreme weather is hitting harder and more often, but diagnosing the “why” behind each event is slow and expert-heavy. Meet EWE (Extreme Weather Expert): an AI agent that acts like a weather detective. * Thinks like experts: Plans analyses, reasons step-by-step, and uses a meteorology toolkit. * Works end-to-end: Turns raw atmospheric
cybersecurity
Can AI spot phishing? This new email dataset puts it to the test Phishing and spam are getting smarter—often written by large language models. Rebeka Toth, Tamas Bisztray, and Richard Dubniczky release a labeled email dataset that separates phishing, spam, and legitimate messages, and flags whether they were written
Policymakers don’t need crystal balls—they need responsible foresight. This paper introduces "responsible computational foresight": human-centric AI plus simulations to anticipate risks and opportunities, and to design policies that are sustainable, resilient, and fair. * Human-centered: AI augments judgment, not replaces it. * Systems-aware: Accounts for social, environmental, economic,
AI
AI on our phones still makes awkward mistakes—and fixing them usually means heavy retraining in the cloud. This research shows a lightweight way to correct errors on-device, in seconds. What’s new: * One-shot fixes: Show the app a single example, and it updates its prototype for that class—no
cybersecurity
Smarter email defenses, grounded in real messages Phishing and spam are evolving fast—often with help from AI. This study releases a large, carefully labeled email dataset spanning phishing, spam, and legitimate messages, with a key twist: it marks whether each message was written by a human or an LLM.
Robotics
Robots guided by Vision-Language-Action (VLA) models are getting good at everyday tasks — but most still grab with simple two-finger claws. That limits them on flat, slippery, or handle-less surfaces. VacuumVLA adds a low-cost twist: a single end-effector that combines a standard gripper with a vacuum suction tool. It can switch
AI for Responsible Foresight What if AI could help policymakers move beyond prediction—and design futures that are ethical, resilient, and sustainable? This paper introduces responsible computational foresight: using human‑centric AI, simulations, and scenario analysis to anticipate risks and opportunities across social, environmental, economic, and political systems. * Explore uncertainty:
AI on our phones still makes embarrassing mistakes. This research shows a simple fix: let users correct misclassifications with just one or two examples—no cloud retraining, no heavy downloads. * Server side: big foundation models teach (distill) strong features into a compact, device-friendly network. * On device: the app classifies using
Finding new uses for existing medicines can deliver treatments faster and cheaper—but today’s tools are fragmented and hard to use. ChatDRex is a conversation-based, no‑code system that helps clinicians and researchers predict drug repurposing candidates simply by asking in natural language. * Built on the NeDRex biomedical knowledge
AI
How do we know if an AI model is truly aligned with human preferences—helpful, honest, safe, and instruction-following? This paper explores a surprisingly effective shortcut: judge the judges. Instead of grading a model’s open-ended answers (which needs lots of human effort or very strong AI judges), the authors