AI
What if AI could think with pictures? Omni-R1 is a new multimodal AI that doesn’t just “talk through” problems—it draws its intermediate steps. Instead of relying on one fixed reasoning style, it unifies many skills (like zooming into regions, pointing to objects, or marking paths) by generating small
AI
Big business decisions often rely on complex optimization models, but building them is slow and manual. Meet LEAN-LLM-OPT, a lightweight, multi-agent AI that auto-formulates large-scale optimization models from a plain-English problem description and datasets. How it works: two planner agents design a step-by-step workflow for similar problems; a builder agent
Robotics
Humanoid robots need to coordinate physically with people—lifting, handing over, steadying—but we lack data of humans interacting with robots. What if they learned from humans interacting with humans? The catch: simply mapping human motions onto a robot often breaks crucial touches and supports. The team proposes PAIR (Physics-Aware
AI
AI discovers better ways to fast-charge batteries Charging batteries quickly without wearing them out is hard—and testing each new idea takes time and money. Researchers show that large language models (the tech behind chatbots) can help design smarter charging “recipes.” * Two approaches: Prompt-to-Optimizer (P2O), where an AI writes small
AI
TL;DR An LLM acts as a coach to prune another LLM, shrinking it ~45% while preserving key knowledge and accuracy. Traditional pruning uses fixed rules and often wipes out facts. This paper lets a foundation model adaptively choose which layers to trim each round. It reads layer sensitivity snapshots—
AI
AI is turbocharging factories, supply chains, and maintenance—but it also widens the ethical playing field. This chapter maps where industrial AI meets ethics: transparency, accountability, fairness, data sharing, and responsible R&D. Core message: building ethics into systems from day one accelerates innovation and trust. Ethics isn’t
AI
TL;DR Debiasing big language models is costly. This study shows compact “BabyLMs” can mimic how larger BERT-style models learn biases—so researchers can test ideas faster and cheaper. * BabyLMs (small, BERT-like models on tiny, editable corpora) track the same bias and performance patterns as standard BERTs. * Correlations hold across
AI
Can AI read ancient coins? Ancient Roman coins are tiny time capsules—but there are millions, and decoding their symbols is slow work. A new study by David Reid and Ognjen Arandjelovic tests whether modern AI can spot coin motifs (like emperors, gods, altars) more reliably. The team compared classic
Audio
What if you could say, “warm it up for a chill evening” or “crisper vocals for a noisy cafe,” and your system dialed in the perfect EQ—no sliders, no presets? This study introduces an LLM-powered equalizer that translates natural-language prompts into audio EQ settings. Trained with data from controlled
AI
Who controls what we see online shapes what we believe—and who holds power. A new paper warns that today's information platforms are vulnerable to authoritarian capture, turbocharged by AI persuasion and Big Tech dominance. Drawing on Paulo Freire's emancipatory pedagogy, the authors argue for a
AI
Leaderboards drive Text-to-SQL progress—but what if the test sets are wrong? This study audits two popular benchmarks and finds widespread annotation errors that can flip who looks best. * Error rates: 52.8% in BIRD Mini-Dev, 62.8% in Spider 2.0-Snow. * After correcting a BIRD Dev subset, open-source agents
AI
Can AI ship real software? Meet APEX-SWE, a new benchmark that tests whether frontier AI models can do economically valuable software engineering—not just toy coding puzzles. * Integration tasks (n=100): build end-to-end systems across cloud primitives, business apps, and infrastructure-as-code. * Observability tasks (n=100): debug production failures using logs,