Who Controls the Future of AI?
Dominant tech companies are poised to control not just AI models, but the crucial process of inference, creating a new bottleneck for competition.
Science
Predicting DeFi Survival: A Race Against Time
A new benchmark challenge reveals the critical importance of understanding user behavior and temporal dynamics in forecasting success within decentralized finance.
Building Trustworthy AI for Advertising Q&A
A new framework leverages reinforcement learning to minimize inaccurate responses and enhance the reliability of question answering systems used in advertising platforms.
When AI Turns Whistleblower: Teaching Agents to Confess Their Mistakes
A new training method incentivizes language model agents to self-report harmful actions, dramatically increasing the detection of covert attacks and bolstering overall safety.
Playing to Win: Hardening AI Vision with a Deceptive Teacher
Researchers are leveraging adversarial self-play to automatically generate challenging training data, significantly improving the robustness of multimodal AI systems against perceptual vulnerabilities.
Taming Turbulence with AI: Discovering Hidden Fluid Dynamics
![Synthetic orbital trajectories, when guided by parameters reflecting periodic symmetry-such as [latex]\mathcal{T}^{s}\mathcal{R}^{a}\mathcal{S}^{m}[/latex] with varying <i>m</i> and <i>a</i>-converge to solutions of the Navier-Stokes equations, demonstrating a pathway to approximate fluid dynamics where dissipation and production rates align between synthetic (black) and converged (coloured) paths, as evidenced by trajectories reaching [latex]T\approx 1.56[/latex] and [latex]T\approx 2.46[/latex].](https://arxiv.org/html/2602.23181v1/2602.23181v1/figures/figposs94.png)
Researchers are harnessing the power of deep learning to uncover previously unknown periodic orbits within turbulent flows, offering new insights into complex fluid behavior.
Sifting Signal from Noise in Single-Cell Data
A new approach effectively isolates key biological variations in high-dimensional datasets by actively suppressing confounding background signals.
Smarter Bidding with AI: A New Approach to Automated Advertising
![The System for Evolving Goal-Based behaviors (SEGB) operates through a three-stage process-planning a high-fidelity future state prediction [latex]s^{\prime}\_{t+1}[/latex], generating an action [latex]a^{\prime}\_{t}[/latex] conditioned on that prediction, and then refining the decision-making through offline evolution guided by a frozen critic and reference model-with online inference streamlined to the efficient planning and action generation stages.](https://arxiv.org/html/2602.22226v1/2602.22226v1/x1.png)
Researchers have developed a novel framework that leverages diffusion models and reinforcement learning to create more effective and robust automated bidding strategies for online advertising.
Beyond the Gaussian Dream: How Neural Networks Learn Features
New research reveals that Bayesian neural networks exhibit complex feature learning beyond simple Gaussian process behavior, offering a deeper understanding of their predictive power.
Mapping Cosmic Rays with AI: A New Direction for Radio Astronomy
Researchers are leveraging the power of deep learning to pinpoint the source and energy of ultra-high-energy cosmic rays detected by ground-based radio antennas.