PAPER PLAINE

As factories, smart homes, and critical infrastructure rely on millions of connected devices, security breaches can cascade rapidly across networks. CLAD makes it practical for these devices to collectively learn threat patterns without exposing sensitive operational data to central servers, while actually improving detection accuracy by making use of unlabeled data that would otherwise be wasted.

As AI systems move to cloud computing, companies rely on cryptographic defenses to keep model weights secret while still computing results. This attack shows a widely-used shuffling defense provides a false sense of security—meaning companies using it may think their models are protected when they're actually vulnerable to cheap theft. Developers now need better defenses before deploying sensitive models to untrusted servers.

Low-dose CT reduces radiation risk to patients, but the grainy images can make tumors and other abnormalities harder to spot, potentially leading to missed diagnoses. This technique cleans up those images automatically using only the noisy scans themselves, making it immediately usable in hospitals without requiring expensive paired training data. Radiologists who reviewed the results confirmed it meets clinical standards, meaning patients could get safer imaging without sacrificing diagnostic clarity.

Image-to-text systems power real applications—from photo search to automated caption evaluation—and companies rely on them to be robust. This vulnerability means a single malicious or accidental hub text could poison search results or break evaluation metrics that measure whether AI-generated captions match human standards, undermining trust in systems used for content moderation, accessibility, and quality assurance.

As quantum computers become practical tools for real tasks, securing them against adversarial attacks matters for any high-stakes application—medical imaging, security screening, or autonomous systems. This defense works without the overhead of constantly retraining on new attack types, making it more practical to deploy when attackers keep changing their tactics.

Companies running AI services on their own hardware often need to handle both time-sensitive requests (like fraud detection) and routine ones (like recommendations) on the same machines. Current systems either guess badly at how long things will take under load or simply interrupt low-priority tasks—wasting GPU power. Strait lets businesses meet their critical deadlines while still processing regular work efficiently, making on-premises AI infrastructure more practical.

Phase diagrams are critical maps in physics and biology that show where systems behave differently. This machine-learning approach turns expensive simulations into comprehensive maps that can predict behavior across untested regions, potentially accelerating research into real collective motion—from bird flocks to autonomous robot swarms—by replacing exhaustive simulations with trained algorithms.

Power grid operators need to understand *why* a forecast says demand will spike before they commit expensive resources. Black-box predictions, no matter how accurate, create operational risk and regulatory friction. This work proves that grid forecasting can be both cutting-edge and transparent, removing a major barrier to deploying faster, more efficient AI systems in electricity infrastructure.