CAFD: Concept-Aware DNN Fault Detection using VLMs
arXiv:2605.24008v1 Announce Type: new
Abstract: Fault detection for Deep Neural Networks (DNNs) has received increasing attention in recent years. While more advanced hybrid approaches have been proposed to combine multiple sources of information and outperform earlier techniques, they often incur ...
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
arXiv:2605.22868v1 Announce Type: new
Abstract: Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets demand run-time adaptivity. In practice, deciding what to compute and ...
TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization
arXiv:2605.21622v1 Announce Type: new
Abstract: Topology optimization can generate efficient structures, but designers often must manually translate qualitative intent, such as desired visual style, product experience, or manufacturability into solver settings that are not directly tied to those pr...
The 3 reasons your AI never makes it to production
Most companies don't have an AI problem. They have a throughput problem. And I think that distinction matters a lot when you start talking about how to actually get AI working in production.
Double descent for least-squares interpolation on contaminated data: A simulation study
arXiv:2605.21494v1 Announce Type: new
Abstract: Overparametrized models can exhibit an excellent generalization performance, although they should be prone to overfitting according to classical statistical theory. The discovery of the "double descent", indicating that the generalization error decrea...
Don't Collapse Your Features: Why CenterLoss Hurts OOD Detection and Multi-Scale Mahalanobis Wins
arXiv:2605.21493v1 Announce Type: new
Abstract: The ability to detect out-of-distribution (OOD) inputs is fundamental to safe deployment of machine learning systems. Yet, current methods often rely on feature representations that are optimised solely for classification accuracy, neglecting the dist...
VSAS-Bench: Real-Time Evaluation of Visual Streaming Assistant Models
Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time visual assistants. Existing VLM frameworks predominantly assess models in offline settings. In contrast, the performance of...
Vega: Zero-knowledge proofs for digital identity in the age of AI
Vega turns a full credential into a single proof, sharing only what is needed and nothing more, with performance that works in real apps.
The post Vega: Zero-knowledge proofs for digital identity in the age of AI appeared first on Microsoft Research.
arXiv:2605.20467v1 Announce Type: new
Abstract: Neural networks can be trained to rank the choices made by logical reasoners, resulting in more efficient searches for answers. A key step in this process is creating useful embeddings, i.e., numeric representations of logical statements. This paper i...
OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
arXiv:2605.20423v1 Announce Type: new
Abstract: Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information ...
MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics
arXiv:2605.20240v1 Announce Type: new
Abstract: Battery health diagnostics today rely overwhelmingly on electrochemical signals measured at the cell terminals. A parallel literature has shown that magnetic sensing can resolve information that terminal-only measurements miss, but method development ...
AI agents keep breaking in production. Here's why nobody's fixed it yet
78% of enterprises have an AI agent pilot running. Only 14% have successfully scaled one. The gap isn't a model problem. It's an engineering one (and it's hiding in plain sight....)
Forget electrons, this breakthrough uses light-matter particles to power AI
Researchers at Penn have created a hybrid light-matter particle that could dramatically speed up AI computing while using far less energy. The breakthrough may help replace some electronic computing processes with ultra-efficient light-based technology.
SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
arXiv:2605.15215v1 Announce Type: new
Abstract: Recently, skills have been widely adopted in large language model (LLM)-based agent systems across various domains. In existing frameworks, skills are typically injected into the agent reasoning loop as contextual guidance once matched to a runtime ta...
NASA’s new AI space chip could let spacecraft think for themselves
NASA is testing a next-generation space computer chip that could give spacecraft the ability to operate far more independently in deep space. The radiation-hardened processor is showing performance levels hundreds of times beyond current spaceflight computers while surviving punishing tests designed...
Vision-Based Runtime Monitoring under Varying Specifications using Semantic Latent Representations
arXiv:2605.13923v1 Announce Type: new
Abstract: We study certified runtime monitoring of past-time signal temporal logic (ptSTL) from visual observations under partial observability. The monitor must infer safety-relevant quantities from images and provide finite-sample guarantees, while being \emp...
Your data engineers may be more influential than you think
The data engineer has gone from a largely behind-the-scenes role to one of the most strategically important positions in a modern technology organization. The leaders who understand why are making significantly better infrastructure decisions than the ones who do not.
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
arXiv:2605.12584v1 Announce Type: new
Abstract: Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to support various network applications. However, real-world graphs are often isolated due to data-sharing...