arXiv AI
Jul 20
AV-JEPA: Extending LeJEPA to Audio-Visual Self-Supervised Learning
arXiv:2607. 15295v1 Announce Type: cross Abstract: We present AV-JEPA, an elegant multimodal extension of LeJEPA to audio-visual self-supervised learning.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv Machine Learning
Jul 20
CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks
arXiv:2607. 15753v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) used in safety-critical applications are vulnerable to hardware and memory faults that corrupt network weights and degrade reliability.
arXiv Machine Learning
Jul 20
PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
arXiv:2607. 16156v1 Announce Type: new Abstract: Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.
arXiv Machine Learning
Jul 20
SloMo-Fast: Slow-Momentum and Fast-Adaptive Teachers for Source-Free Continual Test-Time Adaptation
arXiv:2511. 18468v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) is crucial for deploying models in real-world applications with unseen, evolving target domains.
arXiv AI
Jul 20
Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
arXiv:2607. 15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings.
arXiv Machine Learning
Jul 20
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
arXiv:2605. 31259v2 Announce Type: replace Abstract: Unscheduled trips of high-power pulsed converters are a leading source of downtime at large accelerator facilities.
arXiv AI
Jul 20
Lazy Arithmetic using Systolic Arrays for Closing the Verification Gap on Embedded Systems
arXiv:2607. 15328v1 Announce Type: cross Abstract: Complex algorithms such as deep neural networks are increasingly being deployed on embedded, resource constrained platforms.
arXiv Machine Learning
Jul 20
SODA: Semi On-Policy Black-Box Distillation for Large Language Models
arXiv:2604. 03873v4 Announce Type: replace Abstract: Black-box knowledge distillation for large language models presents a strict trade-off.
arXiv AI
Jul 20
Scaling Time Series Classification via XAI-Driven Data Reduction
arXiv:2607. 15774v1 Announce Type: cross Abstract: Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for downstream tasks remains under-explored.
arXiv AI
Jul 20
Empathy as Predictive Misalignment Tolerance: A Co-Regulation Framework and the Regime Structure of Dialogue Repair
arXiv:2607. 15282v1 Announce Type: cross Abstract: Empathy is most often theorized as resonance: a mirroring of another's present emotional or cognitive state.
arXiv Machine Learning
Jul 20
Knowledge-Guided Cross-Modal Fusion for Adult-to-Pediatric ECG Transfer via Label-Conditioned Contrastive Alignment
arXiv:2607. 15928v1 Announce Type: new Abstract: Adult and pediatric electrocardiogram (ECG) interpretation relies on age-sensitive criteria, and models pretrained mainly on adult ECGs often transfer poorly to pediatric populations when pediatric labels are scarce.
arXiv AI
Jul 20
Harmonizing AI Safety Thresholds
arXiv:2607. 16112v1 Announce Type: new Abstract: Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies.
arXiv Machine Learning
Jul 20
QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides
arXiv:2607. 15810v1 Announce Type: new Abstract: Rollout generation is a major bottleneck in Reinforcement Learning (RL) for Mixture-of-Experts (MoE) Large Language Models, motivating low-precision rollout acceleration such as FP8.
arXiv AI
Jul 20
Logic, Optimization, and Artificial Intelligence
arXiv:2607. 15532v1 Announce Type: new Abstract: Logic and optimization can, in combination, make valuable contributions to rule-based AI.
arXiv Machine Learning
Jul 20
A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data
arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.
arXiv Machine Learning
Jul 20
Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control
arXiv:2607. 15412v1 Announce Type: new Abstract: Multi-objective learning (MOL) aims to optimize multiple objectives simultaneously.
arXiv AI
Jul 20
SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training
arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.
arXiv Machine Learning
Jul 20
Cost-efficient generative AI summarization for scalable automated essay scoring in educational assessment
arXiv:2607. 15829v1 Announce Type: cross Abstract: Automated essay scoring (AES) enables scalable assessment and timely feedback but remains challenged by transformer input-length limitations, which can cause information loss when processing long essays.
arXiv AI
Jul 20
Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making
arXiv:2205. 04599v2 Announce Type: replace-cross Abstract: Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians and patients to interpret.
arXiv AI
Jul 20
MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation
arXiv:2607. 15589v1 Announce Type: cross Abstract: Communication-limited robots in mission-critical scenarios such as disaster inspection and search-and-rescue must make reliable onboard decisions without access to remote operators or high-capacity reasoning services.