arXiv AI
Jun 16
Attention, not scale, drives human-AI alignment in multimodal language prediction
arXiv:2308. 06035v4 Announce Type: replace Abstract: Humans routinely draw on visual context to predict upcoming words.
AI safety and alignment
Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv AI
Jun 16
Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations & Unmeasurable Parameters
arXiv:2412. 00107v2 Announce Type: replace-cross Abstract: Real-time monitoring of safety-critical interior states remains an open problem in energy systems where physical instrumentation is infeasible.
arXiv Machine Learning
Jun 16
Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
arXiv:2605. 26290v2 Announce Type: replace Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks.
arXiv AI
Jun 16
FORTIS: Benchmarking Over-Privilege in Agent Skills
arXiv:2605. 09163v3 Announce Type: replace Abstract: Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution.
arXiv AI
Jun 16
Efficient Flow Matching using Latent Variables
arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.
arXiv Machine Learning
Jun 16
Enhancing Quantum Machine Learning with Anyons
arXiv:2606. 16090v1 Announce Type: cross Abstract: The power of quantum computing and quantum machine learning relies on harnessing uniquely quantum phenomena as computational resources.
arXiv AI
Jun 16
Hierarchical Modeling of ICD Codes in EHR Foundation Models
arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.
arXiv AI
Jun 16
From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
arXiv:2604. 21391v2 Announce Type: replace-cross Abstract: Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal scale mismatch between cognition and action.
arXiv AI
Jun 16
Input-Dependent Fisher Information for Local Sensitivity Analysis of Medical Image Classifiers
arXiv:2606. 16362v1 Announce Type: cross Abstract: Deep neural networks have achieved strong performance in medical image classification, but often work like black-box.
arXiv AI
Jun 16
Continuous Cross-Domain Traffic State Prediction via Memory-Augmented Graph Liquid Time-Constant Networks
arXiv:2606. 15807v1 Announce Type: cross Abstract: Traffic state prediction is a fundamental task in intelligent transportation systems.
arXiv Machine Learning
Jun 16
Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM
arXiv:2606. 16878v1 Announce Type: new Abstract: Retail marketing measurement increasingly requires granular campaign-level insights without relying on user-level tracking.
arXiv AI
Jun 16
Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection
arXiv:2606. 16742v1 Announce Type: cross Abstract: With the rapid advancement of video generation models, distinguishing between AI-generated and authentic videos has emerged as a challenging endeavor.
arXiv Machine Learning
Jun 16
DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.
arXiv AI
Jun 16
Guiding Federated Graph Recommendation with LLM-encoded knowledge
arXiv:2606. 15277v1 Announce Type: cross Abstract: Graph-based recommender systems are highly effective at extracting collaborative signals from user--item interactions, and federated learning (FL) allows these models to be trained while preserving user privacy.
arXiv AI
Jun 16
ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents
arXiv:2606. 15508v1 Announce Type: new Abstract: Tool-augmented large language model agents increasingly operate over large tool libraries, but existing evaluations often focus on whether a model can call a tool correctly rather than how the visible tool menu shapes reliability, efficiency, and safety-relevant risk exposure.
arXiv AI
Jun 16
Phase-Aware Guidance Injection for Recurrent MAPPO in Assembly-Line Disruption Recovery
arXiv:2606. 16330v1 Announce Type: new Abstract: Disruption recovery in industrial assembly lines requires timely decisions under machine faults, worker absence, and emergency orders.
arXiv AI
Jun 16
Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphs
arXiv:2606. 15656v1 Announce Type: new Abstract: Modern artificial intelligence remains fundamentally divided between the continuous, probabilistic spaces of Foundation Models and the discrete, deterministic structures of Knowledge Graphs.
arXiv Machine Learning
Jun 16
Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.
arXiv AI
Jun 16
An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification
arXiv:2606. 14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers.
arXiv Machine Learning
Jun 16
False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Control
arXiv:2606. 15153v1 Announce Type: new Abstract: Selective prediction with distribution-free risk control promises that, with confidence 1-delta over the calibration draw, the error rate of accepted inputs stays below a user budget alpha.