All Articles
16750 articles total
SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training
SLAC is introduced as a method for real-robot reinforcement learning that uses unsupervised simulation pre-training to create a safe, task-agnostic latent action space.
A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model
The paper proposes new classical and quantum algorithms for reinforcement learning that break the classical regret bound barrier using a generative model simulator.
Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD
A new U-net based approach for 360-degree acoustic source localization is presented, treating UAV detection as a spherical semantic segmentation task.
Unsupervised Deep Learning for Inverse Problems in Computed Tomography
An unsupervised deep learning framework is proposed for Computed Tomography (CT) reconstruction, achieving significant speed-ups over per-image optimization methods.
Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs
Latent Fusion Jailbreak (LFJ) is a white-box attack that manipulates internal LLM representations to elicit unsafe outputs with high success rates.
Poison to Detect: Detection of Targeted Overfitting in Federated Learning
The paper proposes three detection techniques to identify targeted overfitting attacks by dishonest orchestrators in Federated Learning systems.
A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream
A systematic study evaluates the integration of LLMs with PDDLStream for task and motion planning in robotics, finding LLM-based planners often underperform compared to engineered systems.
What does the Riemann zeta function have to do with the distribution of primes?
A discussion on the relationship between the Riemann zeta function and the distribution of prime numbers.
Airbus Takes Flight from AWS
Airbus is moving its infrastructure away from Amazon Web Services (AWS).
Towards a General Intelligence and Interface for Wearable Health Data
Researchers propose a foundation model for wearable health data pretrained on one trillion minutes of sensor signals from five million participants to provide personalized health insights.
Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields
Workflow-GYM is introduced as a benchmark for evaluating AI agents' ability to perform long-horizon GUI tasks in professional software environments.
Agents-K1: Towards Agent-native Knowledge Orchestration
Agents-K1 is an end-to-end knowledge orchestration pipeline that converts scientific papers into agent-native knowledge graphs, including the release of the Scholar-KG dataset.
Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making
VL4ML is a human-centered explainability framework that uses visual representations instead of numeric outputs to communicate AI model uncertainty in clinical settings.
Why do CNNs excel at feature extraction? A mathematical explanation
A mathematical study providing a theoretical explanation for why Convolutional Neural Networks (CNNs) are effective at feature extraction for image classification.
CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning
The Composite Tasks Challenge (CTC) is proposed as a benchmark to evaluate division of labor and cooperation in multi-agent reinforcement learning.
MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation
MAnchors proposes a memorization-based framework to accelerate the Anchors local model-agnostic explanation technique without losing fidelity.
AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing
AuditVotes is a framework for Graph Neural Networks (GNNs) that improves both clean accuracy and certified robustness against adaptive attacks.
China delivers a one-two punch to America’s AI dominance
Chinese AI companies Moonshot and Alibaba have released new models, including Kimi K3, that claim to rival US models at lower costs.
When Does Muon Help Agentic Reinforcement Learning?
Research indicates that the Muon optimizer can significantly improve success rates in agentic reinforcement learning, particularly in sparse-reward environments.
Evaluating Open-Weight LLMs for Generating Structured Threat Information for Autonomous Vehicle Vulnerabilities
Researchers evaluated open-weight LLMs for generating structured threat information (STIX) to automate vulnerability analysis for autonomous vehicles.