arXiv AI
Jul 3
Episodic-to-Semantic Consolidation Without Identity Drift
arXiv:2607. 01988v1 Announce Type: new Abstract: Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity.
Robotics and embodied AI
Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
arXiv AI
Jul 3
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
arXiv:2607. 02466v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale.
arXiv AI
Jul 3
CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation
arXiv:2607. 02222v1 Announce Type: cross Abstract: Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored.
arXiv Machine Learning
Jul 3
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
arXiv:2607. 02187v1 Announce Type: new Abstract: Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation.
arXiv AI
Jul 3
WorldSample: Closed-loop Real-robot RL with World Modelling
arXiv:2607. 02431v1 Announce Type: cross Abstract: Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond the states observed in demonstrations.
arXiv AI
Jul 3
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
arXiv:2607. 02426v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications.
arXiv Machine Learning
Jul 3
LIME: Learning Intent-aware Camera Motion from Egocentric Video
arXiv:2607. 02417v1 Announce Type: cross Abstract: Autonomous robots often need to move their camera before they can act: to inspect an object, reveal an occluded region, or obtain a view that responds to a user's intent.
arXiv AI
Jul 3
ACID: Action Consistency via Inverse Dynamics for Planning with World Models
arXiv:2607. 02403v1 Announce Type: cross Abstract: Decision-time planning with action-conditioned world models has become a popular paradigm for embodied control.
arXiv Machine Learning
Jul 3
Sign in the Air to Unlock: An Interface for authentication in Virtual and Augmented Reality Powered by Point-Voxel Cross-Attention Network
arXiv:2607. 01435v1 Announce Type: cross Abstract: Significant advancement of immersive technologies such as Virtual and Augmented Reality (VR/AR) and their integration into diverse aspects of modern life need authentication interfaces that are secure, intuitive, and compatible with embodied interaction.
arXiv Machine Learning
Jul 3
BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer
arXiv:2607. 01410v1 Announce Type: cross Abstract: Sim2real transfer for robot policy learning suffers due to mismatch between simulation and reality.
arXiv AI
Jul 3
Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving
arXiv:2604. 03497v2 Announce Type: replace-cross Abstract: Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-crafted rewards with semantically grounded signals; however, deploying such simulation-trained policies on real vehicles remains a fundamental challenge, because they rely on simulator-native observations and simulator-coupled action semantics with no counterpart on physical hardware.
arXiv AI
Jul 3
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
arXiv:2605. 00412v3 Announce Type: replace Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning.
arXiv AI
Jul 3
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
arXiv:2607. 02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation.
arXiv AI
Jul 3
BuilderBench: The Building Blocks of Intelligent Agents
arXiv:2510. 06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set by existing data.
arXiv AI
Jul 3
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
arXiv:2603. 22435v2 Announce Type: replace-cross Abstract: "Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied manipulation remains underexplored.
arXiv AI
Jul 3
Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies
arXiv:2607. 02092v1 Announce Type: cross Abstract: Flow-matching vision-language-action policies generate robot action chunks through an iterative transport process, creating an opportunity for test-time guidance without retraining the base policy.
arXiv AI
Jul 3
DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
arXiv:2603. 18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the rich contextual understanding required for safe driving and make unsafe exploration unavoidable in real-world settings.
arXiv AI
Jul 3
Learning 3D-Gaussian Simulators from RGB Videos
arXiv:2503. 24009v3 Announce Type: replace-cross Abstract: Realistic simulation is critical for applications ranging from robotics to animation.
arXiv AI
Jul 3
VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment
arXiv:2607. 01586v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remain difficult to compare because existing models often differ in architecture, data, action space, and evaluation protocol.
Hugging Face Trending Papers
Jul 2
Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving
Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets. However, the adversarial robustness of these models remains untested.