arXiv:2603.27529v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is distorted as it is compressed through limited message-passing pathways. This bottleneck limits their ability to capture essential global context and decreases their performance, particularly in dense and heterophilic regions […]
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
arXiv:2604.02198v1 Announce Type: new Abstract: While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence to rigorous certification standards. Current EASA guidelines mandate demonstrating complete coverage of the AI/ML constituent’s Operational Design Domain (ODD) — a requirement that demands proof that no critical gaps […]
Phase estimation with autoregressive padding (PEAP): addressing inaccuracies and biases in EEG analysis
arXiv:2604.02212v1 Announce Type: new Abstract: Accurate phase estimation at the edge of data segments is crucial for EEG applications such as EEG-TMS in offline and real-time data analysis. Our research evaluates the phase estimation performance of four commonly used methods (Phastimate, SSPE, ETP, and PhastPadding) for accuracy and systemic biases, using data from young and […]
Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing
arXiv:2401.15855v1 Announce Type: cross Abstract: Remote sensing images present unique challenges to image analysis due to the extensive geographic coverage, hardware limitations, and misaligned multi-scale images. This paper revisits the classical multi-scale representation learning problem but under the general framework of self-supervised learning for remote sensing image understanding. We present Cross-Scale MAE, a self-supervised model […]
A Data-Driven Measure of REM Sleep Propensity for Human and Rodent Sleep
arXiv:2604.01252v1 Announce Type: new Abstract: Mammalian sleep is characterized by multiple alternations between episodes of rapid-eye-movement sleep (REMS) and non-REM sleep (NREMS). While the mechanisms governing the timing of these ultradian NREMS-REMS cycles remain poorly understood, the phenomenon of REMS pressure, namely a drive for REMS that builds up between REMS episodes, is thought to […]
OpenGo: An OpenClaw-Based Robotic Dog with Real-Time Skill Switching
arXiv:2604.01708v1 Announce Type: cross Abstract: Adaptation to complex tasks and multiple scenarios remains a significant challenge for a single robot agent. The ability to acquire organize, and switch between a wide range of skills in real time, particularly in dynamic environments, has become a fundamental requirement for embodied intelligence. We introduce OpenGo, an OpenClaw-powered embodied […]
What Capable Agents Must Know: Selection Theorems for Robust Decision-Making under Uncertainty
arXiv:2603.02491v2 Announce Type: replace-cross Abstract: As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical results show that optimal control can be *implemented* using belief states or world models, but not that such representations are required. We prove quantitative “selection theorems” showing that strong task […]
Learning Contextual Runtime Monitors for Safe AI-Based Autonomy
arXiv:2601.20666v3 Announce Type: replace-cross Abstract: We introduce a novel framework for learning context-aware runtime monitors for AI-based control ensembles. Machine-learning (ML) controllers are increasingly deployed in (autonomous) cyber-physical systems because of their ability to solve complex decision-making tasks. However, their accuracy can degrade sharply in unfamiliar environments, creating significant safety concerns. Traditional ensemble methods aim […]
Neuro-RIT: Neuron-Guided Instruction Tuning for Robust Retrieval-Augmented Language Model
arXiv:2604.02194v1 Announce Type: cross Abstract: Retrieval-Augmented Language Models (RALMs) have demonstrated significant potential in knowledge-intensive tasks; however, they remain vulnerable to performance degradation when presented with irrelevant or noisy retrieved contexts. Existing approaches to enhance robustness typically operate via coarse-grained parameter updates at the layer or module level, often overlooking the inherent neuron-level sparsity of […]
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
arXiv:2603.27044v2 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy parameter space. A recent framework, which we refer to as Action-based Policy Compression (APC), mitigates this issue by compressing the parameter space $Theta$ into […]
Ego-Grounding for Personalized Question-Answering in Egocentric Videos
arXiv:2604.01966v1 Announce Type: cross Abstract: We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding – the ability to understand the camera-wearer in egocentric videos. To this end, we introduce MyEgo, the first egocentric VideoQA dataset designed to evaluate MLLMs’ ability to understand, remember, and reason about the […]
Human Misperception of Generative-AI Alignment: A Laboratory Experiment
arXiv:2502.14708v3 Announce Type: replace-cross Abstract: We conduct an incentivized laboratory experiment to study people’s perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and […]