Knowledge catalogue
arXiv:2606.22304v1 Announce Type: new Abstract: Recently, several learning paradigms have been introduced to enforce idempotency in generative models. The goal is to ensure that repeated application o
arXiv:2602.07049v2 Announce Type: replace Abstract: Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardwar
arXiv:2606.22429v1 Announce Type: new Abstract: Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transfe
arXiv:2606.22381v1 Announce Type: cross Abstract: Road traffic accidents remain a critical global crisis, consistently serving as a primary driver of preventable mortality and severe injury. These inc
arXiv:2602.21218v2 Announce Type: replace-cross Abstract: High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic dat
arXiv:2606.23038v1 Announce Type: new Abstract: Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers a
arXiv:2606.20658v1 Announce Type: cross Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expecte
arXiv:2606.23131v1 Announce Type: new Abstract: Background: Laparoscopic camera navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are tim
arXiv:2606.21197v1 Announce Type: new Abstract: Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioni
arXiv:2602.06776v2 Announce Type: replace-cross Abstract: We study the transit stop placement (TrSP) problem in general metric spaces, where agents travel between source-destination pairs and may eith
arXiv:2606.23515v1 Announce Type: cross Abstract: Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined
arXiv:2602.23611v2 Announce Type: replace-cross Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such
arXiv:2503.22934v2 Announce Type: replace Abstract: Image classification models trained on clean data often degrade sharply when exposed to corrupted test or deployment data, such as images with impul
arXiv:2508.12042v2 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data. However, heteroge
arXiv:2606.21587v1 Announce Type: new Abstract: Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency. Standard
arXiv:2606.18011v2 Announce Type: replace-cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration,
arXiv:2606.21422v1 Announce Type: new Abstract: The rapid proliferation of Internet of Medical Things (IoMT) devices introduces critical cybersecurity vulnerabilities in healthcare environments where
arXiv:2509.17053v2 Announce Type: replace Abstract: Many contact-rich manipulation tasks require precise force regulation. However, most imitation learning (IL) policies remain position-centric and la
arXiv:2606.22303v1 Announce Type: new Abstract: Real-world reinforcement learning for robotic manipulation remains challenging, and this difficulty is amplified for flow matching policies: applying po
arXiv:2606.20867v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-sho
arXiv:2606.20967v1 Announce Type: new Abstract: Policies must operate across diverse conditions, yet a single policy is often conservative while fully adaptive schemes can be complex. We study zero-sh
arXiv:2606.21993v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) face driving scenarios ranging from routine traffic to rare events. To assess safety it is crucial to reproduce these scenar
arXiv:2606.21763v1 Announce Type: new Abstract: Medical image segmentation is widely used for disease detection but relies on sensitive data, raising privacy concerns as trained models can leak inform
arXiv:2606.23486v1 Announce Type: new Abstract: Curvilinear object segmentation, including vessels and cracks, is challenging due to extreme spatial sparsity and topological fragility, where small loc
arXiv:2606.13102v2 Announce Type: replace Abstract: Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups.
arXiv:2603.01501v2 Announce Type: replace Abstract: Asynchronous execution is essential for scaling reinforcement learning (RL) to modern large model workloads, including large language models and AI
arXiv:2606.22688v1 Announce Type: cross Abstract: Self-play with naive gradient ascent cycles in two-player zero-sum games: the last iterate orbits the equilibrium. Modern methods restore last-iterate
arXiv:2505.00909v3 Announce Type: replace Abstract: In this paper, we propose a Gaussian Process (GP)-based policy iteration framework for addressing both forward and inverse problems in Hamilton--Jac
arXiv:2606.20871v1 Announce Type: new Abstract: We study how trajectory-shape diversity in demonstrations affects imitation learning (IL) performance across models, tasks, and data scales. We introduc
arXiv:2509.09597v3 Announce Type: replace-cross Abstract: Graph alignment, the problem of identifying corresponding nodes across multiple graphs, is fundamental to numerous applications. Most existing
arXiv:2606.21368v1 Announce Type: new Abstract: Medical image re-identification (MedReID) enables longitudinal patient linkage but remains vulnerable to shortcut learning and often produces decisions
arXiv:2606.22995v1 Announce Type: new Abstract: Group-based Reinforcement Learning (RL) has significantly enhanced Large Language Models (LLMs) in agentic scenarios. To achieve finer-grained policy up
arXiv:2606.21915v1 Announce Type: new Abstract: Automated chest X-ray report generation requires precise cross-modal grounding to ensure clinically reliable descriptions. However, existing vision-lang
arXiv:2602.00032v3 Announce Type: replace-cross Abstract: Synthetic faces from text-to-image (T2I) models pervade digital media, yet their demographic biases under emotionally conditioned prompts rema
arXiv:2508.15555v4 Announce Type: replace-cross Abstract: HEAS is a Python framework that connects agent-based simulation, evolutionary search, and scenario-based evaluation in a single reproducible p
OpenAI discusses its efforts to contribute to the development of shared industry standards and best practices for advanced artificial intelligence systems. The article likely covers OpenAI's involveme
arXiv:2606.20962v1 Announce Type: new Abstract: High-performing human-human teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams
arXiv:2606.22922v1 Announce Type: new Abstract: Applying machine learning techniques to solving long-standing mathematical conjectures can be particularly challenging due to their extreme reward spars
arXiv:2606.22860v1 Announce Type: new Abstract: Recent advancements in generative imitation learning have significantly propelled the field of robotic manipulation. However, the majority of existing m
arXiv:2606.20189v2 Announce Type: replace Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data
arXiv:2606.23565v1 Announce Type: cross Abstract: LLM agents follow a practical execution loop in digital environments: they reason over structured states, invoke tools, inspect feedback, and revise a
arXiv:2606.21136v1 Announce Type: new Abstract: Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional tempor
arXiv:2510.17525v3 Announce Type: replace Abstract: Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on
arXiv:2509.17125v2 Announce Type: replace Abstract: Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric
arXiv:2606.21672v1 Announce Type: cross Abstract: Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale
arXiv:2601.22969v2 Announce Type: replace Abstract: Nash regret has recently emerged as a principled fairness-aware performance metric for stochastic multi-armed bandits, motivated by the Nash Social
arXiv:2606.22158v1 Announce Type: new Abstract: Achieving human-like reasoning in Vision-Language Models (VLMs) remains a long-standing challenge. Recent approaches leverage Chain-of-Thought (CoT) rat
arXiv:2606.22907v1 Announce Type: cross Abstract: Imitation learning for robotic manipulation relies on large sets of human demonstration trajectories, which are often noisy and temporally irregular d
arXiv:2606.20999v1 Announce Type: cross Abstract: Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models l
arXiv:2405.10231v3 Announce Type: replace-cross Abstract: Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in
'Instead of allowing for greater focus, the latest AI tools are overwhelming workers, frazzling minds and shredding attention spans.' https://www.theatlantic.com/technology/2026/06/ai-agents-jobs-exha
arXiv:2606.21970v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models, such as Moshi, enable natural, low-latency voice conversations. However, they remain limited to the audio modality
arXiv:2503.03579v2 Announce Type: replace-cross Abstract: Spoken instructions in robot-to-human handovers may specify either an object ('the cup') or an intended use ('pour water'); in both cases, suc
arXiv:2606.23177v1 Announce Type: new Abstract: Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While pr
arXiv:2606.21173v1 Announce Type: new Abstract: Model-based and model-free reinforcement learning are traditionally viewed as separate paradigms: instead of learning a model of the transition kernel P
arXiv:2606.21975v1 Announce Type: cross Abstract: Despite its massive player base and complex hidden-information dynamics, Indian Rummy has received no reinforcement learning attention. Existing agent
arXiv:2603.05916v2 Announce Type: replace Abstract: Obstacle avoidance of polytopic obstacles by polytopic robots is a challenging problem in optimization-based control and trajectory planning. Many e
arXiv:2606.22892v1 Announce Type: cross Abstract: The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'ex
arXiv:2606.20686v1 Announce Type: new Abstract: Shared-space transportation operation requires low-speed autonomous platforms to navigate safely and efficiently among pedestrians, service robots, micr
arXiv:2606.22113v1 Announce Type: new Abstract: Generalizing manipulation policies across robot embodiments remains difficult because standard policies entangle task reasoning with embodiment-specific