Knowledge catalogue
arXiv:2606.05345v2 Announce Type: replace Abstract: We organize relative-position mechanisms in attention as a learnable Fourier-Jet-Affine position space. The starting point is lag-shift dynamics: a
arXiv:2606.20648v1 Announce Type: cross Abstract: Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and conn
arXiv:2606.21139v1 Announce Type: cross Abstract: Latent action pretraining learns representations of visual change from pairs of observations, but existing methods typically encode each transition as
arXiv:2606.22806v1 Announce Type: new Abstract: Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current d
arXiv:2606.22540v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execut
arXiv:2606.21148v1 Announce Type: new Abstract: Functional robotic grasping requires a policy that generalizes across diverse object geometries and poses while maintaining task-specific contact precis
arXiv:2606.23634v1 Announce Type: new Abstract: Estimating the 6D pose of unseen objects is a fundamental yet challenging problem for open-world robotics and embodied perception. Model-based methods a
arXiv:2606.20118v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distr
arXiv:2408.06350v2 Announce Type: replace-cross Abstract: One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving.
arXiv:2606.21994v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by applying dense teacher supervision on student-sampled trajectories. However, scaling OPD to lo
arXiv:2606.21737v1 Announce Type: new Abstract: Magnetically programmable soft elastomers enable complex shape morphing and locomotion dynamics in small scale soft robots under external magnetic field
arXiv:2606.22056v1 Announce Type: new Abstract: Adversarial imitation learning (AIL) achieves high-quality imitation compared to behavioral cloning (BC), but demands substantial online environment int
arXiv:2606.23593v1 Announce Type: cross Abstract: Robot-assisted minimally invasive surgery improves surgical precision but introduces complexity, making technical error detection essential for ensuri
arXiv:2606.23617v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for task
arXiv:2606.21238v1 Announce Type: cross Abstract: Key-value (KV) caching is a powerful technique for accelerating large language model inference and generation. Inference workloads are large and diver
arXiv:2606.21086v1 Announce Type: new Abstract: We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for effi
arXiv:2606.22108v1 Announce Type: cross Abstract: Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic signal syst
arXiv:2601.03357v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a standard approach to reconstruct and render photorealistic 3D head avatars. A major challenge is to religh
arXiv:2606.21188v1 Announce Type: new Abstract: Long horizon, contact-rich manipulation is inherently partially observable. This is as a single visual observation rarely captures a robot's full action
arXiv:2606.23436v1 Announce Type: new Abstract: Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent app
arXiv:2606.22142v1 Announce Type: new Abstract: We present RoboLineage, an agent-native data lifecycle governance system for robot policy iteration. Modern robot policies improve through repeated data
arXiv:2606.21572v1 Announce Type: new Abstract: Large vision-language models contain several priors about the world and object interactions, making them useful critics during inference to steer robot
arXiv:2606.21406v1 Announce Type: cross Abstract: A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, a
arXiv:2602.03531v2 Announce Type: replace-cross Abstract: Masked Autoencoders (MAEs) achieve impressive performance in image classification tasks, yet the internal representations they learn remain le
arXiv:2606.21788v1 Announce Type: cross Abstract: Point-cloud goals provide a direct way to specify dexterous in-hand reorientation: instead of defining an object-specific pose frame or estimating 6D
arXiv:2511.20651v2 Announce Type: replace Abstract: Reinforcement learning (RL) has recently emerged as a promising approach for aligning text-to-image generative models with human preferences. A key
arXiv:2606.23267v1 Announce Type: new Abstract: Flow matching has recently emerged as a strong paradigm for state-of-the-art text-to-image (T2I) generation, enabling high-quality generation with a sma
arXiv:2603.05497v3 Announce Type: replace Abstract: Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around
arXiv:2606.20698v1 Announce Type: new Abstract: Safe control is a prerequisite for real-world embodied intelligence, for which safe reinforcement learning has emerged as a promising paradigm. However,
arXiv:2606.22144v1 Announce Type: new Abstract: Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions & lif
arXiv:2606.19641v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and oft
arXiv:2606.23019v1 Announce Type: new Abstract: While Diffusion Transformers (DiTs) have revolutionized high-fidelity video generation, their reliance on 3D full attention creates a quadratic computat
arXiv:2606.23567v1 Announce Type: new Abstract: Masked diffusion language models decode by iteratively unmasking tokens, where the unmasking order defines an 'order of thought' that strongly influence
arXiv:2606.13007v2 Announce Type: replace Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. Howev
arXiv:2606.21300v1 Announce Type: new Abstract: We present SCOPE (Scale-Consistent One-Pass Estimation of 3D Geometry), a novel approach for estimating 3D geometry from extended monocular video sequen
arXiv:2606.15171v2 Announce Type: replace Abstract: Seams encode rich structural information about garments but are frequently partially observable in robotic manipulation scenarios. To robustly lever
arXiv:2606.19120v2 Announce Type: replace-cross Abstract: On-policy self-distillation (OPSD) trains a model on its own rollouts and uses a frozen copy to provide dense token-level targets conditioned
arXiv:2602.21816v2 Announce Type: replace Abstract: Precise shape control of Deformable Linear Objects (DLOs) is crucial in robotic applications such as industrial and medical fields. However, existin
arXiv:2606.21493v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable robots to predict actions directly from visual observations and language instructions, but adapting them to n
arXiv:2505.11494v3 Announce Type: replace Abstract: Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring d
arXiv:2606.20857v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to execute manipulation tasks from natural-language instructions grounded in visual observations. Ho
arXiv:2606.20636v1 Announce Type: cross Abstract: Computer-Use Agents (CUAs) are increasingly deployed in dynamic interactive environments, creating a growing need for continual skill learning during
arXiv:2606.23575v1 Announce Type: new Abstract: Hyperparameter tuning almost always means search: fit the model at every value on a grid, score each by cross-validation, and keep the winner. For splin
New York Times: Sources: the Trump administration is pressing Meta to submit its AI models for voluntary review; Meta is the only major US AI developer without an agreement — Federal officials are urg
arXiv:2606.20520v2 Announce Type: replace-cross Abstract: Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not re
arXiv:2606.23041v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in visual understanding but remain constrained in visual generation due to the
arXiv:2601.20209v2 Announce Type: replace Abstract: Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challengin
arXiv:2606.23129v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have been proven successful in encoding continuous signals through coordinate-based networks, yet facing a spectr
arXiv:2606.22443v1 Announce Type: new Abstract: Compliant mechanisms are important in robotics because they can improve adaptability, safety, and energy efficiency while reducing hardware complexity.
arXiv:2606.23537v1 Announce Type: cross Abstract: Text-to-SQL enables users to access relational databases via natural language, but real-world settings remain challenging due to coordinated reasoning
arXiv:2601.22679v2 Announce Type: replace-cross Abstract: Consistency models have been proposed for fast generative modeling, achieving results competitive with diffusion and flow models. However, the
arXiv:2606.20197v2 Announce Type: replace Abstract: Actor-Critic Model Predictive Control (MPC) effectively addresses complex, non-convex control problems, but guaranteeing the closed-loop stability o
arXiv:2606.22579v1 Announce Type: new Abstract: Deploying multi-agent reinforcement learning (MARL) in the real world is often limited by model mismatches between the training simulators and the true
arXiv:2606.22639v1 Announce Type: cross Abstract: Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment. Yet these advantages
arXiv:2606.23166v1 Announce Type: new Abstract: There has been rapid progress in generative artificial intelligence (AI) models for inorganic crystal design, which can efficiently generate large numbe
arXiv:2606.23348v1 Announce Type: new Abstract: We present a superhuman AI agent for Generals.io, a real-time strategy game that requires both long-horizon planning and short-term tactics under strong
arXiv:2606.21866v1 Announce Type: new Abstract: Co-design of legged robots with elastic elements is challenging due to the non-differentiability of contact dynamics and mechanism engagement. This pape
arXiv:2606.15623v2 Announce Type: replace Abstract: Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons
arXiv:2606.21446v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) aims to classify old categories and discover new ones from unlabeled data. Recent multi-modal approaches introduce
arXiv:2606.20645v1 Announce Type: new Abstract: Foothold selection on structured terrain requires explicit reasoning about contact planarity, surface steepness, and kinematic reachability, properties