Knowledge catalogue
arXiv:2606.20627v1 Announce Type: cross Abstract: Planning with world models is bottlenecked by compounding prediction errors and the difficulty of defining optimizable goals. Visual targets provide p
arXiv:2410.05985v4 Announce Type: replace Abstract: The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur
arXiv:2604.05648v2 Announce Type: replace Abstract: We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the Laplacian matrix used
arXiv:2506.02849v3 Announce Type: replace-cross Abstract: In this letter we study 1v1 quadrotor pursuit-evasion, where a pursuer and an evader are trained via reinforcement learning (RL) by competing
arXiv:2606.22261v1 Announce Type: new Abstract: Abnormality detection in complex systems faces two practical barriers: abnormal labels are scarce, and binary labels do not quantify how far an event ha
arXiv:2606.22801v1 Announce Type: new Abstract: Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong p
arXiv:2606.22831v1 Announce Type: cross Abstract: This paper studies learning-augmented online weighted vertex cover with advice and a parameter lambda in (0,1). We consider two graph cases: bipartite
arXiv:2606.20958v1 Announce Type: new Abstract: Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order prin
arXiv:2510.09898v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance on code translation between widely used programming languages. However, translation becom
arXiv:2606.21957v1 Announce Type: new Abstract: Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise. Self-supervised l
arXiv:2502.15376v2 Announce Type: replace Abstract: Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning probl
arXiv:2606.20660v1 Announce Type: new Abstract: Soft robots are valuable robophysical platforms for studying body-caudal undulatory locomotion, but their compliant bodies are difficult to control prec
arXiv:2606.22076v1 Announce Type: new Abstract: Single-reference unseen object 6D pose estimation reduces object onboarding by estimating poses of arbitrary novel objects from only one reference view.
arXiv:2606.22634v1 Announce Type: new Abstract: Learning Entropy (LE) has recently been extended to image analysis through Spatial Learning Entropy Maps (SLEMs), which are two-dimensional LE distribut
arXiv:2411.19468v4 Announce Type: replace Abstract: The random feature (RF) method is a powerful kernel approximation technique, but it typically uses fixed activation functions, limiting its adaptabi
arXiv:2606.22895v1 Announce Type: new Abstract: Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This
arXiv:2606.23640v1 Announce Type: new Abstract: In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given every
arXiv:2606.23027v1 Announce Type: new Abstract: Feed-forward Gaussian splatting (FFGS) facilitates real-time novel view synthesis, yet current methods often remain tied to view-dependent predictions.
arXiv:2506.01882v3 Announce Type: replace-cross Abstract: The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and othe
arXiv:2606.20937v1 Announce Type: new Abstract: We study internalization processes, by which neural-network-based systems absorb an explicit computational procedure into their own weights, and how the
arXiv:2606.21604v1 Announce Type: new Abstract: Neural combinatorial optimization (NCO) has shown that policies trained by reinforcement can construct strong solutions to NP-hard problems directly fro
arXiv:2606.23625v1 Announce Type: new Abstract: Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both searc
arXiv:2602.09402v2 Announce Type: replace Abstract: We study the problem of learning with multiple correct answers, where each instance admits a set of valid labels. We primarily focus on the online s
arXiv:2606.23246v1 Announce Type: new Abstract: Incidents in chemical plants can pose a high level of risk and harsh environments for first responders. Contamination and explosion hazards can deny hum
arXiv:2606.23208v1 Announce Type: new Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose
arXiv:2606.21718v1 Announce Type: cross Abstract: Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural contexts where intergroup hostility is prev
arXiv:2606.23414v1 Announce Type: new Abstract: In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags,
arXiv:2606.21358v1 Announce Type: new Abstract: Supervised video pretraining is a common transfer learning practice for improving downstream action recognition performance. However, it requires large-
arXiv:2606.23686v1 Announce Type: new Abstract: Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains large
arXiv:2606.23688v1 Announce Type: new Abstract: Reconstructing dynamic non-rigid objects from monocular video requires integrating visual cues from direct observations with data-driven priors over geo
arXiv:2606.21564v1 Announce Type: new Abstract: Transformers achieve strong performance, but their internal computations remain opaque. We view each Transformer layer as a dynamic graph whose nodes ar
arXiv:2606.22481v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is widely used to capture and render real scenes. Compositing objects from one capture into another has applications in m
arXiv:2412.05976v2 Announce Type: replace Abstract: Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong ge
arXiv:2606.23539v1 Announce Type: new Abstract: Visual document retrieval requires rapidly locating relevant pages from large multi-modal corpora in response to user queries. While recent methods powe
arXiv:2606.21292v1 Announce Type: new Abstract: We present Casper3D, a lightweight probabilistic framework for converting noisy multi-view 2D foundation-model embeddings into a latent 3D semantic repr
arXiv:2606.23653v1 Announce Type: new Abstract: Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods
arXiv:2606.22499v1 Announce Type: cross Abstract: This study presents the hardware and software architecture of a transformative system for illuminating line drawings and letterforms. These mid-air il
arXiv:2606.21018v1 Announce Type: cross Abstract: As artificial intelligence advances into the era of Embodied AI, live musical interaction urgently needs to break free from the limitations of offline
arXiv:2606.23136v1 Announce Type: cross Abstract: Finding the shortest path in non-geometric network graphs, where edge weights encode arbitrary metrics such as latency or monetary cost rather than sp
arXiv:2511.22354v2 Announce Type: replace Abstract: This paper introduces CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture for heterogeneous robot teams that unifi
arXiv:2606.20729v1 Announce Type: cross Abstract: Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed app
arXiv:2606.22013v1 Announce Type: new Abstract: Machine learning (ML) model serving has become a dominant consumer of GPU infrastructure, yet capacity planning in these systems remains largely ad hoc.
arXiv:2606.21821v1 Announce Type: new Abstract: Understanding the causal structure of a language model's thought process is a problem of significant importance for both transparency and safety. In thi
arXiv:2410.02548v4 Announce Type: replace-cross Abstract: Flow Matching (FM) is a simulation-free method for learning a continuous, invertible flow that interpolates between two distributions, and in
arXiv:2603.14343v2 Announce Type: replace Abstract: Large Audio-Language Models (LALMs) have shown strong performance in speech understanding, making speech a natural interface for accessing factual i
arXiv:2606.22772v1 Announce Type: new Abstract: Lip-syncing deepfakes are among the most challenging forms of manipulated media because their artifacts are localized almost exclusively to the mouth re
arXiv:2606.21527v1 Announce Type: cross Abstract: Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the ro
arXiv:2606.23110v1 Announce Type: cross Abstract: Outer-loop link adaptation (OLLA) is widely deployed in 5G NR to track channel variations, yet its reliance on first-order, single-bit feedback degrad
arXiv:2606.21387v1 Announce Type: new Abstract: Legged-wheeled robots have long been studied for their potential to combine the efficient flat-ground mobility of wheels with the rough-terrain capabili
arXiv:2606.21968v1 Announce Type: new Abstract: Vision-Language Models (VLMs) struggle as query-relevant objects become smaller. To address this, recent training-free approaches dynamically retrieve a
arXiv:2606.22565v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thi
arXiv:2606.13912v2 Announce Type: replace-cross Abstract: Complex neural quantum states are difficult to optimize when their wavefunction phase carries gauge, chiral, fermionic, or topological structu
arXiv:2606.23249v1 Announce Type: new Abstract: Humanoid local navigation in cluttered environments must jointly resolve obstacle avoidance, sparse-goal recovery, and stable whole-body locomotion unde
arXiv:2606.22662v1 Announce Type: new Abstract: Forecasting chaotic dynamical systems such as the Lorenz attractor is notoriously difficult: small numerical errors are amplified exponentially over lon
arXiv:2606.23118v1 Announce Type: new Abstract: Low-light human action recognition remains a challenging problem due to poor illumination, amplified noise, motion ambiguity, and diverse real-world sce
arXiv:2509.23729v3 Announce Type: replace Abstract: Large Language Models (LLMs) with multimodal capabilities have revolutionized vision-language tasks, but their deployment often requires huge memory
arXiv:2304.12319v2 Announce Type: replace-cross Abstract: Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distort
arXiv:2606.20874v1 Announce Type: new Abstract: Cryopathy syndromes are difficult to classify because laboratory patterns often overlap across diagnostic categories, while some diagnoses are rare. Thi
arXiv:2606.20641v1 Announce Type: cross Abstract: Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them
arXiv:2412.20034v2 Announce Type: replace Abstract: Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environ