Knowledge catalogue
arXiv:2609.08841v1 Announce Type: new Abstract: High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail s
arXiv:2609.07274v1 Announce Type: cross Abstract: SLAM systems based on 3D Gaussian Splatting (3DGS) have recently demonstrated promising reconstruction accuracy for dense 3D scene representations. Ho
arXiv:2512.13271v2 Announce Type: replace Abstract: Cable-driven continuum robots (CDCRs) require accurate, real-time dynamic models for high-speed dynamics prediction or model-based control, making s
arXiv:2609.06686v1 Announce Type: cross Abstract: We present an unsupervised search for anomalous dijet events in proton--proton collision data using neural spline flow density estimation. A normalizi
arXiv:2609.08585v1 Announce Type: cross Abstract: Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs
arXiv:2609.06341v1 Announce Type: new Abstract: Linear algebra provides the framework of concepts (matrix rank, singular value decomposition (SVD), and eigendecomposition) that modern artificial intel
arXiv:1906.02590v2 Announce Type: replace-cross Abstract: This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods
arXiv:2609.05559v1 Announce Type: cross Abstract: We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer's CAD model, the method returns a v
arXiv:2609.07876v1 Announce Type: cross Abstract: Recent methods in language model interpretability employ techniques such as sparse autoencoders to decompose residual stream contributions into linear
arXiv:2609.06467v1 Announce Type: new Abstract: In performative reinforcement learning the deployed policy shapes the environment that generates the learner's future data, and the natural solution con
arXiv:2609.07752v1 Announce Type: new Abstract: Field temporal prediction and source identification constitute canonical problems in dynamical systems. Conventional approaches to these problems depend
arXiv:2609.08408v1 Announce Type: new Abstract: Focusing on spatially localized, control-relevant visual cues has been shown to improve data efficiency in visuomotor policies by reducing the need to m
arXiv:2609.07029v1 Announce Type: new Abstract: Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from
arXiv:2609.05955v1 Announce Type: new Abstract: Tabular foundation models have become powerful graph learners. Systems such as G2T-FM and GraphPFN encode each node as a feature row and make prediction
arXiv:2609.07303v1 Announce Type: new Abstract: Current paradigms for training language models via reinforcement learning rely heavily on sparse outcome rewards. However, as we pursue tasks that requi
arXiv:2602.12351v2 Announce Type: replace-cross Abstract: This paper develops LongNav-R1, an end-to-end multi-turn reinforcement learning (RL) framework designed to optimize Visual-Language-Action (VL
arXiv:2601.21873v2 Announce Type: replace Abstract: Learning systems often expand their ambient features or latent representations over time, embedding earlier representations into larger spaces with
arXiv:2609.07859v1 Announce Type: new Abstract: Executing contact-rich tasks efficiently requires the seamless integration of whole-body coordination and physical compliance regulation. However, exist
arXiv:2609.07116v1 Announce Type: new Abstract: Visual-Inertial (VI) fusion is fundamental to accurate and robust state estimation, where camera and IMU measurements are combined according to their re
arXiv:2609.05970v1 Announce Type: new Abstract: Empiric antibiotic therapy for suspected ESBL-producing Enterobacteriaceae must be selected 48-72 hours before culture results, forcing clinicians to ch
arXiv:2609.08041v1 Announce Type: new Abstract: Many pedestrian trajectory prediction algorithms have been proposed to improve the safety of navigation for mobile robots working in human-robot coexist
arXiv:2609.05841v1 Announce Type: new Abstract: Language-goal aerial navigation requires an agent to local- ize a potentially unobserved target from relational instruc- tions and partial observations,
arXiv:2609.06563v1 Announce Type: cross Abstract: Social deduction games (SDGs) require agents to reason under partial observability by maintaining relational beliefs about hidden roles and team align
arXiv:2609.08084v1 Announce Type: new Abstract: Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computationa
arXiv:2609.06645v1 Announce Type: new Abstract: All-in-one medical image restoration seeks to recover heterogeneous clinical images with a single model, but PET, CT, and MRI differ substantially in de
arXiv:2609.08283v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in zero-shot transfer but often struggles with dense prediction
arXiv:2609.09123v1 Announce Type: new Abstract: Autoregressive (AR) video diffusion models have shown great potential in real-time video generation. Recent methods distill pretrained bidirectional vid
arXiv:2609.07428v1 Announce Type: cross Abstract: In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes o
arXiv:2609.07276v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) depends on dense retrieval: each document is stored as a learned vector, and a query is answered by finding its n
arXiv:2509.04757v2 Announce Type: replace Abstract: Hurricanes cause widespread damage to buildings, roads, and other infrastructure, making timely post-disaster damage assessment critical for emergen
arXiv:2509.14191v4 Announce Type: replace-cross Abstract: Recent progress in dense SLAM has primarily targeted monocular setups, often at the expense of robustness and geometric coverage. We present M
arXiv:2609.07470v1 Announce Type: new Abstract: Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most languages. We study th
arXiv:2601.06847v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We posit tha
arXiv:2609.06874v1 Announce Type: cross Abstract: High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and fo
arXiv:2609.08879v1 Announce Type: new Abstract: Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical que
arXiv:2609.06615v1 Announce Type: new Abstract: Behavior-cloned visuomotor policies can remain accurate near their training distribution yet fail when object position and camera viewpoint change toget
arXiv:2609.07047v1 Announce Type: new Abstract: Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when the
arXiv:2609.06006v1 Announce Type: new Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state-sp
arXiv:2609.07966v1 Announce Type: new Abstract: Key--value (KV) cache compression is an effective way to reduce the memory overhead of large language model (LLM) inference, particularly for long-conte
arXiv:2609.06396v1 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet R
arXiv:2609.08626v1 Announce Type: cross Abstract: A simultaneous localization and mapping (SLAM) method using a monocular camera and a low-cost inertial measurement unit (IMU) sensor is an effective w
arXiv:2602.17211v2 Announce Type: replace-cross Abstract: Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide prin
arXiv:2609.08059v1 Announce Type: cross Abstract: Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related bioproc
arXiv:2506.19947v2 Announce Type: replace-cross Abstract: Channel hopping (CS) communication systems must adapt to interference changes in the wireless network and to node mobility for maintaining thr
arXiv:2609.08368v1 Announce Type: new Abstract: We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each sta
arXiv:2609.07155v1 Announce Type: new Abstract: Model compression is key to mitigate deployment challenges of ever growing machine learning models. In this area of research, singular value decompositi
arXiv:2507.08867v3 Announce Type: replace-cross Abstract: Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics
arXiv:2609.06958v1 Announce Type: new Abstract: Reinforcement learning has become the leading paradigm in legged locomotion, enabling complex behaviors from backflips to parkour through massively para
arXiv:2512.04313v2 Announce Type: replace Abstract: Current expressive avatar systems rely heavily on visual cues, failing when faces are occluded or when emotions remain internal. We present Mind-to-
arXiv:2609.06042v1 Announce Type: new Abstract: This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and im
arXiv:2601.04058v3 Announce Type: replace Abstract: We address the problem of classifying trajectories or sequences generated by nonlinear dynamical systems, where each class corresponds to a distinct
arXiv:2609.07504v1 Announce Type: new Abstract: Standard Convolutional Neural Networks (CNNs) exhibit severe performance degradation due to a strong inductive texture bias that prioritizes local, high
arXiv:2603.12530v3 Announce Type: replace Abstract: Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts ar
arXiv:2609.08010v1 Announce Type: new Abstract: This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-spac
arXiv:2609.08399v1 Announce Type: new Abstract: Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations. Benchmarks enable reproducible evaluation b
arXiv:2609.06116v1 Announce Type: cross Abstract: In the field of UI design, Scalable Vector Graphics (SVG) is widely used as a design medium. However, traditional SVG editing techniques have high ent
arXiv:2609.05992v1 Announce Type: cross Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task comple
arXiv:2609.06251v1 Announce Type: new Abstract: Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on
arXiv:2512.03491v3 Announce Type: replace Abstract: Neural Networks are indispensable to natural sciences and society. Their impact extends from applications in public health to workforce productivity
arXiv:2609.06514v1 Announce Type: new Abstract: Adaptive frequency hopping against predictive jamming must address both model uncertainty and policy exposure: the context-loss relationship may vary ac