Knowledge catalogue
arXiv:2610.16273v1 Announce Type: cross Abstract: Speech timing is fundamental for speech models to recognize words accurately, but how word-timing information is represented in speech models remains
arXiv:2610.17316v1 Announce Type: cross Abstract: Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These meth
arXiv:2610.17751v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) are commonly organized around two coordination regimes. Centralized coordination provides e
arXiv:2610.14710v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language unders
arXiv:2606.13106v2 Announce Type: replace Abstract: Latent chain-of-thought compresses reasoning through continuous hidden-state recurrence, but existing approaches are difficult to optimize with stan
arXiv:2610.18809v1 Announce Type: new Abstract: Residual connections propagate information through a network by additively accumulating layer outputs. Recently proposed alternatives, such as Manifold-
arXiv:2610.14436v1 Announce Type: cross Abstract: Multimodal learning offers the potential to improve predictions by integrating complementary information from heterogeneous data sources. However, mul
arXiv:2610.16463v1 Announce Type: new Abstract: Vision-based tactile sensors enable fine-grained perception for dexterous robotic manipulation, but current marker-based designs tightly couple surface
arXiv:2610.14691v1 Announce Type: new Abstract: Recent advances in 3D hand-object interaction (HOI) perception have opened new opportunities for learning dexterous robotic skills from human videos. Ho
arXiv:2610.17208v1 Announce Type: cross Abstract: Cutting a piece of food looks effortless in human hands. For a robot, however, every cut is a high-contact interaction with a deformable, elasto-plast
arXiv:2610.17789v1 Announce Type: cross Abstract: A long-horizon manipulation task consists of primitive skills and the transitions connecting them. Learning skills that can be executed in sequence re
arXiv:2610.17882v1 Announce Type: new Abstract: Flow-based image generators achieve strong semantic synthesis but still produce localized structural distortions, such as malformed hands and limbs. Exi
arXiv:2502.05505v4 Announce Type: replace-cross Abstract: Differentially private (DP) synthetic data enables the use of sensitive datasets while providing strong privacy guarantees. Recently, Private
arXiv:2610.14999v1 Announce Type: cross Abstract: Steering candidate generation toward user-defined objectives is central to inverse materials design, yet remains difficult when objectives are black-b
arXiv:2610.15932v1 Announce Type: new Abstract: Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something convent
arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more
arXiv:2610.14159v1 Announce Type: cross Abstract: Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited
arXiv:2602.00865v2 Announce Type: replace Abstract: While multi-view 3D reconstruction has shifted toward large-scale foundation models capable of inferring globally consistent geometry, their relianc
arXiv:2610.17968v1 Announce Type: new Abstract: Diffusion MRI (dMRI) probes water diffusion within the brain, providing unique information about tissue microstructure and white matter connectivity. Hi
arXiv:2610.14064v1 Announce Type: new Abstract: Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-
arXiv:2605.25437v2 Announce Type: replace Abstract: Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-so
arXiv:2610.18870v1 Announce Type: new Abstract: We generally perceive that training text-to-image diffusion models requires heavy compute: it is true if we aim at a comprehensive frontier model. This
arXiv:2610.14376v1 Announce Type: new Abstract: Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available. However, adaptation objectiv
arXiv:2603.00466v2 Announce Type: replace Abstract: Despite impressive progress in video generation, existing models remain limited to surface-level plausibility, lacking a coherent and unified unders
arXiv:2610.17903v1 Announce Type: new Abstract: Vision-Language Models (VLMs) and world models have demonstrated strong capabilities in semantic understanding and future scene evolution for autonomous
arXiv:2607.17291v2 Announce Type: replace Abstract: Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleadi
arXiv:2610.14945v1 Announce Type: new Abstract: Visual localization enables unmanned aerial vehicles (UAVs) to maintain absolute positioning in GNSS-denied environments. A common approach matches UAV
arXiv:2610.14499v1 Announce Type: new Abstract: 3D human pose estimation requires accurate modeling of motion-dependent skeletal structures and inter-joint dependencies. Existing methods typically rel
arXiv:2610.14630v1 Announce Type: new Abstract: Dual-lens super-resolution (DuSR) aims to enhance a wide-angle low-resolution (LR) image by exploiting a telephoto reference (Ref) image. A central chal
arXiv:2610.17853v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deploy
arXiv:2603.16157v2 Announce Type: replace Abstract: While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard pas
arXiv:2610.14624v1 Announce Type: new Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive secto
arXiv:2610.16523v1 Announce Type: cross Abstract: Training LLM agents to interact with users over multiple turns is challenging because the learning signal depends not only on the agent, but also on t
arXiv:2610.17718v1 Announce Type: cross Abstract: Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a re
arXiv:2610.16433v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) provide general-purpose forecasting capabilities, yet their forecasts can remain misaligned with the temporal char
arXiv:2610.15934v1 Announce Type: new Abstract: Motion planning for automated vehicles in interactive traffic has to simultaneously ensure safety, respect traffic rules, and maintain comfortable progr
arXiv:2610.16343v1 Announce Type: cross Abstract: Multi-agent debate has shown promise for improving the reasoning of large language models, yet recent theory suggests its benefits are highly regime-d
arXiv:2510.11068v4 Announce Type: replace Abstract: Real-world deployment often exposes vision models to distribution shifts, making test-time adaptation (TTA) important for reliable prediction. Exist
arXiv:2610.17572v1 Announce Type: new Abstract: While expert demonstrations on real robots are difficult to collect, human ego-centric videos are easy to collect and abundant on the internet. However,
arXiv:2610.16118v1 Announce Type: new Abstract: Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe o
arXiv:2610.15580v1 Announce Type: new Abstract: This paper presents an algorithm for detecting and fitting multiple ellipsoids in 2D, 3D and higher-dimensional data. The representation is based on tan
arXiv:2512.01495v3 Announce Type: replace Abstract: Video instance segmentation (VIS) for low-light content remains highly challenging for both humans and machines alike, due to noise, blur and other
arXiv:2606.12965v2 Announce Type: replace Abstract: Scalable robot imitation learning relies on large-scale heterogeneous data from diverse robots or body-free data, making Cartesian end-effector acti
arXiv:2610.15452v1 Announce Type: new Abstract: Text-to-video (T2V) models are increasingly used to generate videos with targeted emotional expressions, yet existing evaluation methods mainly rely on
arXiv:2610.17686v1 Announce Type: cross Abstract: Longer contexts allow large language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with con
arXiv:2610.15111v1 Announce Type: cross Abstract: Data and the governing partial differential equations (PDEs) of continuous-time dynamical systems provide rich structure for statistical learning. A c
arXiv:2610.14940v1 Announce Type: new Abstract: Lifelong learning enables artificial systems to adapt to changing environments. To achieve this, an effective artificial learner should dynamically reor
arXiv:2610.18790v1 Announce Type: new Abstract: Long-term robot autonomy depends on more than effective action generation: a policy must recognize when its behavior is no longer sufficiently supported
arXiv:2610.16411v1 Announce Type: new Abstract: Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Cla
arXiv:2610.17326v1 Announce Type: new Abstract: Household robots must successfully navigate im-perfect circumstances, both in their internal models and the external world. We introduce a benchmark of
arXiv:2610.16879v1 Announce Type: new Abstract: The rapid spread of face forgeries on the Web challenges the credibility of visual content. In practical Web environments, manipulated media originate f
arXiv:2610.16679v1 Announce Type: new Abstract: Tool-augmented vision-language agents compose perception and measurement to reason about spatial relationships, but effective execution also requires tr
arXiv:2511.20088v3 Announce Type: replace Abstract: In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanat
arXiv:2610.15241v1 Announce Type: cross Abstract: Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric cond
arXiv:2610.16329v1 Announce Type: new Abstract: Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benef
arXiv:2604.02973v2 Announce Type: replace Abstract: Text-driven human motion generation aims to synthesize realistic motion sequences that follow textual descriptions. Despite recent advances, accurat
arXiv:2610.15425v1 Announce Type: new Abstract: In embodied manipulation, Vision Language Action (VLA) models can execute atomic skills, but long-horizon manipulation requires recombining these skills
arXiv:2610.18082v1 Announce Type: new Abstract: Long-horizon agents often struggle to recover from failures whose causes lie several steps earlier, beyond what local reflection or backtracking can res
arXiv:2610.18260v1 Announce Type: new Abstract: Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Exi
arXiv:2610.17841v1 Announce Type: cross Abstract: Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queri