Knowledge catalogue
arXiv:2606.05536v1 Announce Type: new Abstract: Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detec
arXiv:2606.05669v1 Announce Type: new Abstract: Robotic Cellular Warehousing Systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple
arXiv:2601.18383v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer. H
arXiv:2606.06379v1 Announce Type: new Abstract: Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generatio
arXiv:2606.06350v1 Announce Type: new Abstract: Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student a
arXiv:2606.05372v1 Announce Type: new Abstract: Vector-field-based methods are widely used for robot control and are often applied to the path-tracking problem. Some vector field approaches require re
arXiv:2606.06329v1 Announce Type: cross Abstract: Estimating local mean curvature at each point of a high-dimensional dataset is a key ingredient of geometry-aware machine learning algorithms, such as
arXiv:2606.05179v1 Announce Type: new Abstract: Punctuation restoration improves ASR (Automatic Speech Recognition) readability. However streaming ASR requires online decisions with limited future con
arXiv:2605.24496v2 Announce Type: replace Abstract: The EPIC-KITCHENS-100 Action Detection challenge evaluates whether a model can localize the start and end of each action in long untrimmed egocentri
arXiv:2605.24500v2 Announce Type: replace Abstract: This technical report presents our solution, EgoAdapt (Egocentric Adaptation via Category, Calibration, and Consistency), to the CVPR 2026 HD-EPIC V
arXiv:2602.10106v2 Announce Type: replace Abstract: Human demonstrations offer rich environmental diversity and scale naturally, making them an appealing alternative to robot teleoperation. While this
arXiv:2606.06025v1 Announce Type: new Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Lar
arXiv:2606.05894v1 Announce Type: new Abstract: Long-horizon agents can archive large histories, but future answers still incur retrieval, rereading, and context costs. When retained memory misses ans
arXiv:2606.06380v1 Announce Type: new Abstract: The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-der
arXiv:2606.05816v1 Announce Type: new Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns
arXiv:2606.06038v1 Announce Type: new Abstract: We study English-to-Prakrit machine translation in a low-resource setting where the target language is unsupported by IndicTrans2. We adapt the multilin
arXiv:2604.03042v2 Announce Type: replace Abstract: Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveilla
arXiv:2606.06370v1 Announce Type: new Abstract: Wearable exoskeletons can augment human phys ical capabilities during complex activities. However, ensuring adaptation across diverse tasks while guaran
arXiv:2606.05872v1 Announce Type: cross Abstract: AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of age
arXiv:2606.05168v1 Announce Type: new Abstract: Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation. In reality, the AI ecosystem involves cr
arXiv:2606.05513v1 Announce Type: cross Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming proces
arXiv:2606.05936v1 Announce Type: new Abstract: Modern language models rely on pretraining filters to remove undesirable content from training corpora and inference-time guardrails to suppress undesir
arXiv:2606.05874v1 Announce Type: new Abstract: Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-n
arXiv:2512.21430v2 Announce Type: replace Abstract: Visuomotor policies based on generative such as diffusion and flow-matching have shown strong performance for robotics applications but degrade unde
arXiv:2606.05415v1 Announce Type: new Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inco
arXiv:2510.17256v2 Announce Type: replace Abstract: Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a
arXiv:2512.15153v2 Announce Type: replace Abstract: Evaluating whether human action is standard or not and providing reasonable feedback to improve action standardization is very crucial but challengi
arXiv:2606.05760v1 Announce Type: new Abstract: Deepfake videos are increasingly challenging the credibility of online content. Many existing detection methodology relies on complex, resource-intensiv
arXiv:2511.10254v2 Announce Type: replace Abstract: Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. The task integrat
arXiv:2606.05949v1 Announce Type: new Abstract: Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts a
arXiv:2408.11336v3 Announce Type: replace-cross Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising
arXiv:2606.06211v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a sign
arXiv:2606.05254v1 Announce Type: cross Abstract: World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation b
arXiv:2606.06461v1 Announce Type: new Abstract: Leveraging prior knowledge from pretrained policies, foundation models, or human operators offers an efficient alternative to learning robot skills from
arXiv:2606.05468v1 Announce Type: new Abstract: Post-training Vision-Language-Action (VLA) models into policies that can be reliably deployed on real robots remains a major bottleneck. SFT and DAgger
arXiv:2606.06066v1 Announce Type: new Abstract: Typography generation in diffusion models faces a persistent trade-off: enabling precise font control typically degrades text legibility, while maintain
arXiv:2606.05857v1 Announce Type: new Abstract: Handling toxic retrieval in text-to-audio systems is challenging due to contextual dependencies. Existing strategies (e.g., rephrasing, summarization) r
arXiv:2606.05471v1 Announce Type: new Abstract: Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this
arXiv:2606.06271v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly used to generate writing feedback, there remains no systematic comparison of LLM and expert feedback
arXiv:2606.05983v1 Announce Type: cross Abstract: Generative AI makes answers easy and understanding hard, and uncritical use invites cognitive offloading. Schools still measure unaided performance, y
arXiv:2606.05180v1 Announce Type: new Abstract: Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts,
arXiv:2606.06266v1 Announce Type: new Abstract: Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough a
arXiv:2602.19190v4 Announce Type: replace Abstract: Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applicati
arXiv:2606.05829v1 Announce Type: new Abstract: Artistic styles are rooted in specific socio-historical contexts that encode social hierarchies, including distinct constructions of gender. Yet in AI r
arXiv:2606.05175v1 Announce Type: new Abstract: We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token inte
arXiv:2511.13183v2 Announce Type: replace Abstract: Tractography is the process of inferring the trajectories of white-matter pathways in the brain from diffusion magnetic resonance imaging (dMRI). Lo
arXiv:2606.06278v1 Announce Type: new Abstract: Blind image restoration requires recovering clean images from observations corrupted by unknown and potentially mixed degradations. While recent determi
arXiv:2606.05883v1 Announce Type: new Abstract: Dataset condensation aims to construct compact datasets from real data via synthesis or selection. However, existing approaches are ill-suited for diffu
arXiv:2606.05889v1 Announce Type: cross Abstract: We propose GLASS, a framework for composable acoustic style control in zero-shot autoregressive text-to-speech (TTS) that learns controls from post-ge
arXiv:2603.08491v2 Announce Type: replace Abstract: Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery, which is crucial for pedestrian navigatio
arXiv:2606.06002v1 Announce Type: new Abstract: Large Vision-Language Models have achieved significant reasoning performance in various tasks.However, there are few studies on text-to-3D indoor scene
arXiv:2606.06363v1 Announce Type: new Abstract: Urban green-space extraction from ultra-high-resolution (UHR) imagery is commonly performed patch by patch, which limits semantic reuse among spatially
arXiv:2606.06040v1 Announce Type: new Abstract: Soft, growing vine robots extend through tip eversion, a mechanism that enables navigation through cluttered environments. However, integrating cameras
arXiv:2606.06249v1 Announce Type: new Abstract: Transformer-based multimodal models rely on attention mechanisms to integrate information across heterogeneous modalities. Despite their success, existi
arXiv:2602.05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain
arXiv:2606.05650v1 Announce Type: cross Abstract: Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelit
arXiv:2606.06493v1 Announce Type: new Abstract: For a humanoid robot to be deployed in the real world, the choice of command space (i.e., the interface between task planning and whole-body control) is
arXiv:2511.20158v2 Announce Type: replace Abstract: While continual visual instruction tuning (CVIT) has shown promise in adapting multimodal large language models (MLLMs), existing studies predominan
arXiv:2606.05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end
arXiv:2606.06109v1 Announce Type: new Abstract: Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cr