Knowledge catalogue
arXiv:2506.01004v3 Announce Type: replace-cross Abstract: Unlike traditional video editing or inpainting, video semantic mixing fuses a reference concept with a moving target entity to produce a hybri
arXiv:2609.36893v1 Announce Type: new Abstract: Detailed image captioning requires accurate and comprehensive descriptions of fine-grained visual content, yet caption quality spans factual accuracy, i
arXiv:2606.19122v2 Announce Type: replace Abstract: Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe nav
arXiv:2309.15670v3 Announce Type: replace-cross Abstract: In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seve
arXiv:2609.36154v1 Announce Type: new Abstract: General-purpose models promise sensor-based decisions without training a task-specific classifier, which could reduce the dependence of Human Activity R
arXiv:2609.35873v1 Announce Type: new Abstract: Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated
arXiv:2609.36301v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) layers are central to frontier language models, and recent architectures push toward more and smaller experts. In this regime
arXiv:2609.37384v1 Announce Type: new Abstract: Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complement
arXiv:2609.36832v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that
arXiv:2609.37030v1 Announce Type: cross Abstract: Despite rapid progress in video generation models, they still exhibit obvious motion deficiencies, often manifested as incorrect object motion. Howeve
arXiv:2609.37495v1 Announce Type: new Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exist
arXiv:2609.38078v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited
arXiv:2507.13628v3 Announce Type: replace Abstract: Separating moving and static objects from a moving camera viewpoint is essential for 3D reconstruction, autonomous navigation, and scene understandi
arXiv:2609.37141v1 Announce Type: new Abstract: Semantic typography is a design technique where the visual representation of a word conveys its semantic meaning, while maintaining its legibility. Exis
arXiv:2609.37322v1 Announce Type: new Abstract: Rubric-based evaluation is widely used to assess LLM-based systems by decomposing response quality into task-specific scoring criteria. However, automat
arXiv:2609.38077v1 Announce Type: new Abstract: Interactive panoramic video generation aims to synthesize immersive 360extdegree{} videos that remain visually coherent while following user-specified c
arXiv:2609.38133v1 Announce Type: cross Abstract: Generative modeling is widely used for producing diverse objects from complex, multimodal distributions. However, its expressivity does not, in genera
arXiv:2609.36611v1 Announce Type: new Abstract: Retrieval-augmented fact-checkers often receive a reliability label, such as HIGH or LOW trust, for each evidence source. These labels should adjust the
arXiv:2609.36039v1 Announce Type: cross Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing st
arXiv:2609.37047v1 Announce Type: cross Abstract: We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies. Our key re
arXiv:2609.37135v1 Announce Type: new Abstract: Using language instructions as conditions to guide robot policy learning has recently become an important research domain. However, existing language-gu
arXiv:2609.37750v1 Announce Type: cross Abstract: Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet the real-world performance of FDA-cleared AI detection models remains inco
arXiv:2410.14839v5 Announce Type: replace-cross Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corpora
arXiv:2609.37387v1 Announce Type: new Abstract: Enlarged perivascular spaces (PVS) visible in brain magnetic resonance imaging (MRI) are increasingly thought to be linked to poor brain health. PVS are
arXiv:2609.37116v1 Announce Type: cross Abstract: Accurate perceptual quality assessment is essential for evaluating and optimizing spatial audio, where perceived quality depends on both signal fideli
arXiv:2609.37086v1 Announce Type: new Abstract: Multimodal biomimetic underwater robots (BURs) can conduct underwater tasks suitable for the environment. Combining the characteristics of aquatic organ
arXiv:2609.37148v1 Announce Type: cross Abstract: Many of the qualities that matter most in how people learn and grow, how someone regulates their emotions, reflects on a setback, or stays aware of ot
arXiv:2609.36903v1 Announce Type: cross Abstract: End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limit
arXiv:2609.37994v1 Announce Type: cross Abstract: The classical information bottleneck (IB) measures the relevance of a representation U of X to a target Y by I(U;Y), which does not directly character
arXiv:2609.36224v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) combine image generation and visual understanding in a shared backbone. Since generation and understanding are invers
arXiv:2609.37793v1 Announce Type: new Abstract: World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. H
arXiv:2609.36687v1 Announce Type: cross Abstract: Neural codecs encode continuous signals into compact sequences of discrete tokens, providing an interface for efficient transmission, storage, and tok
arXiv:2609.36756v1 Announce Type: cross Abstract: One-dimensional (1D) variable-length visual tokenizers enable adaptive compression by varying the number of tokens, allowing downstream autoregressive
arXiv:2609.37113v1 Announce Type: new Abstract: Generalized reaction-diffusion systems encompass diverse transport mechanisms and coupled reaction kinetics. A central question for neural PDE solvers i
arXiv:2609.36047v1 Announce Type: cross Abstract: Given a functional dependent on the spectrum of a differential operator, we address the problem of finding a domain which optimizes this functional. P
arXiv:2609.36620v1 Announce Type: new Abstract: Structural reasoning, the ability to recognize and make inferences over the relational structure between objects and concepts, is a hallmark of human co
arXiv:2609.36375v1 Announce Type: new Abstract: Continual learning is usually studied through mechanisms that preserve old knowledge. We develop Successional Learning Theory (SLT), a mesoscopic accoun
arXiv:2609.38089v1 Announce Type: cross Abstract: Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological
arXiv:2609.36927v1 Announce Type: new Abstract: Many computer tasks recur: the same workflow runs many times, with new inputs and from different starting states. Current computer-use agents re-plan ev
arXiv:2609.38098v1 Announce Type: new Abstract: Current vision-language models (VLMs) encode visual information in dense hidden states where object identity, spatial layout, and local attributes are i
arXiv:2609.35833v1 Announce Type: new Abstract: Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on
arXiv:2609.37048v1 Announce Type: new Abstract: Non-rigid partial-to-full shape correspondence from sparse anchors requires identifying the corresponding region on the full surface and recovering dens
arXiv:2609.36878v1 Announce Type: new Abstract: LiDAR return intensity provides complementary surface-response cues for robotic perception and state estimation, yet many simulation pipelines omit it o
arXiv:2609.38004v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these s
arXiv:2605.05511v2 Announce Type: replace Abstract: Active feature acquisition (AFA) considers prediction problems in which features are costly to obtain and the learner adaptively decides which featu
arXiv:2606.17531v2 Announce Type: replace Abstract: Non-negative matrix factorisation (NMF) learns additive representations from data, but non-negativity alone does not ensure interpretable basis func
arXiv:2609.37660v1 Announce Type: new Abstract: We study nonpreemptive contextual queueing bandits in a single-server system. Each job is represented by a d-dimensional context vector; in each round,
arXiv:2609.36692v1 Announce Type: cross Abstract: Matrix optimizers have emerged as a promising direction, with Muon standing out as a prominent design. Revisiting Muon through its full-Gram represent
arXiv:2609.35926v1 Announce Type: cross Abstract: Continual learning models suffer from catastrophic forgetting when trained sequentially on non-stationary data distributions. Previously, this has bee
arXiv:2609.36655v1 Announce Type: new Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods of
arXiv:2609.37038v1 Announce Type: cross Abstract: Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability. Diffusion models are well suited to modeling c
arXiv:2609.37115v1 Announce Type: new Abstract: We revisit the role of appearance modeling in 3D Gaussian Splatting (3DGS) and show that limited expressiveness in view-dependent reflectance is a key d
arXiv:2609.36644v1 Announce Type: new Abstract: Cross-attention is a crucial component in learning-based image-to-point-cloud (I2P) registration. Although existing cross-attention mechanisms have achi
arXiv:2609.37331v1 Announce Type: new Abstract: Balancing performance trade-offs on long-tailed data distributions remains a long-standing challenge in visual recognition. Existing methods mainly impr
arXiv:2609.37052v1 Announce Type: new Abstract: Omnimodal large language models (Omni-LLMs) encode audio and visual streams into temporally interleaved token sequences for multimodal reasoning. Howeve
arXiv:2609.38079v1 Announce Type: new Abstract: Training a model to generate visual content can encourage it to learn rich perceptual capabilities related to geometry, spatial relationships, and objec
arXiv:2609.37773v1 Announce Type: new Abstract: Artificial Intelligence Virtual Cells (AIVCs) are envisioned as scientific agents that simulate cellular responses, explain underlying mechanisms, and s
arXiv:2602.13540v2 Announce Type: replace-cross Abstract: Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on
arXiv:2609.36659v1 Announce Type: new Abstract: The strong generalization performance of on-policy post-training paradigms has motivated studies of their parameter update behaviors. However, these stu
arXiv:2609.36838v1 Announce Type: cross Abstract: Visual agents solve problems by interleaving reasoning with image operations, and on-policy distillation (OPD) provides guidance from a strong teacher