Knowledge catalogue
arXiv:2608.22857v1 Announce Type: new Abstract: Vision-language models (VLMs) frequently fail at visual change reasoning, even when their vision encoders contain sufficient information. We observe tha
arXiv:2608.21700v1 Announce Type: cross Abstract: Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein
arXiv:2608.22062v1 Announce Type: new Abstract: Longitudinal clinical event-relation verification determines whether a patient record supports a specified relation among two or more clinical events. T
arXiv:2506.13529v2 Announce Type: replace-cross Abstract: Seismic acoustic impedance plays a crucial role in lithological identification and subsurface structure interpretation. However, due to the in
arXiv:2608.21499v1 Announce Type: cross Abstract: Cardiac auscultation remains the most cost-effective screening procedure for cardiovascular diseases, and requires listening at the four main ausculta
arXiv:2608.21402v1 Announce Type: cross Abstract: World action models (WAMs) jointly denoise future video frames and robot actions, and the video prior is expected to generalize their control. Camera
arXiv:2608.22898v1 Announce Type: new Abstract: Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded gener
arXiv:2608.23249v1 Announce Type: new Abstract: We consider a multistatic radio-frequency imaging problem with anisotropy, in which the reflection from a point depends on the positions of the transmit
arXiv:2608.21636v1 Announce Type: new Abstract: Video Object-Centric Learning (OCL) has traditionally focused on refining the encoder architecture to ensure temporal consistency. In this paper, we arg
arXiv:2608.23211v1 Announce Type: cross Abstract: In this work, we study the oracle complexity of finding an epsilon-stationary point for nonconvex-strongly-convex (NC-SC) bilevel optimization using o
arXiv:2608.22252v1 Announce Type: cross Abstract: We establish sharp Barron regularity for Coulombic many-electron wave functions after extraction of the universal cut-off Jastrow factors. Following t
arXiv:2603.11421v2 Announce Type: replace Abstract: Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Impli
arXiv:2608.23296v1 Announce Type: cross Abstract: Learned KV-cache eviction often faces a soft-to-hard mismatch: during training, differentiable gates typically attenuate token contributions, whereas
arXiv:2607.15772v2 Announce Type: replace Abstract: Maintaining recurring character identities across scene transitions and long temporal gaps is a central challenge in narrative long video generation
arXiv:2405.01990v2 Announce Type: replace Abstract: PU learning refers to the classification problem in which only part of positive samples are labeled. Existing PU learning methods treat unlabeled sa
arXiv:2608.21579v1 Announce Type: new Abstract: Consider the following problem of learning an unknown linear order on n items. In each round, the learner guesses a complete ordering of the items and r
arXiv:2608.21776v1 Announce Type: new Abstract: Recent advances in image editing allow impressive manipulation of objects, existing methods still struggle to handle spatial movement in complex scenes,
arXiv:2608.23279v1 Announce Type: new Abstract: Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For repr
arXiv:2608.21466v1 Announce Type: cross Abstract: We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. Fo
arXiv:2608.23382v1 Announce Type: new Abstract: Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data throug
arXiv:2608.23163v1 Announce Type: new Abstract: Conventional multirotor aerial vehicles actively suppress yaw rotation during hover, expending power to maintain a fixed heading despite the fact that y
arXiv:2608.21952v1 Announce Type: new Abstract: Recent advances in sequence modeling have highlighted Mamba as a state space architecture offering efficient long-range dependency modeling and providin
arXiv:2608.21710v1 Announce Type: new Abstract: With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stereo
arXiv:2608.22874v1 Announce Type: cross Abstract: Neurobiological studies and representation learning have observed that representations of objects belonging to the same category in high-dimensional n
arXiv:2608.23411v1 Announce Type: new Abstract: LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes that
arXiv:2608.22896v1 Announce Type: new Abstract: Robotic navigation in human environments requires a spatio-temporal semantic representation that can rec- oncile open-vocabulary perception with long-te
arXiv:2608.23068v1 Announce Type: new Abstract: Source seeking arises in applications such as gas leak localization, radiation monitoring, and environmental surveillance, where the origin of an unknow
arXiv:2608.22112v1 Announce Type: new Abstract: We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approach p
arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fre
arXiv:2608.22594v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize
arXiv:2608.22117v1 Announce Type: cross Abstract: A standard Transformer block separates cross-token interaction in self-attention from a nonlinear feed-forward network applied independently at each p
arXiv:2608.22299v1 Announce Type: new Abstract: The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical image
arXiv:2608.23347v1 Announce Type: new Abstract: Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains du
arXiv:2608.21871v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has become the dominant recipe for improving large language model reasoning, yet it presumes large human-
arXiv:2608.23338v1 Announce Type: cross Abstract: LoRA fine-tuning is standard for adapting LLMs to reranking, but it remains unclear where in the network task-specific relevance behavior is learned a
arXiv:2608.23358v1 Announce Type: new Abstract: The performance gap between low- and high-resource languages in LLMs is widely known, but it remains unclear which internal model factors drive these di
arXiv:2608.23037v1 Announce Type: cross Abstract: This paper introduces the Multilingual FrameNet Corpus (mFNC), a novel resource that extends the English Berkeley FrameNet corpus by collecting and ha
arXiv:2608.22293v1 Announce Type: cross Abstract: Urban wildfire resilience depends on interactions among built form, vegetation condition, and extreme fire weather, yet city-scale risk models often o
arXiv:2608.22096v1 Announce Type: new Abstract: This report describes Libo Zhang's algorithmic solution to autoPETV Grand Challenge on interactive lesion segmentation in whole-body PET/CT. Interaction
arXiv:2608.22179v1 Announce Type: cross Abstract: Membership inference asks whether a text was used to train a language model, whereas AI-generated text detection asks whether it was generated by a la
arXiv:2608.21404v1 Announce Type: new Abstract: Fixed coordinate measuring machines (CMMs) and portable articulated-arm CMMs are often assigned to the same inspection task, but their nominal accuracy
arXiv:2608.21455v1 Announce Type: new Abstract: Face presentation attack detection (PAD) is traditionally formulated as a face-specific problem, although many of the visual artifacts introduced by pri
arXiv:2608.21422v1 Announce Type: new Abstract: CBCT (Cone Beam Computed Tomography) scans provide detailed three-dimensional images, widely used in dentistry for diagnostic and treatment planning tas
arXiv:2608.21969v1 Announce Type: new Abstract: Humans have multiple levels of temporal abstractions on daily interaction and thinking, such as concept perception and strategic planning. Inspired by t
arXiv:2608.22253v1 Announce Type: new Abstract: We study the language-model head and softmax as a single module, deriving an update geometry from their composition rather than from the weight matrix i
arXiv:2608.23190v1 Announce Type: new Abstract: Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time
arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,
arXiv:2608.21699v1 Announce Type: new Abstract: Modern flapping-wing insect-scale air vehicles display agility similar to that of their insect counterparts; however, these impressive maneuvers are onl
arXiv:2501.13976v2 Announce Type: replace-cross Abstract: The prevalence of harmful content on social media platforms poses significant risks to users and society, necessitating more effective and sca
arXiv:2608.23458v1 Announce Type: new Abstract: Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's Ariel, where ground truth is unavailable du
arXiv:2608.23102v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combini
arXiv:2608.22263v1 Announce Type: cross Abstract: Vision-language models can be personalized in a training-free manner by directly providing user profiles, preferences, or visual references at inferen
arXiv:2412.06769v4 Announce Type: replace Abstract: Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-
arXiv:2608.22575v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have emerged as a powerful framework for learning from graph-structured data. However, their efficient training remains c
arXiv:2505.00333v3 Announce Type: replace Abstract: Federated fine-tuning with low-rank adaptation (LoRA) communicates only two low-rank matrices instead of the full model, but existing methods typica
arXiv:2601.14492v3 Announce Type: replace Abstract: Robotic strawberry harvesting remains challenging under partial occlusion, where leaves obscure fruit geometry and make grasp decisions based on a s
arXiv:2601.21242v2 Announce Type: replace-cross Abstract: Motivated by challenges in conditional generative modeling, where the target conditional density takes the form of a ratio f1 over f2, this pa
arXiv:2608.22217v1 Announce Type: new Abstract: Radiologists generate diagnostic reports through iterative and selective revisiting of suspicious regions to refine their interpretations. Recent multim
arXiv:2608.21660v1 Announce Type: new Abstract: When discrete-time optimizers operate at the edge of stability, they exhibit near-two-periodic behavior. These oscillatory dynamics are reminiscent of c
arXiv:2608.22116v1 Announce Type: new Abstract: The dataset provides individual vehicle speed observations on European E-roads: motorways, trunk roads, primary and secondary roads, as tagged in OpenSt