Knowledge catalogue
arXiv:2608.29517v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as essay graders in learning analytics, evaluated almost exclusively with agreement statistics. Educa
arXiv:2608.29640v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for spatio-temporal forecasting, but existing approaches often rely on regularly sampled token sequenc
arXiv:2601.06973v2 Announce Type: replace Abstract: As LLMs move from text completion toward autonomous agents, they remain constrained by the standard chat interface, which lacks private working memo
arXiv:2510.09347v2 Announce Type: replace Abstract: Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) oft
arXiv:2608.29228v1 Announce Type: new Abstract: Failures in agentic AI systems can arise from interactions among messages exchanged by multiple large language model (LLM) agents. Pointwise attribution
arXiv:2608.30959v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning
arXiv:2608.28627v1 Announce Type: new Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization probl
arXiv:2606.05371v2 Announce Type: replace Abstract: Reduced-order modeling of high-dimensional dynamical systems is often hindered by closure effects arising from unresolved variables, which can intro
arXiv:2603.23295v2 Announce Type: replace Abstract: Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imaging
arXiv:2608.29865v1 Announce Type: new Abstract: Manga visual question answering requires models to answer questions over panel-based visual narratives, where relevant evidence is distributed across or
arXiv:2608.29164v1 Announce Type: new Abstract: In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation in
arXiv:2608.29528v1 Announce Type: new Abstract: Longitudinal clinical agents must maintain an evolving patient state from evidence distributed across visits, time points, and specialties. However, how
arXiv:2608.28773v1 Announce Type: cross Abstract: Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity simi
arXiv:2412.00241v3 Announce Type: replace Abstract: Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world sys
arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state m
arXiv:2607.14252v2 Announce Type: replace-cross Abstract: Embodied agents accumulate experience over time. We study how accumulated experience can be formed into persistent memory for future reasoning
arXiv:2608.30521v1 Announce Type: new Abstract: Conventional algebraic triangulation solves 3D human pose estimation (HPE) from multi-view 2D keypoints. The typical approach, decoding 2D keypoints fro
arXiv:2608.28931v1 Announce Type: cross Abstract: Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where l
arXiv:2608.29623v1 Announce Type: cross Abstract: Recent advances in large reasoning models (LRMs) have shown strong performance on complex problems through long chain-of-thought (Long CoT) reasoning.
arXiv:2603.21716v2 Announce Type: replace-cross Abstract: Efficient selection among multiple generative models is increasingly important in modern generative AI, where sampling from suboptimal models
arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructio
arXiv:2601.04035v2 Announce Type: replace Abstract: Mobile GUI agents have shown strong potential in real-world automation and practical applications. However, most existing agents remain reactive, ma
arXiv:2608.30636v1 Announce Type: new Abstract: Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning model
arXiv:2602.11793v2 Announce Type: replace-cross Abstract: Watermarking has emerged as a crucial technique for detecting and attributing content generated by large language models. While recent advance
arXiv:2608.29139v1 Announce Type: new Abstract: Learning effective multimodal entity representations is fundamental for reasoning tasks such as multimodal knowledge graph completion (MMKGC). However,
arXiv:2608.30279v1 Announce Type: new Abstract: Point cloud video representation learning is crucial for 3D dynamic scene understanding. In this paper, we propose MoSaiC, a novel Motion-Saliency Compl
arXiv:2608.28675v1 Announce Type: cross Abstract: Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. In
arXiv:2608.29186v1 Announce Type: new Abstract: Federated Video Domain Adaptation (FVDA) enables collaborative learning across distributed and non-IID video datasets while preserving privacy, but is u
arXiv:2608.28680v1 Announce Type: new Abstract: Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains di
arXiv:2512.23667v3 Announce Type: replace Abstract: Intrinsic image decomposition aims to recover material and illumination factors from RGB observations, but real-world images entangle reflectance wi
arXiv:2608.28874v1 Announce Type: new Abstract: The automated sorting of shredded black plastics from end-of-life (EOF) industrial waste presents a significant challenge in recycling facilities, prima
arXiv:2608.29714v1 Announce Type: cross Abstract: Longitudinal cohort studies produce repeated data that enable the assessment of time-varying association patterns between exposures and health outcome
arXiv:2608.29112v1 Announce Type: new Abstract: Recent advances in anomaly detection (AD) for industrial inspection have pushed performance on standard benchmarks toward saturation. However, strong be
arXiv:2608.30194v1 Announce Type: new Abstract: Diffusion models have recently advanced text-to-video (T2V) generation, yet they still struggle with fine-grained compositional alignment, such as attri
arXiv:2601.15436v3 Announce Type: replace Abstract: We propose a novel perspective for probing LLM sycophancy in a direct and neutral way, mitigating various forms of uncontrolled bias, noise, or mani
arXiv:2608.29820v1 Announce Type: new Abstract: Ultrasound B-mode imaging commonly suffers from speckle noise and artifacts, requiring a delicate balance between contrast, resolution, and preservation
arXiv:2608.30756v1 Announce Type: cross Abstract: Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their
arXiv:2608.29944v1 Announce Type: new Abstract: Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learni
arXiv:2608.30003v1 Announce Type: new Abstract: Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burdene
arXiv:2608.29881v1 Announce Type: new Abstract: Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective,
arXiv:2608.29187v1 Announce Type: new Abstract: The point-based occupancy prediction paradigm has achieved an attractive trade-off between accuracy and efficiency by modeling 3D space sparsely. Howeve
arXiv:2608.29905v1 Announce Type: new Abstract: Text-guided style editing of 3D assets is essential for adapting existing objects to diverse visual aesthetics in digital content creation. Despite rapi
arXiv:2608.28596v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adva
arXiv:2608.31119v1 Announce Type: new Abstract: Research planning is the decisive capability of AI scientists. Yet a research plan admits no verifiable answer, so reinforcement learning lacks the envi
arXiv:2608.29107v1 Announce Type: new Abstract: While modern generative models excel at modeling complex data, precise inference-time control in conditional generation remains a critical challenge. Cl
arXiv:2608.28728v1 Announce Type: new Abstract: Urban construction governance requires early decisions that connect workplace safety, permitting requirements, and community impact, yet the relevant ev
arXiv:2608.29287v1 Announce Type: new Abstract: Shot boundary detection (SBD) is widely treated as the localisation of local visual discontinuities, yet many false positives such as hand-held shake, i
arXiv:2510.26444v2 Announce Type: replace-cross Abstract: Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is a critical task in precision medicin
arXiv:2608.29819v1 Announce Type: new Abstract: Accurate stereo matching remains challenging in ill-posed regions such as fine structures, reflective, or transparent objects, where appearance cues are
arXiv:2310.08774v3 Announce Type: replace-cross Abstract: Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and
arXiv:2608.29663v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables contactless physiological measurement from facial videos, yet its subtle pulse-related variations are easily
arXiv:2608.30782v1 Announce Type: new Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realist
arXiv:2601.14056v2 Announce Type: replace-cross Abstract: Training robust visual surveillance models requires large-scale datasets with precise spatial annotations, yet collecting real surveillance da
arXiv:2608.24921v2 Announce Type: replace Abstract: Graph RAG connects facts no single passage states, but implementations pay three times: in infrastructure, keeping vector store, graph database and
arXiv:2608.29579v1 Announce Type: new Abstract: Chaotic time series forecasting is a challenging task due to its sensitivity to initial conditions and long-term unpredictability. Traditional methods t
arXiv:2608.20118v1 Announce Type: cross Abstract: The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as le
arXiv:2608.25992v2 Announce Type: replace Abstract: Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative rea
arXiv:2603.17546v2 Announce Type: replace Abstract: Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual
arXiv:2608.30874v1 Announce Type: cross Abstract: This paper considers decentralized contingency MPC for multi-agent control under a state-only information pattern, with particular focus on limited se
arXiv:2603.12165v3 Announce Type: replace Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult t