Knowledge catalogue
arXiv:2609.02108v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order g
arXiv:2609.02606v1 Announce Type: new Abstract: Loneliness is a critical public health issue among older adults, linked to higher risks of depression, cognitive decline, and mortality. Scalable, objec
arXiv:2609.01623v1 Announce Type: cross Abstract: Autonomous and intelligent transportation systems operate in complex urban environments where safety depends on interactions among vehicle behavior, e
arXiv:2609.02055v1 Announce Type: cross Abstract: Privacy policies may contain internal contradictions in which commitments are undermined by practices documented elsewhere in the same policy. We oper
arXiv:2609.01667v1 Announce Type: cross Abstract: Artificial Intelligence has shown to help improve agricultural practices, yet adoption remains limited: 69% of U.S. farmers have privacy concerns with
arXiv:2609.01658v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances Large Language Models by grounding responses in external knowledge, but multi-hop reasoning remains vulnerable
arXiv:2609.02549v1 Announce Type: cross Abstract: Drug-target interaction (DTI) prediction is an important task in AI-driven drug discovery. Although recent biochemical representation learning methods
arXiv:2603.16749v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode.
arXiv:2609.02171v1 Announce Type: new Abstract: Point-supervised change detection (PS-CD) aims to identify pixel-level changes between bi-temporal images using only sparsely annotated points. Although
arXiv:2609.01615v1 Announce Type: new Abstract: Personalizing a frozen large language model (LLM) to individual users is often framed as a meta-learning problem in prompt space: each user is a task, a
arXiv:2407.14845v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses
arXiv:2609.02242v1 Announce Type: new Abstract: AI assistants often collaborate by proposing candidate edits, plans, or designs that users evaluate before adoption. Existing assistance methods focus o
arXiv:2609.02377v1 Announce Type: new Abstract: High-resolution Synthetic Aperture Radar (SAR) imagery is critical for precision analysis such as automatic target recognition, yet its acquisition is c
arXiv:2609.02209v1 Announce Type: cross Abstract: Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast
arXiv:2609.01693v1 Announce Type: cross Abstract: Safety properties assessed separately for Model Context Protocol (MCP) tool use and Agent2Agent (A2A) delegation need not describe behavior when one a
arXiv:2609.01945v1 Announce Type: cross Abstract: Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated. However, the exchanged model updates
arXiv:2609.02186v1 Announce Type: cross Abstract: Quantum generative models offer a promising framework for exploring whether quantum computation can enhance generative machine learning. Flow matching
arXiv:2504.03626v2 Announce Type: replace-cross Abstract: We present quantum speedups for sampling from distributions of the form pipropto e^{-f} on R^d. We consider two stochastic oracle models: a st
arXiv:2609.02664v1 Announce Type: new Abstract: Recent 4D Gaussian representation frameworks have demonstrated strong performance in language-guided dynamic scene understanding. However, these methods
arXiv:2609.01876v1 Announce Type: new Abstract: Digital volume correlation (DVC) provides three-dimensional full-field displacement measurements from volumetric images, but how the internal resolution
arXiv:2609.01675v1 Announce Type: cross Abstract: Accurate large-scale wildfire spread modelling requires models that capture both the environmental conditions associated with fire occurrence and the
arXiv:2609.01671v1 Announce Type: cross Abstract: Security teams and researchers choose knowledge-graph extraction tooling for threat reports on the strength of published triple-F1 scores, yet those s
arXiv:2608.27463v2 Announce Type: replace Abstract: LLMs now sit on every side of evaluation: as examinees scored on benchmarks, judges of other models' outputs, and raters of human-generated content.
arXiv:2609.02095v1 Announce Type: new Abstract: An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete re
arXiv:2609.02020v1 Announce Type: new Abstract: This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework. The proposed framework explicitly solves rigid-bod
arXiv:2609.01622v1 Announce Type: cross Abstract: The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recomm
arXiv:2609.01729v1 Announce Type: cross Abstract: Kolmogorov--Arnold Networks (KANs) replace the fixed scalar weights of a standard network with learnable univariate functions on each edge, but existi
arXiv:2609.02237v1 Announce Type: new Abstract: Scaling offline goal-conditioned reinforcement learning (GCRL) to long-horizon tasks is difficult because (1) long-range value learning depends on short
arXiv:2609.01942v1 Announce Type: new Abstract: Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses). Exi
arXiv:2609.01680v1 Announce Type: new Abstract: This paper compares rule-based and learning-based pricing mechanisms for peer-to-peer (P2P) electricity trading in residential photovoltaic communities.
arXiv:2609.01811v1 Announce Type: cross Abstract: No single optimization method is uniformly best for all problems, and the most suitable optimizer choice can change during a run. Existing approaches
arXiv:2609.02088v1 Announce Type: new Abstract: Multimodal large language models have achieved remarkable progress in front-end web development, generating interactive webpages from multimodal referen
arXiv:2609.02749v1 Announce Type: new Abstract: Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for plannin
arXiv:2609.02507v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) has recently emerged as a promising strategy that allows the adaptation of pre-trained models to changing data distributions
arXiv:2609.02268v1 Announce Type: cross Abstract: With the rapid proliferation of generative models on Machine Learning as a Service (MLaaS) platforms, reliably tracing the provenance of synthetic med
arXiv:2602.10118v2 Announce Type: replace Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics, namely lazy thinking and non-specific critiques, has threatened rev
arXiv:2609.02556v1 Announce Type: new Abstract: Synthetic training data is crucial for developing vision AI when real-world data is scarce, as in thermal infrared (IR) aerial vehicle detection. While
arXiv:2609.02250v1 Announce Type: cross Abstract: Ride-sharing, which allows multiple passengers with different origin-destination (OD) pairs to share a single vehicle, is a challenging operational pr
arXiv:2609.02497v1 Announce Type: cross Abstract: Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-d
arXiv:2607.10372v2 Announce Type: replace-cross Abstract: The transition of autonomous mobile robots from controlled industrial settings to dynamic, human-centric environments, such as manufacturing,
arXiv:1902.03223v4 Announce Type: replace-cross Abstract: We consider streaming principal component analysis when the stochastic data generating model is subject to perturbations. While existing model
arXiv:2609.02847v1 Announce Type: new Abstract: Novel view synthesis from sparse inputs requires both geometric grounding from the observed views and generative priors of unobserved regions, motivatin
arXiv:2609.02282v1 Announce Type: cross Abstract: Mineral image classification is important for geological exploration and resource development, but it remains challenging due to substantial intra-cla
arXiv:2609.02731v1 Announce Type: cross Abstract: Large vision-language models have achieved remarkable success in vision-language tasks. However, they remain prone to Visual Hallucinations (VHs), und
arXiv:2609.02786v1 Announce Type: new Abstract: The performance of LLM-based agents is jointly shaped by the base model and the harness used when interacting with the environment. This exposes them to
arXiv:2609.02336v1 Announce Type: new Abstract: Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface s
arXiv:2502.14210v4 Announce Type: replace-cross Abstract: Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics.
arXiv:2609.02247v1 Announce Type: cross Abstract: Semi-supervised learning has shown great potential for reducing annotation costs in medical image segmentation. However, most existing methods mainly
arXiv:2609.02126v1 Announce Type: new Abstract: Estimating physical parameters from scientific images is a common inverse problem in materials characterization that often relies on expensive physics-b
arXiv:2609.02623v1 Announce Type: cross Abstract: Voice actors often re-read the same script while modifying their delivery in response to performance directions. We study this setting as direction-fo
arXiv:2609.02451v1 Announce Type: cross Abstract: In this paper, we propose a scalable Kronecker-based approximation that captures cross-layer interactions without storing the entire Fisher matrix, en
arXiv:2609.02196v1 Announce Type: cross Abstract: Generative modeling directly on geometric manifolds can avoid errors introduced by flattening non-Euclidean data, repeated ambient projection, and coo
arXiv:2609.01743v1 Announce Type: new Abstract: Edge vision models are difficult to deploy on resource-constrained hardware, making low-bit post-training quantization (PTQ) attractive. In practice, st
arXiv:2609.02292v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) and the growing diversity of their applications presents a unique optimization opportunity: sele
arXiv:2609.02293v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) is a scaling architecture for large language models that activates only a small subset of expert modules per token, enabling
arXiv:2512.15823v3 Announce Type: replace-cross Abstract: Immersive formats such as 360{eg} and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR st
arXiv:2609.02011v1 Announce Type: cross Abstract: Large language model question answering over power-grid models must respect a fixed context budget. We introduce seed-anchored graph rendering, a dete
arXiv:2609.02390v1 Announce Type: cross Abstract: Sampling error yields exclusively reactive, non-lesional brain parenchyma in a significant proportion of intracranial biopsies, leaving the underlying
arXiv:2609.02091v1 Announce Type: new Abstract: Knowledge editing provides an efficient way to update factual knowledge in large language models. However, malicious edits may introduce safety risks, m
arXiv:2609.02036v1 Announce Type: new Abstract: Few-step diffusion models substantially compress temporal computation, making the spatial cost of each model evaluation an increasingly dominant source