Knowledge catalogue
arXiv:2511.20284v2 Announce Type: replace-cross Abstract: Precise access control decisions are crucial for the security of both traditional applications and emerging agent-based systems. Typically, th
arXiv:2605.04229v1 Announce Type: new Abstract: In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accur
arXiv:2603.08022v2 Announce Type: replace Abstract: A data mixture refers to how different data sources are combined to train large language models, and selecting an effective mixture is crucial for o
arXiv:2605.04495v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) depends on document ranking to provide useful evidence for generation, but conventional reranking methods mainly op
arXiv:2605.05014v1 Announce Type: new Abstract: Autonomous driving must operate across diverse surfaces to enable safe mobility. However, most driving datasets are captured on well-paved flat roads. M
arXiv:2506.22226v2 Announce Type: replace-cross Abstract: Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilita
arXiv:2605.04641v1 Announce Type: new Abstract: Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviat
arXiv:2605.04381v1 Announce Type: cross Abstract: Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumpti
arXiv:2603.20684v2 Announce Type: replace Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectivenes
arXiv:2605.05012v1 Announce Type: new Abstract: Texture classification is a pivotal task in computer vision, presenting unique challenges due to high inter-class similarity and the sensitivity of stru
arXiv:2605.04652v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge lies in jointly capturing two sources of p
arXiv:2605.04206v1 Announce Type: new Abstract: Large-scale restoration in drylands is widely promoted to address land degradation and biodiversity loss, yet many efforts rely on long-term irrigation,
arXiv:2502.10148v3 Announce Type: replace Abstract: Cooperative multi-agent reinforcement learning (MARL) struggles with sample efficiency, interpretability, and generalization. While Large Language M
arXiv:2605.04761v1 Announce Type: new Abstract: This paper presents the Personalized Thinking Model (PTM), a hierarchical and interpretable learner representation designed for AI supported education.
arXiv:2502.13498v2 Announce Type: replace-cross Abstract: Object-goal visual navigation aims to reach a specific target object using egocentric visual observations. Recent deep reinforcement learning
arXiv:2505.05880v2 Announce Type: replace-cross Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between t
arXiv:2605.05031v1 Announce Type: new Abstract: Recent deep learning approaches seek to automate CAD creation by representing a model as a sequence of discrete commands and parameters, and then genera
arXiv:2605.03067v1 Announce Type: new Abstract: Approval-based committee voting has received significant attention in the social choice community. Among the studied rules, Thiele rules, and especially
arXiv:2605.04980v1 Announce Type: cross Abstract: Activation-based steering provides control of LLM behavior at inference time, but the dominant paradigm reduces each concept to a single direction who
arXiv:2605.04830v1 Announce Type: new Abstract: Diffusion models undergo a phase transition in a critical time window during generation dynamics, with two complementary diagnoses of criticality. The s
arXiv:2605.04366v1 Announce Type: cross Abstract: Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation off
arXiv:2605.05124v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electr
arXiv:2512.06393v5 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve high accuracy on many reasoning benchmarks but remain brittle under structural perturbations of rule-base
arXiv:2605.04073v1 Announce Type: new Abstract: Bail decisions present a fundamental challenge for data-driven decision support systems. When bail is denied, the counterfactual outcome of whether the
arXiv:2605.04080v1 Announce Type: new Abstract: This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illega
arXiv:2504.11101v4 Announce Type: replace Abstract: Optical Character Recognition (OCR) is fundamental to Vision-Language Models (VLMs) and high-quality data generation for LLM training. Yet, despite
arXiv:2605.05126v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models primarily focus on mapping 2D observations to actions, but exhibit notable limitations in spatiotemporal per
arXiv:2605.04130v1 Announce Type: new Abstract: High-fidelity simulations, such as computational fluid dynamics and finite element analysis, are essential for modeling complex engineering systems but
arXiv:2605.04052v1 Announce Type: cross Abstract: Low Earth orbit (LEO) satellites increasingly carry compute hardware capable of on-board processing, yet each satellite generates roughly two orders o
arXiv:2605.04185v1 Announce Type: new Abstract: When deploying reinforcement learning policies to physical robots, actuator rate constraints -- hard limits on how fast each joint can move per control
arXiv:2605.04662v1 Announce Type: new Abstract: Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints a
arXiv:2511.03475v4 Announce Type: replace Abstract: AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support stronger reasoning. Common examples
arXiv:2605.04400v1 Announce Type: cross Abstract: While Separate Source-Channel Coding (SSCC) retains the practical benefits of modular system design, its effectiveness in noisy text transmission is f
arXiv:2605.03900v1 Announce Type: new Abstract: Frontier AI systems perform best in settings with clear, stable, and verifiable objectives, such as code generation, mathematical reasoning, games, and
arXiv:2605.04059v1 Announce Type: cross Abstract: Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual
arXiv:2605.05097v1 Announce Type: cross Abstract: LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicit
arXiv:2511.02230v4 Announce Type: replace-cross Abstract: KV cache management is essential for efficient LLM inference. To maximize utilization, existing inference engines evict finished requests' KV
arXiv:2605.03278v2 Announce Type: cross Abstract: Doubly Robust (DR) estimation of treatment effect relies on an untestable assumption that is the absence of unobserved confounding. This assumption is
arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide
arXiv:2605.03231v1 Announce Type: new Abstract: What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines re
arXiv:2605.04555v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of pred
arXiv:2605.04413v1 Announce Type: new Abstract: Structural causal models provide a unified semantics for interventions and counterfactuals, but most identifiability results rely on restrictive assumpt
arXiv:2605.04346v1 Announce Type: cross Abstract: The Forward-Forward algorithm eliminates global gradient flow and full network activations storage. However, in convolutional settings, existing BP-fr
arXiv:2605.05136v1 Announce Type: new Abstract: Domain Generalization (DG) aims to learn representations that remain robust under out-of-distribution (OOD) shifts and generalize effectively to unseen
arXiv:2605.04470v1 Announce Type: new Abstract: Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induce
arXiv:2605.04396v1 Announce Type: new Abstract: Recent work has shown that Transformers' compositional generalization is governed by complexity control, initialization scale and weight decay, which st
arXiv:2602.10043v2 Announce Type: replace Abstract: Head magnetic resonance imaging (MRI) data are routinely collected and shared for research under strict regulatory frameworks that require the remov
arXiv:2605.04905v1 Announce Type: new Abstract: Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology
arXiv:2512.14954v2 Announce Type: replace Abstract: Computing next-token likelihood ratios between two language models (LMs) is a standard task in training paradigms such as knowledge distillation. Si
arXiv:2605.03309v1 Announce Type: cross Abstract: Dependency confusion attacks exploit a structural gap in software distribution: once a package is installed, there is no cryptographic proof of which
arXiv:2605.05023v1 Announce Type: new Abstract: Efficient CUDA implementations of attention mechanisms are critical to modern deep learning systems, yet supporting diverse and evolving attention varia
arXiv:2605.05204v1 Announce Type: new Abstract: The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterpa
arXiv:2605.04518v1 Announce Type: new Abstract: Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This p
arXiv:2605.04943v1 Announce Type: new Abstract: The condition monitoring (CM) of synthetic fibre ropes (SFRs) used in offshore, maritime, and industrial settings demands more than a classifier: inspec
arXiv:2408.09929v2 Announce Type: replace-cross Abstract: Inspired by the idea of Positive-incentive Noise (Pi-Noise or pi-Noise) that aims at learning the reliable noise beneficial to tasks, we scien
arXiv:2605.04477v1 Announce Type: new Abstract: Online reinforcement learning from human feedback (RLHF) has emerged as a promising paradigm for aligning large language models (LLMs) by continuously c
arXiv:2410.23222v3 Announce Type: replace Abstract: Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale
arXiv:2605.03882v1 Announce Type: cross Abstract: Individuals frequently form deep attachments to physical objects (e.g., plush toys) that usually cannot sense or respond to their emotions. While AI c
arXiv:2605.02970v1 Announce Type: cross Abstract: Network traffic anomaly detection represents a critical cybersecurity task, yet widespread encryption makes this task increasingly challenging. In res
arXiv:2605.04447v1 Announce Type: new Abstract: Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation model