Knowledge catalogue
arXiv:2605.15333v1 Announce Type: new Abstract: Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence relies on world-know
arXiv:2605.15010v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading representation for real-time novel view synthesis and been widely adopted in various downstream ap
arXiv:2304.03641v4 Announce Type: replace-cross Abstract: Nonsmooth composite optimization with orthogonality constraints has a wide range of applications in statistical learning and data science. How
arXiv:2605.14929v1 Announce Type: new Abstract: Scaled Outer Product (SOP) is a post-training quantization methodology for large language model weights, designed to deliver near-lossless fidelity at 4
arXiv:2605.13858v1 Announce Type: cross Abstract: Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fund
arXiv:2605.14489v1 Announce Type: new Abstract: Building black-box models for dynamical systems from data is a challenging problem in machine learning, especially when asymptotic stability guarantees
arXiv:2605.13913v1 Announce Type: cross Abstract: Deep neural networks generalize well despite being heavily overparameterized, in apparent contradiction with classical learning theory based on unifor
arXiv:2510.19973v4 Announce Type: replace-cross Abstract: The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perce
arXiv:2605.14416v1 Announce Type: new Abstract: The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem with broad applications in logistics and transportation. Real-world CVRP
arXiv:2512.11855v2 Announce Type: replace-cross Abstract: Enforcing exact symmetry in machine learning models often yields significant gains in scientific applications, serving as a powerful inductive
arXiv:2509.26100v2 Announce Type: replace Abstract: The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, exis
arXiv:2510.15982v3 Announce Type: replace-cross Abstract: Autoregressive large language models (LLMs) have achieved remarkable improvement across many tasks but incur high computational and memory cos
arXiv:2605.14624v1 Announce Type: cross Abstract: A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational e
arXiv:2605.14331v1 Announce Type: cross Abstract: Modern edge devices increasingly rely on neural networks for intelligent applications. However, conventional digital computing-based edge inference re
arXiv:2601.03969v2 Announce Type: replace Abstract: Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their cha
arXiv:2605.14341v1 Announce Type: new Abstract: Existing diffusion models have made significant progress in generating realistic images. However, their direct adaptation to remote sensing imagery ofte
arXiv:2605.14255v1 Announce Type: cross Abstract: Industrial visual inspection systems increasingly rely on deep classifiers whose heatmap explanations may appear visually plausible while failing to i
arXiv:2605.14054v1 Announce Type: new Abstract: Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal v
arXiv:2603.28205v2 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) faces critical challenges due to representation entanglement and false-negative collisions in real-valued emb
arXiv:2605.14772v1 Announce Type: new Abstract: Understanding human motion beyond surface kinematics is crucial for motion analysis, rehabilitation, and injury risk assessment. However, progress in th
arXiv:2605.15012v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large Language Models (LLMs) with chain-of-thought roll
arXiv:2605.14709v1 Announce Type: new Abstract: Recent unified models integrate multimodal understanding and generation within a single framework. However, an 'understanding-generation gap' persists,
arXiv:2605.15141v1 Announce Type: new Abstract: Real-time interactive video generation requires low-latency, streaming, and controllable rollout. Existing autoregressive (AR) diffusion distillation me
arXiv:2605.15133v1 Announce Type: new Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variet
arXiv:2602.20571v2 Announce Type: replace Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effe
arXiv:2602.04473v2 Announce Type: replace Abstract: Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform
arXiv:2605.15122v1 Announce Type: cross Abstract: Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact-rich scenarios. Traditional app
arXiv:2605.14398v1 Announce Type: new Abstract: World models have emerged as a powerful paradigm for building interactive simulation environments, with recent video-based approaches demonstrating impr
arXiv:2605.14067v1 Announce Type: new Abstract: Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial da
arXiv:2605.14769v1 Announce Type: new Abstract: De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. E
arXiv:2605.15093v1 Announce Type: new Abstract: The life history of an individual coral is archived within the accreting skeleton of the colony. While reef-forming coral colonies (e.g. massive Porites
arXiv:2504.18544v3 Announce Type: replace-cross Abstract: Generating synthetic tabular health data is challenging, and evaluating their quality is equally, if not more, complex. This systematic review
arXiv:2509.01299v2 Announce Type: replace Abstract: Cross-domain few-shot segmentation (CD-FSS) aims to segment unseen categories with very limited samples while alleviating the negative effects of do
arXiv:2605.14171v1 Announce Type: new Abstract: Channel state information (CSI) provides a widely available sensing modality for human and environment perception, but existing CSI sensing models usual
arXiv:2605.14326v1 Announce Type: new Abstract: Remote sensing image generation provides a reliable data foundation for remote sensing large models and downstream tasks. However, existing controllable
arXiv:2605.14379v1 Announce Type: cross Abstract: Finding approximate equilibria for large-scale imperfect-information competitive games such as StarCraft, Dota, and CounterStrike remains computationa
arXiv:2605.14488v1 Announce Type: new Abstract: Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) techniques are revolutionizing applications across multiple domains, su
arXiv:2605.15116v1 Announce Type: new Abstract: Large-scale labelled driving video data is essential for training autonomous driving systems. Although simulation offers scalable and fully annotated da
arXiv:2502.00270v3 Announce Type: replace-cross Abstract: The performance of an LLM depends heavily on the relevance of its training data to the downstream evaluation task. However, in practice, the d
arXiv:2605.14014v1 Announce Type: cross Abstract: Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such a
arXiv:2605.14742v1 Announce Type: new Abstract: Understanding human--environment interactions from egocentric vision is essential for assistive robotics and embodied intelligent agents, yet existing m
arXiv:2605.13942v1 Announce Type: new Abstract: Machine learning (ML) is increasingly applied to optimize system performance in tasks such as resource management and network simulation. Unlike traditi
arXiv:2605.14521v1 Announce Type: new Abstract: Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference
arXiv:2605.14696v1 Announce Type: new Abstract: Data scaling plays a pivotal role in the pursuit of general intelligence. However, the prevailing perception-planning paradigm in autonomous driving rel
arXiv:2605.14855v1 Announce Type: cross Abstract: Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as N
arXiv:2605.14801v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation (VLN) has gained significant attention due to its minimal data collection costs and inherent generalization. Th
arXiv:2605.14305v1 Announce Type: new Abstract: Discrete diffusion language models improve generation efficiency through parallel token prediction, but standard X_0 prediction methods introduce factor
arXiv:2605.14665v1 Announce Type: new Abstract: Legal reasoning is not semantic similarity search. A court judgment encodes constrained symbolic reasoning: precedent propagation, procedural state tran
arXiv:2506.04499v2 Announce Type: replace Abstract: Existing LiDAR 3D object detection methods predominantely rely on sparse convolutions and/or transformers, which can be challenging to run on resour
arXiv:2605.13874v1 Announce Type: cross Abstract: Autonomous research agents can already run machine learning experiments without human supervision, but many rely on a narrow search strategy: they rep
arXiv:2605.14839v1 Announce Type: new Abstract: Traditional methods for classifying global navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data str
arXiv:2603.00772v2 Announce Type: replace-cross Abstract: Score-based generative models (SGMs) have achieved remarkable empirical success, motivating their application to a broad range of data distrib
arXiv:2605.14809v1 Announce Type: new Abstract: Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single
arXiv:2605.14317v1 Announce Type: new Abstract: Extreme precipitation causes severe societal and economic damage, and weather control has long been discussed as a potential mitigation strategy. Howeve
arXiv:2508.17588v2 Announce Type: replace Abstract: Generation-driven world models create immersive virtual environments but suffer slow inference due to the iterative nature of diffusion models. Whil
arXiv:2601.01972v4 Announce Type: cross Abstract: State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their advers
arXiv:2605.14309v1 Announce Type: cross Abstract: Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove tar
arXiv:2605.14774v1 Announce Type: new Abstract: In the world of AI and advanced technologies investigation aspects identification of a crime or criminal plays a major problem. In this research we focu
arXiv:2605.15102v1 Announce Type: cross Abstract: Large language model (LLM) based multi-turn dialogue systems often struggle to track dependencies across non-adjacent turns, undermining both consiste
arXiv:2605.14455v1 Announce Type: new Abstract: The Intelligence Impact Quotient (IIQ) is a composite metric intended to quantify the depth to which AI systems are integrated into organizational work