Knowledge catalogue
arXiv:2605.18704v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise that invalidate the station
arXiv:2605.16357v1 Announce Type: cross Abstract: WiFi fingerprint-based indoor localization has been widely studied, but most existing approaches focus on absolute positioning and rely on dense coord
arXiv:2605.17379v1 Announce Type: cross Abstract: Large language models pretrained on general-domain corpora often exhibit tokenization inefficiencies when applied to specialized domains. Although con
arXiv:2601.22297v2 Announce Type: replace Abstract: The reasoning abilities of large language models (LLMs) have been substantially improved by reinforcement learning with verifiable rewards (RLVR). A
arXiv:2605.17316v1 Announce Type: cross Abstract: Sensor networks increasingly govern modern infrastructure, yet the data they lose are rarely missing in the uniform-random patterns assumed by standar
arXiv:2605.16632v1 Announce Type: cross Abstract: Despite the effectiveness of Cube-and-Conquer (C&C) for solving challenging Boolean Satisfiability (SAT) problems, no prior work has shown that transf
arXiv:2605.17238v1 Announce Type: new Abstract: We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and i
arXiv:2510.04006v2 Announce Type: replace Abstract: Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physic
arXiv:2605.17058v1 Announce Type: new Abstract: The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Sto
arXiv:2605.18701v1 Announce Type: new Abstract: Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that
arXiv:2605.18681v1 Announce Type: new Abstract: Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to e
arXiv:2605.17980v1 Announce Type: new Abstract: Diffusion-based methods demonstrate significant potential for remote sensing image super-resolution at large scaling factors, particularly in reference-
arXiv:2605.16857v1 Announce Type: new Abstract: Experience-driven learning has emerged as a promising paradigm for enabling agents to improve from interaction trajectories by accumulating and reusing
arXiv:2505.19590v5 Announce Type: replace-cross Abstract: Training large language models (LLMs) for complex reasoning via Reinforcement Learning with Verifiable Rewards (RLVR) is effective but limited
arXiv:2605.18094v1 Announce Type: new Abstract: We study the Compositional Geometry Routing Problem (CGRP), a unified superclass of traditional routing problems that covers point-only, line-only, area
arXiv:2605.16755v1 Announce Type: cross Abstract: Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn prod
arXiv:2605.17003v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has emerged as the dominant paradigm for eliciting mathematical reasoning in Large Language Models (LLMs), y
arXiv:2605.16289v1 Announce Type: cross Abstract: Linguistic uncertainty is common in social media, but its relationship with engagement remains unclear across languages and topics. Using 2,258 Englis
arXiv:2509.01629v3 Announce Type: replace-cross Abstract: We study the design of interpolation schedules in flow and diffusion-based generative models from both statistical and numerical perspectives.
arXiv:2605.17287v1 Announce Type: new Abstract: Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems. Beyond being vulnerable to sudde
arXiv:2605.17260v1 Announce Type: new Abstract: The fundamental challenge in scaling Video Large Language Models (Video LLMs) to long-form video lies in managing the explosion of visual-token context
arXiv:2506.23978v3 Announce Type: replace-cross Abstract: While the Internet's core infrastructure was designed to be open and universal, today's application layer is dominated by closed, proprietary
arXiv:2605.17653v1 Announce Type: cross Abstract: Sub-billion-parameter Transformer language models are increasingly deployed on edge devices, where the privacy, latency, and operating-cost advantages
arXiv:2503.14800v3 Announce Type: replace-cross Abstract: Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augme
arXiv:2605.17303v1 Announce Type: new Abstract: Recovering a dynamic 3D scene from a long monocular video is crucial for dense geometry, camera motion, and temporal correspondence to remain consistent
arXiv:2605.18565v1 Announce Type: cross Abstract: Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accura
arXiv:2605.16343v1 Announce Type: cross Abstract: Looped language models (LoopLMs) improve parameter efficiency by recursively reusing Transformer blocks, enabling deeper computation under a fixed mod
arXiv:2512.01030v3 Announce Type: replace Abstract: Recovering pixel-wise geometric properties from a single image is fundamentally ill-posed due to appearance ambiguity and non-injective mappings bet
arXiv:2605.17329v1 Announce Type: cross Abstract: Guardrails are a critical safety layer for modern AI systems, but their operating regime is changing. As LLMs are deployed as customized assistants, s
arXiv:2508.06799v3 Announce Type: replace-cross Abstract: Digital Twins (DTs) offer powerful tools for managing complex infrastructure systems, but their effectiveness is often limited by challenges i
arXiv:2601.01685v2 Announce Type: replace-cross Abstract: As large language models (LLMs) transition to autonomous agents synthesizing real-time information, their reasoning capabilities introduce an
arXiv:2512.16085v2 Announce Type: replace-cross Abstract: Particulate composites underpin many solid-state chemical and electrochemical systems, where microstructural features such as multiphase bound
arXiv:2605.18253v1 Announce Type: cross Abstract: Recent masked diffusion language models (MDLMs), such as LLaDA and Dream, have achieved performance comparable to autoregressive large language models
arXiv:2605.17478v1 Announce Type: new Abstract: Visual Geometry Grounded Transformers (VGGT) have set new benchmarks in high-fidelity 3D scene reconstruction. However, as the sequence length increases
arXiv:2605.16290v1 Announce Type: cross Abstract: Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal s
arXiv:2605.16468v1 Announce Type: cross Abstract: A central goal in understanding human vision is to uncover the visual features that drive neuronal activity. A growing body of work has used artificia
arXiv:2605.18652v1 Announce Type: new Abstract: Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require m
arXiv:2605.17365v1 Announce Type: new Abstract: Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refi
arXiv:2605.17539v1 Announce Type: new Abstract: Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small
arXiv:2605.17444v1 Announce Type: cross Abstract: Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can op
arXiv:2510.10528v3 Announce Type: replace Abstract: Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this length
arXiv:2505.02621v2 Announce Type: replace Abstract: The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over R^d, and has gain
arXiv:2602.03664v3 Announce Type: replace Abstract: Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn a
arXiv:2506.12119v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token co
arXiv:2605.18449v1 Announce Type: cross Abstract: Understanding customer movement within retail spaces is essential for optimizing store layouts. Real-world trajectory data can provide highly accurate
arXiv:2605.18549v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not alwa
arXiv:2605.16922v1 Announce Type: new Abstract: LiDAR scene flow estimation is essential for autonomous driving, as it provides 3D motion for each point. Self-supervised approaches use static-dynamic
arXiv:2605.17593v1 Announce Type: new Abstract: Active 3D reconstruction of moving objects requires selecting informative viewpoints while accounting for object motion uncertainty during the decision-
arXiv:2605.16456v1 Announce Type: new Abstract: Understanding how objects relate to each other in space is fundamental to scene understanding, yet most contrastive pre-training approaches only model p
arXiv:2511.16361v3 Announce Type: replace Abstract: Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-qualit
arXiv:2605.17126v1 Announce Type: cross Abstract: We study the multi-task linear regression problem in the presence of contaminated tasks. We address the setting where the unknown parameters of a majo
arXiv:2605.17669v1 Announce Type: new Abstract: The preservation and interpretation of cultural heritage increasingly rely on digital technologies, among which Knowledge Graphs (KGs) stand out for the
arXiv:2605.17421v1 Announce Type: new Abstract: Accurate visual state estimation has been a central topic in robotics with a wide range of applications in robot navigation, autonomous driving, and aut
arXiv:2605.16384v1 Announce Type: cross Abstract: Accurate and effective discrete image tokenization is crucial for long image sequence processing. However, current methods rigidly compress all conten
arXiv:2605.17761v1 Announce Type: cross Abstract: Insider threats often reveal early anomalies through disruptions in behavioral statistics-such as altered recurrence patterns or short-versus long-ter
arXiv:2505.11143v2 Announce Type: replace-cross Abstract: Sparse linear regression is a fundamental tool in data analysis. However, traditional approaches often fall short when covariates exhibit stru
arXiv:2605.16447v1 Announce Type: cross Abstract: Spatiotemporal forecasting is critical for real-world applications like traffic management, yet capturing reliable interactions remains challenging un
arXiv:2605.17780v1 Announce Type: new Abstract: Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent 'black-box
arXiv:2605.16418v1 Announce Type: cross Abstract: EEG-based visual decoding aims to establish a mapping between neural signals and visual semantics. However, it remains constrained by the dual challen
arXiv:2605.16805v1 Announce Type: new Abstract: LiDARs are widely used for 3D depth reconstruction, but their performance is often limited by inherent hardware constraints that impose trade-offs betwe